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  1. .config/.last_opt_in_prompt.yaml +1 -0
  2. .config/.last_survey_prompt.yaml +1 -0
  3. .config/.last_update_check.json +1 -0
  4. .config/active_config +1 -0
  5. .config/config_sentinel +0 -0
  6. .config/configurations/config_default +6 -0
  7. .config/default_configs.db +0 -0
  8. .config/gce +1 -0
  9. .config/hidden_gcloud_config_universe_descriptor_data_cache_configs.db +0 -0
  10. .config/logs/2025.07.01/21.03.20.550425.log +765 -0
  11. .config/logs/2025.07.01/21.03.41.948871.log +5 -0
  12. .config/logs/2025.07.01/21.03.53.252709.log +153 -0
  13. .config/logs/2025.07.01/21.03.54.626322.log +5 -0
  14. .config/logs/2025.07.01/21.04.06.597104.log +8 -0
  15. .config/logs/2025.07.01/21.04.07.490711.log +8 -0
  16. .gitattributes +15 -0
  17. condacolab_install.log +318 -0
  18. lerobot/.dockerignore +160 -0
  19. lerobot/.gitattributes +21 -0
  20. lerobot/.github/ISSUE_TEMPLATE/bug-report.yml +68 -0
  21. lerobot/.github/PULL_REQUEST_TEMPLATE.md +34 -0
  22. lerobot/.github/workflows/build-docker-images.yml +135 -0
  23. lerobot/.github/workflows/build_documentation.yml +23 -0
  24. lerobot/.github/workflows/build_pr_documentation.yml +19 -0
  25. lerobot/.github/workflows/nightly-tests.yml +93 -0
  26. lerobot/.github/workflows/quality.yml +72 -0
  27. lerobot/.github/workflows/test-docker-build.yml +82 -0
  28. lerobot/.github/workflows/test.yml +150 -0
  29. lerobot/.github/workflows/trufflehog.yml +35 -0
  30. lerobot/.github/workflows/upload_pr_documentation.yml +16 -0
  31. lerobot/.gitignore +175 -0
  32. lerobot/.pre-commit-config.yaml +74 -0
  33. lerobot/CODE_OF_CONDUCT.md +133 -0
  34. lerobot/CONTRIBUTING.md +305 -0
  35. lerobot/LICENSE +507 -0
  36. lerobot/MANIFEST.in +2 -0
  37. lerobot/Makefile +180 -0
  38. lerobot/README.md +412 -0
  39. lerobot/benchmarks/video/README.md +271 -0
  40. lerobot/benchmarks/video/capture_camera_feed.py +102 -0
  41. lerobot/benchmarks/video/run_video_benchmark.py +490 -0
  42. lerobot/docker/lerobot-cpu/Dockerfile +29 -0
  43. lerobot/docker/lerobot-gpu-dev/Dockerfile +68 -0
  44. lerobot/docker/lerobot-gpu/Dockerfile +24 -0
  45. lerobot/docs/README.md +137 -0
  46. lerobot/docs/source/_toctree.yml +44 -0
  47. lerobot/docs/source/backwardcomp.mdx +82 -0
  48. lerobot/docs/source/cameras.mdx +173 -0
  49. lerobot/docs/source/contributing.md +305 -0
  50. lerobot/docs/source/hilserl.mdx +548 -0
.config/.last_opt_in_prompt.yaml ADDED
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+ {}
.config/.last_survey_prompt.yaml ADDED
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+ last_prompt_time: 1751403832.4579225
.config/.last_update_check.json ADDED
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+ {"last_update_check_time": 1751403834.040841, "last_update_check_revision": 20250627154417, "notifications": [], "last_nag_times": {}}
.config/active_config ADDED
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+ default
.config/config_sentinel ADDED
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.config/configurations/config_default ADDED
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+ [component_manager]
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+ disable_update_check = true
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+
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+ [compute]
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+ gce_metadata_read_timeout_sec = 0
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+
.config/default_configs.db ADDED
Binary file (12.3 kB). View file
 
.config/gce ADDED
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.config/hidden_gcloud_config_universe_descriptor_data_cache_configs.db ADDED
Binary file (12.3 kB). View file
 
.config/logs/2025.07.01/21.03.20.550425.log ADDED
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+ 2025-07-01 21:03:32,573 DEBUG root Loaded Command Group: ['gcloud', 'components']
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+ 2025-07-01 21:03:32,577 DEBUG root Loaded Command Group: ['gcloud', 'components', 'update']
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+ 2025-07-01 21:03:32,579 DEBUG root Running [gcloud.components.update] with arguments: [--compile-python: "True", --quiet: "True", COMPONENT-IDS:6: "['core', 'gcloud-deps', 'bq', 'gcloud', 'gcloud-crc32c', 'gsutil']"]
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+ 2025-07-01 21:03:32,580 INFO ___FILE_ONLY___ Beginning update. This process may take several minutes.
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+
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+ 2025-07-01 21:03:32,614 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
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+ 2025-07-01 21:03:32,626 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components-2.json HTTP/11" 200 239798
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+ 2025-07-01 21:03:32,638 INFO ___FILE_ONLY___
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+ Your current Google Cloud CLI version is: 529.0.0
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+
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+ 2025-07-01 21:03:32,639 INFO ___FILE_ONLY___ Installing components from version: 529.0.0
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+
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+
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+ 2025-07-01 21:03:32,639 DEBUG root Chosen display Format:table[box,title="These components will be removed."](details.display_name:label=Name:align=left,version.version_string:label=Version:align=right,data.size.size(zero="",min=1048576):label=Size:align=right)
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+ 2025-07-01 21:03:32,640 DEBUG root Chosen display Format:table[box,title="These components will be updated."](details.display_name:label=Name:align=left,version.version_string:label=Version:align=right,data.size.size(zero="",min=1048576):label=Size:align=right)
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+ 2025-07-01 21:03:32,640 DEBUG root Chosen display Format:table[box,title="These components will be installed."](details.display_name:label=Name:align=left,version.version_string:label=Version:align=right,data.size.size(zero="",min=1048576):label=Size:align=right)
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+ 2025-07-01 21:03:32,678 INFO ___FILE_ONLY___ ┌─────────────────────────────────────────────────────────────────────────────┐
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+ 2025-07-01 21:03:33,180 INFO ___FILE_ONLY___ For the latest full release notes, please visit:
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+ 2025-07-01 21:03:33,180 INFO ___FILE_ONLY___ Performing in place update...
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+ 2025-07-01 21:03:33,865 INFO ___FILE_ONLY___ ╠═ Downloading: Cloud Storage Command Line Tool (Platfor... ═╣
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+ 2025-07-01 21:03:33,883 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-gsutil-nix-20250523104322.tar.gz HTTP/11" 200 1950
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+ 2025-07-01 21:03:33,906 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-core-nix-20250523104322.tar.gz HTTP/11" 200 2325
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+ 2025-07-01 21:03:33,911 INFO ___FILE_ONLY___ ╠═ Downloading: Google Cloud CRC32C Hash Tool (Platform ... ═╣
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+ 2025-07-01 21:03:33,968 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-gcloud-crc32c-linux-x86_64-20250613150750.tar.gz HTTP/11" 200 1525557
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+ 2025-07-01 21:03:33,990 INFO ___FILE_ONLY___ ╝
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+ 2025-07-01 21:03:33,993 INFO ___FILE_ONLY___ ╠═ Downloading: gcloud cli dependencies (Platform Specific) ═╣
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+ 2025-07-01 21:03:33,993 INFO ___FILE_ONLY___ ╚
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+ 2025-07-01 21:03:33,996 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
481
+ 2025-07-01 21:03:34,012 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-gcloud-deps-linux-x86_64-20210416153011.tar.gz HTTP/11" 200 104
482
+ 2025-07-01 21:03:34,013 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
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+ 2025-07-01 21:03:34,015 INFO ___FILE_ONLY___ ╠═ Installing: BigQuery Command Line Tool ═╣
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490
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491
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508
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509
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510
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511
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514
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515
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517
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518
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519
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520
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521
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522
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523
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524
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525
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526
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527
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528
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529
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530
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531
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532
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533
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534
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535
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536
+ 2025-07-01 21:03:34,236 INFO ___FILE_ONLY___ ═
537
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538
+ 2025-07-01 21:03:34,240 INFO ___FILE_ONLY___ ═
539
+ 2025-07-01 21:03:34,242 INFO ___FILE_ONLY___ ═
540
+ 2025-07-01 21:03:34,245 INFO ___FILE_ONLY___ ═
541
+ 2025-07-01 21:03:34,247 INFO ___FILE_ONLY___ ═
542
+ 2025-07-01 21:03:34,250 INFO ___FILE_ONLY___ ═
543
+ 2025-07-01 21:03:34,252 INFO ___FILE_ONLY___ ═
544
+ 2025-07-01 21:03:34,254 INFO ___FILE_ONLY___ ═
545
+ 2025-07-01 21:03:34,256 INFO ___FILE_ONLY___ ═
546
+ 2025-07-01 21:03:34,259 INFO ___FILE_ONLY___ ═
547
+ 2025-07-01 21:03:34,261 INFO ___FILE_ONLY___ ═
548
+ 2025-07-01 21:03:34,263 INFO ___FILE_ONLY___ ═
549
+ 2025-07-01 21:03:34,265 INFO ___FILE_ONLY___ ═
550
+ 2025-07-01 21:03:34,265 INFO ___FILE_ONLY___ ╝
551
+
552
+ 2025-07-01 21:03:34,277 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
553
+
554
+ 2025-07-01 21:03:34,277 INFO ___FILE_ONLY___ ╠═ Installing: BigQuery Command Line Tool (Platform Spec... ═╣
555
+
556
+ 2025-07-01 21:03:34,277 INFO ___FILE_ONLY___ ╚
557
+ 2025-07-01 21:03:34,278 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
558
+ 2025-07-01 21:03:34,278 INFO ___FILE_ONLY___ ╝
559
+
560
+ 2025-07-01 21:03:34,286 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
561
+
562
+ 2025-07-01 21:03:34,286 INFO ___FILE_ONLY___ ╠═ Installing: Bundled Python 3.12 ═╣
563
+
564
+ 2025-07-01 21:03:34,286 INFO ___FILE_ONLY___ ╚
565
+ 2025-07-01 21:03:34,289 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
566
+ 2025-07-01 21:03:34,289 INFO ___FILE_ONLY___ ╝
567
+
568
+ 2025-07-01 21:03:34,291 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
569
+
570
+ 2025-07-01 21:03:34,291 INFO ___FILE_ONLY___ ╠═ Installing: Bundled Python 3.12 (Platform Specific) ═╣
571
+
572
+ 2025-07-01 21:03:34,291 INFO ___FILE_ONLY___ ╚
573
+ 2025-07-01 21:03:36,564 INFO ___FILE_ONLY___ ═
574
+ 2025-07-01 21:03:36,577 INFO ___FILE_ONLY___ ═
575
+ 2025-07-01 21:03:36,590 INFO ___FILE_ONLY___ ═
576
+ 2025-07-01 21:03:36,603 INFO ___FILE_ONLY___ ═
577
+ 2025-07-01 21:03:36,616 INFO ___FILE_ONLY___ ═
578
+ 2025-07-01 21:03:36,631 INFO ___FILE_ONLY___ ═
579
+ 2025-07-01 21:03:36,645 INFO ___FILE_ONLY___ ═
580
+ 2025-07-01 21:03:36,658 INFO ___FILE_ONLY___ ═
581
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582
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583
+ 2025-07-01 21:03:36,703 INFO ___FILE_ONLY___ ═
584
+ 2025-07-01 21:03:36,718 INFO ___FILE_ONLY___ ═
585
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586
+ 2025-07-01 21:03:36,744 INFO ___FILE_ONLY___ ═
587
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588
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589
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590
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591
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592
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593
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594
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595
+ 2025-07-01 21:03:36,866 INFO ___FILE_ONLY___ ═
596
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597
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598
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599
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600
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601
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602
+ 2025-07-01 21:03:36,959 INFO ___FILE_ONLY___ ═
603
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604
+ 2025-07-01 21:03:36,985 INFO ___FILE_ONLY___ ═
605
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606
+ 2025-07-01 21:03:37,012 INFO ___FILE_ONLY___ ═
607
+ 2025-07-01 21:03:37,030 INFO ___FILE_ONLY___ ═
608
+ 2025-07-01 21:03:37,046 INFO ___FILE_ONLY___ ═
609
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610
+ 2025-07-01 21:03:38,040 INFO ___FILE_ONLY___ ═
611
+ 2025-07-01 21:03:38,564 INFO ___FILE_ONLY___ ═
612
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613
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614
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615
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616
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617
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618
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619
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620
+ 2025-07-01 21:03:38,766 INFO ___FILE_ONLY___ ═
621
+ 2025-07-01 21:03:38,861 INFO ___FILE_ONLY___ ═
622
+ 2025-07-01 21:03:38,880 INFO ___FILE_ONLY___ ═
623
+ 2025-07-01 21:03:39,028 INFO ___FILE_ONLY___ ═
624
+ 2025-07-01 21:03:39,046 INFO ___FILE_ONLY___ ═
625
+ 2025-07-01 21:03:39,063 INFO ___FILE_ONLY___ ═
626
+ 2025-07-01 21:03:39,081 INFO ___FILE_ONLY___ ═
627
+ 2025-07-01 21:03:39,097 INFO ___FILE_ONLY___ ═
628
+ 2025-07-01 21:03:39,116 INFO ___FILE_ONLY___ ═
629
+ 2025-07-01 21:03:39,134 INFO ___FILE_ONLY___ ═
630
+ 2025-07-01 21:03:39,153 INFO ___FILE_ONLY___ ═
631
+ 2025-07-01 21:03:39,183 INFO ___FILE_ONLY___ ═
632
+ 2025-07-01 21:03:39,742 INFO ___FILE_ONLY___ ═
633
+ 2025-07-01 21:03:39,743 INFO ___FILE_ONLY___ ╝
634
+
635
+ 2025-07-01 21:03:39,801 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
636
+
637
+ 2025-07-01 21:03:39,801 INFO ___FILE_ONLY___ ╠═ Installing: Cloud Storage Command Line Tool ═╣
638
+
639
+ 2025-07-01 21:03:39,801 INFO ___FILE_ONLY___ ╚
640
+ 2025-07-01 21:03:40,363 INFO ___FILE_ONLY___ ═
641
+ 2025-07-01 21:03:40,376 INFO ___FILE_ONLY___ ═
642
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643
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644
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645
+ 2025-07-01 21:03:40,446 INFO ___FILE_ONLY___ ═
646
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647
+ 2025-07-01 21:03:40,487 INFO ___FILE_ONLY___ ═
648
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649
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650
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651
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652
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653
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654
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655
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656
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657
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658
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659
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660
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661
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662
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663
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664
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665
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666
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667
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668
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669
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670
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671
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672
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673
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674
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675
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676
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677
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678
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679
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680
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681
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682
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683
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684
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685
+ 2025-07-01 21:03:41,058 INFO ___FILE_ONLY___ ═
686
+ 2025-07-01 21:03:41,069 INFO ___FILE_ONLY___ ═
687
+ 2025-07-01 21:03:41,080 INFO ___FILE_ONLY___ ═
688
+ 2025-07-01 21:03:41,091 INFO ___FILE_ONLY___ ═
689
+ 2025-07-01 21:03:41,105 INFO ___FILE_ONLY___ ═
690
+ 2025-07-01 21:03:41,121 INFO ___FILE_ONLY___ ═
691
+ 2025-07-01 21:03:41,135 INFO ___FILE_ONLY___ ═
692
+ 2025-07-01 21:03:41,150 INFO ___FILE_ONLY___ ═
693
+ 2025-07-01 21:03:41,166 INFO ___FILE_ONLY___ ═
694
+ 2025-07-01 21:03:41,180 INFO ___FILE_ONLY___ ═
695
+ 2025-07-01 21:03:41,197 INFO ___FILE_ONLY___ ═
696
+ 2025-07-01 21:03:41,215 INFO ___FILE_ONLY___ ═
697
+ 2025-07-01 21:03:41,238 INFO ___FILE_ONLY___ ═
698
+ 2025-07-01 21:03:41,256 INFO ___FILE_ONLY___ ═
699
+ 2025-07-01 21:03:41,277 INFO ___FILE_ONLY___ ═
700
+ 2025-07-01 21:03:41,277 INFO ___FILE_ONLY___ ╝
701
+
702
+ 2025-07-01 21:03:41,313 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
703
+
704
+ 2025-07-01 21:03:41,313 INFO ___FILE_ONLY___ ╠═ Installing: Cloud Storage Command Line Tool (Platform... ═╣
705
+
706
+ 2025-07-01 21:03:41,313 INFO ___FILE_ONLY___ ╚
707
+ 2025-07-01 21:03:41,314 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
708
+ 2025-07-01 21:03:41,314 INFO ___FILE_ONLY___ ╝
709
+
710
+ 2025-07-01 21:03:41,320 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
711
+
712
+ 2025-07-01 21:03:41,320 INFO ___FILE_ONLY___ ╠═ Installing: Default set of gcloud commands ═╣
713
+
714
+ 2025-07-01 21:03:41,320 INFO ___FILE_ONLY___ ╚
715
+ 2025-07-01 21:03:41,324 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
716
+ 2025-07-01 21:03:41,324 INFO ___FILE_ONLY___ ╝
717
+
718
+ 2025-07-01 21:03:41,326 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
719
+
720
+ 2025-07-01 21:03:41,326 INFO ___FILE_ONLY___ ╠═ Installing: Google Cloud CLI Core Libraries (Platform... ═╣
721
+
722
+ 2025-07-01 21:03:41,326 INFO ___FILE_ONLY___ ╚
723
+ 2025-07-01 21:03:41,328 INFO ___FILE_ONLY___ ══════════════════════════════
724
+ 2025-07-01 21:03:41,328 INFO ___FILE_ONLY___ ══════════════════════════════
725
+ 2025-07-01 21:03:41,328 INFO ___FILE_ONLY___ ╝
726
+
727
+ 2025-07-01 21:03:41,333 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
728
+
729
+ 2025-07-01 21:03:41,333 INFO ___FILE_ONLY___ ╠═ Installing: Google Cloud CRC32C Hash Tool ═╣
730
+
731
+ 2025-07-01 21:03:41,333 INFO ___FILE_ONLY___ ╚
732
+ 2025-07-01 21:03:41,337 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
733
+ 2025-07-01 21:03:41,337 INFO ___FILE_ONLY___ ╝
734
+
735
+ 2025-07-01 21:03:41,339 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
736
+
737
+ 2025-07-01 21:03:41,339 INFO ___FILE_ONLY___ ╠═ Installing: Google Cloud CRC32C Hash Tool (Platform S... ═╣
738
+
739
+ 2025-07-01 21:03:41,339 INFO ___FILE_ONLY___ ╚
740
+ 2025-07-01 21:03:41,381 INFO ___FILE_ONLY___ ══════════════════════════════
741
+ 2025-07-01 21:03:41,381 INFO ___FILE_ONLY___ ══════════════════════════════
742
+ 2025-07-01 21:03:41,381 INFO ___FILE_ONLY___ ╝
743
+
744
+ 2025-07-01 21:03:41,387 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
745
+
746
+ 2025-07-01 21:03:41,387 INFO ___FILE_ONLY___ ╠═ Installing: gcloud cli dependencies (Platform Specific) ═╣
747
+
748
+ 2025-07-01 21:03:41,387 INFO ___FILE_ONLY___ ╚
749
+ 2025-07-01 21:03:41,387 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
750
+ 2025-07-01 21:03:41,388 INFO ___FILE_ONLY___ ╝
751
+
752
+ 2025-07-01 21:03:41,393 DEBUG root Updating notification cache...
753
+ 2025-07-01 21:03:41,393 INFO ___FILE_ONLY___
754
+
755
+ 2025-07-01 21:03:41,395 INFO ___FILE_ONLY___ Performing post processing steps...
756
+ 2025-07-01 21:03:41,396 DEBUG root Executing command: ['/tools/google-cloud-sdk/bin/gcloud', 'components', 'post-process']
757
+ 2025-07-01 21:03:52,373 DEBUG ___FILE_ONLY___
758
+ 2025-07-01 21:03:52,373 DEBUG ___FILE_ONLY___
759
+ 2025-07-01 21:03:52,453 INFO root descriptor_list: [{'universeDomain': 'googleapis.com', 'universeShortName': '', 'authenticationDomain': 'auth.cloud.google.com', 'projectPrefix': '', 'cloudWebDomain': 'cloud.google.com', 'documentationDomain': 'cloud.google.com', 'version': '1.0.0', 'state': 'primary', 'artifactRegistryDomain': 'pkg.dev'}]
760
+ 2025-07-01 21:03:52,454 INFO ___FILE_ONLY___
761
+ Update done!
762
+
763
+
764
+ 2025-07-01 21:03:52,456 DEBUG root Chosen display Format:none
765
+ 2025-07-01 21:03:52,457 INFO root Display format: "none"
.config/logs/2025.07.01/21.03.41.948871.log ADDED
@@ -0,0 +1,5 @@
 
 
 
 
 
 
1
+ 2025-07-01 21:03:41,949 DEBUG root Loaded Command Group: ['gcloud', 'components']
2
+ 2025-07-01 21:03:41,951 DEBUG root Loaded Command Group: ['gcloud', 'components', 'post_process']
3
+ 2025-07-01 21:03:41,953 DEBUG root Running [gcloud.components.post-process] with arguments: []
4
+ 2025-07-01 21:03:52,197 DEBUG root Chosen display Format:none
5
+ 2025-07-01 21:03:52,198 INFO root Display format: "none"
.config/logs/2025.07.01/21.03.53.252709.log ADDED
@@ -0,0 +1,153 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ 2025-07-01 21:03:53,253 DEBUG root Loaded Command Group: ['gcloud', 'components']
2
+ 2025-07-01 21:03:53,255 DEBUG root Loaded Command Group: ['gcloud', 'components', 'update']
3
+ 2025-07-01 21:03:53,257 DEBUG root Running [gcloud.components.update] with arguments: [--quiet: "True", COMPONENT-IDS:8: "['gcloud', 'core', 'bq', 'gsutil', 'compute', 'preview', 'alpha', 'beta']"]
4
+ 2025-07-01 21:03:53,258 INFO ___FILE_ONLY___ Beginning update. This process may take several minutes.
5
+
6
+ 2025-07-01 21:03:53,268 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
7
+ 2025-07-01 21:03:53,281 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components-2.json HTTP/11" 200 239798
8
+ 2025-07-01 21:03:53,295 WARNING root Component [compute] no longer exists.
9
+ 2025-07-01 21:03:53,296 INFO ___FILE_ONLY___
10
+
11
+ 2025-07-01 21:03:53,296 INFO ___FILE_ONLY___
12
+ Your current Google Cloud CLI version is: 529.0.0
13
+
14
+ 2025-07-01 21:03:53,296 INFO ___FILE_ONLY___ Installing components from version: 529.0.0
15
+
16
+ 2025-07-01 21:03:53,296 INFO ___FILE_ONLY___
17
+
18
+ 2025-07-01 21:03:53,296 DEBUG root Chosen display Format:table[box,title="These components will be removed."](details.display_name:label=Name:align=left,version.version_string:label=Version:align=right,data.size.size(zero="",min=1048576):label=Size:align=right)
19
+ 2025-07-01 21:03:53,297 DEBUG root Chosen display Format:table[box,title="These components will be updated."](details.display_name:label=Name:align=left,version.version_string:label=Version:align=right,data.size.size(zero="",min=1048576):label=Size:align=right)
20
+ 2025-07-01 21:03:53,297 DEBUG root Chosen display Format:table[box,title="These components will be installed."](details.display_name:label=Name:align=left,version.version_string:label=Version:align=right,data.size.size(zero="",min=1048576):label=Size:align=right)
21
+ 2025-07-01 21:03:53,312 INFO ___FILE_ONLY___ ┌────────────────────────────────────────────────┐
22
+ 2025-07-01 21:03:53,312 INFO ___FILE_ONLY___
23
+
24
+ 2025-07-01 21:03:53,312 INFO ___FILE_ONLY___ │ These components will be installed. │
25
+ 2025-07-01 21:03:53,312 INFO ___FILE_ONLY___
26
+
27
+ 2025-07-01 21:03:53,312 INFO ___FILE_ONLY___ ├─────────────────────────┬────────────┬─────────┤
28
+ 2025-07-01 21:03:53,312 INFO ___FILE_ONLY___
29
+
30
+ 2025-07-01 21:03:53,312 INFO ___FILE_ONLY___ │ Name │ Version │ Size │
31
+ 2025-07-01 21:03:53,313 INFO ___FILE_ONLY___
32
+
33
+ 2025-07-01 21:03:53,313 INFO ___FILE_ONLY___ ├─────────────────────────┼────────────┼─────────┤
34
+ 2025-07-01 21:03:53,313 INFO ___FILE_ONLY___
35
+
36
+ 2025-07-01 21:03:53,313 INFO ___FILE_ONLY___ │
37
+ 2025-07-01 21:03:53,313 INFO ___FILE_ONLY___ gcloud Alpha Commands
38
+ 2025-07-01 21:03:53,313 INFO ___FILE_ONLY___
39
+ 2025-07-01 21:03:53,313 INFO ___FILE_ONLY___ │
40
+ 2025-07-01 21:03:53,313 INFO ___FILE_ONLY___ 2025.06.27
41
+ 2025-07-01 21:03:53,313 INFO ___FILE_ONLY___
42
+ 2025-07-01 21:03:53,313 INFO ___FILE_ONLY___ │
43
+ 2025-07-01 21:03:53,313 INFO ___FILE_ONLY___ < 1 MiB
44
+ 2025-07-01 21:03:53,313 INFO ___FILE_ONLY___
45
+ 2025-07-01 21:03:53,313 INFO ___FILE_ONLY___ │
46
+ 2025-07-01 21:03:53,313 INFO ___FILE_ONLY___
47
+
48
+ 2025-07-01 21:03:53,313 INFO ___FILE_ONLY___ │
49
+ 2025-07-01 21:03:53,313 INFO ___FILE_ONLY___ gcloud Beta Commands
50
+ 2025-07-01 21:03:53,313 INFO ___FILE_ONLY___
51
+ 2025-07-01 21:03:53,313 INFO ___FILE_ONLY___ │
52
+ 2025-07-01 21:03:53,313 INFO ___FILE_ONLY___ 2025.06.27
53
+ 2025-07-01 21:03:53,313 INFO ___FILE_ONLY___
54
+ 2025-07-01 21:03:53,313 INFO ___FILE_ONLY___ │
55
+ 2025-07-01 21:03:53,314 INFO ___FILE_ONLY___ < 1 MiB
56
+ 2025-07-01 21:03:53,314 INFO ___FILE_ONLY___
57
+ 2025-07-01 21:03:53,314 INFO ___FILE_ONLY___ │
58
+ 2025-07-01 21:03:53,314 INFO ___FILE_ONLY___
59
+
60
+ 2025-07-01 21:03:53,314 INFO ___FILE_ONLY___ │
61
+ 2025-07-01 21:03:53,314 INFO ___FILE_ONLY___ gcloud Preview Commands
62
+ 2025-07-01 21:03:53,314 INFO ___FILE_ONLY___
63
+ 2025-07-01 21:03:53,314 INFO ___FILE_ONLY___ │
64
+ 2025-07-01 21:03:53,314 INFO ___FILE_ONLY___
65
+ 2025-07-01 21:03:53,314 INFO ___FILE_ONLY___
66
+ 2025-07-01 21:03:53,314 INFO ___FILE_ONLY___ │
67
+ 2025-07-01 21:03:53,314 INFO ___FILE_ONLY___ < 1 MiB
68
+ 2025-07-01 21:03:53,314 INFO ___FILE_ONLY___
69
+ 2025-07-01 21:03:53,314 INFO ___FILE_ONLY___ │
70
+ 2025-07-01 21:03:53,314 INFO ___FILE_ONLY___
71
+
72
+ 2025-07-01 21:03:53,314 INFO ___FILE_ONLY___ └─────────────────────────┴────────────┴─────────┘
73
+ 2025-07-01 21:03:53,314 INFO ___FILE_ONLY___
74
+
75
+ 2025-07-01 21:03:53,314 INFO ___FILE_ONLY___
76
+
77
+ 2025-07-01 21:03:53,318 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
78
+ 2025-07-01 21:03:53,332 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/RELEASE_NOTES HTTP/11" 200 1444035
79
+ 2025-07-01 21:03:53,802 INFO ___FILE_ONLY___ For the latest full release notes, please visit:
80
+ https://cloud.google.com/sdk/release_notes
81
+
82
+
83
+ 2025-07-01 21:03:53,803 INFO ___FILE_ONLY___ Performing in place update...
84
+
85
+
86
+ 2025-07-01 21:03:53,805 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
87
+
88
+ 2025-07-01 21:03:53,805 INFO ___FILE_ONLY___ ╠═ Downloading: gcloud Alpha Commands ═╣
89
+
90
+ 2025-07-01 21:03:53,806 INFO ___FILE_ONLY___ ╚
91
+ 2025-07-01 21:03:53,809 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
92
+ 2025-07-01 21:03:53,916 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-alpha-20250627154417.tar.gz HTTP/11" 200 800
93
+ 2025-07-01 21:03:53,917 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
94
+ 2025-07-01 21:03:53,917 INFO ___FILE_ONLY___ ╝
95
+
96
+ 2025-07-01 21:03:53,919 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
97
+
98
+ 2025-07-01 21:03:53,919 INFO ___FILE_ONLY___ ╠═ Downloading: gcloud Beta Commands ═╣
99
+
100
+ 2025-07-01 21:03:53,919 INFO ___FILE_ONLY___ ╚
101
+ 2025-07-01 21:03:53,923 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
102
+ 2025-07-01 21:03:53,974 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-beta-20250627154417.tar.gz HTTP/11" 200 797
103
+ 2025-07-01 21:03:53,975 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
104
+ 2025-07-01 21:03:53,975 INFO ___FILE_ONLY___ ╝
105
+
106
+ 2025-07-01 21:03:53,977 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
107
+
108
+ 2025-07-01 21:03:53,977 INFO ___FILE_ONLY___ ╠═ Downloading: gcloud Preview Commands ═╣
109
+
110
+ 2025-07-01 21:03:53,977 INFO ___FILE_ONLY___ ╚
111
+ 2025-07-01 21:03:53,981 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
112
+ 2025-07-01 21:03:54,014 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-preview-20241115154308.tar.gz HTTP/11" 200 823
113
+ 2025-07-01 21:03:54,015 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
114
+ 2025-07-01 21:03:54,015 INFO ___FILE_ONLY___ ╝
115
+
116
+ 2025-07-01 21:03:54,018 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
117
+
118
+ 2025-07-01 21:03:54,018 INFO ___FILE_ONLY___ ╠═ Installing: gcloud Alpha Commands ═╣
119
+
120
+ 2025-07-01 21:03:54,018 INFO ___FILE_ONLY___ ╚
121
+ 2025-07-01 21:03:54,019 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
122
+ 2025-07-01 21:03:54,019 INFO ___FILE_ONLY___ ╝
123
+
124
+ 2025-07-01 21:03:54,026 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
125
+
126
+ 2025-07-01 21:03:54,026 INFO ___FILE_ONLY___ ╠═ Installing: gcloud Beta Commands ═╣
127
+
128
+ 2025-07-01 21:03:54,026 INFO ___FILE_ONLY___ ╚
129
+ 2025-07-01 21:03:54,027 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
130
+ 2025-07-01 21:03:54,027 INFO ___FILE_ONLY___ ╝
131
+
132
+ 2025-07-01 21:03:54,033 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
133
+
134
+ 2025-07-01 21:03:54,034 INFO ___FILE_ONLY___ ╠═ Installing: gcloud Preview Commands ═╣
135
+
136
+ 2025-07-01 21:03:54,034 INFO ___FILE_ONLY___ ╚
137
+ 2025-07-01 21:03:54,034 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
138
+ 2025-07-01 21:03:54,035 INFO ___FILE_ONLY___ ╝
139
+
140
+ 2025-07-01 21:03:54,040 DEBUG root Updating notification cache...
141
+ 2025-07-01 21:03:54,041 INFO ___FILE_ONLY___
142
+
143
+ 2025-07-01 21:03:54,043 INFO ___FILE_ONLY___ Performing post processing steps...
144
+ 2025-07-01 21:03:54,043 DEBUG root Executing command: ['/tools/google-cloud-sdk/bin/gcloud', 'components', 'post-process']
145
+ 2025-07-01 21:04:05,574 DEBUG ___FILE_ONLY___
146
+ 2025-07-01 21:04:05,574 DEBUG ___FILE_ONLY___
147
+ 2025-07-01 21:04:05,812 INFO root descriptor_list: [{'universeDomain': 'googleapis.com', 'universeShortName': '', 'authenticationDomain': 'auth.cloud.google.com', 'projectPrefix': '', 'cloudWebDomain': 'cloud.google.com', 'documentationDomain': 'cloud.google.com', 'version': '1.0.0', 'state': 'primary', 'artifactRegistryDomain': 'pkg.dev'}]
148
+ 2025-07-01 21:04:05,813 INFO ___FILE_ONLY___
149
+ Update done!
150
+
151
+
152
+ 2025-07-01 21:04:05,815 DEBUG root Chosen display Format:none
153
+ 2025-07-01 21:04:05,815 INFO root Display format: "none"
.config/logs/2025.07.01/21.03.54.626322.log ADDED
@@ -0,0 +1,5 @@
 
 
 
 
 
 
1
+ 2025-07-01 21:03:54,627 DEBUG root Loaded Command Group: ['gcloud', 'components']
2
+ 2025-07-01 21:03:54,628 DEBUG root Loaded Command Group: ['gcloud', 'components', 'post_process']
3
+ 2025-07-01 21:03:54,630 DEBUG root Running [gcloud.components.post-process] with arguments: []
4
+ 2025-07-01 21:04:05,377 DEBUG root Chosen display Format:none
5
+ 2025-07-01 21:04:05,378 INFO root Display format: "none"
.config/logs/2025.07.01/21.04.06.597104.log ADDED
@@ -0,0 +1,8 @@
 
 
 
 
 
 
 
 
 
1
+ 2025-07-01 21:04:06,599 DEBUG root Loaded Command Group: ['gcloud', 'config']
2
+ 2025-07-01 21:04:06,658 DEBUG root Loaded Command Group: ['gcloud', 'config', 'set']
3
+ 2025-07-01 21:04:06,661 DEBUG root Running [gcloud.config.set] with arguments: [SECTION/PROPERTY: "component_manager/disable_update_check", VALUE: "true"]
4
+ 2025-07-01 21:04:06,662 INFO ___FILE_ONLY___ Updated property [component_manager/disable_update_check].
5
+
6
+ 2025-07-01 21:04:06,663 DEBUG root Chosen display Format:default
7
+ 2025-07-01 21:04:06,663 INFO root Display format: "default"
8
+ 2025-07-01 21:04:06,664 DEBUG root SDK update checks are disabled.
.config/logs/2025.07.01/21.04.07.490711.log ADDED
@@ -0,0 +1,8 @@
 
 
 
 
 
 
 
 
 
1
+ 2025-07-01 21:04:07,492 DEBUG root Loaded Command Group: ['gcloud', 'config']
2
+ 2025-07-01 21:04:07,547 DEBUG root Loaded Command Group: ['gcloud', 'config', 'set']
3
+ 2025-07-01 21:04:07,550 DEBUG root Running [gcloud.config.set] with arguments: [SECTION/PROPERTY: "compute/gce_metadata_read_timeout_sec", VALUE: "0"]
4
+ 2025-07-01 21:04:07,551 INFO ___FILE_ONLY___ Updated property [compute/gce_metadata_read_timeout_sec].
5
+
6
+ 2025-07-01 21:04:07,552 DEBUG root Chosen display Format:default
7
+ 2025-07-01 21:04:07,553 INFO root Display format: "default"
8
+ 2025-07-01 21:04:07,554 DEBUG root SDK update checks are disabled.
.gitattributes CHANGED
@@ -33,3 +33,18 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
33
  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
33
  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
35
  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ lerobot/media/gym/aloha_act.gif filter=lfs diff=lfs merge=lfs -text
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38
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+ lerobot/media/wandb.png filter=lfs diff=lfs merge=lfs -text
46
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48
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49
+ sample_data/mnist_test.csv filter=lfs diff=lfs merge=lfs -text
50
+ sample_data/mnist_train_small.csv filter=lfs diff=lfs merge=lfs -text
condacolab_install.log ADDED
@@ -0,0 +1,318 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ PREFIX=/usr/local
2
+ Unpacking payload ...
3
+ Extracting _libgcc_mutex-0.1-conda_forge.tar.bz2
4
+ Extracting ca-certificates-2024.12.14-hbcca054_0.conda
5
+ Extracting ld_impl_linux-64-2.43-h712a8e2_2.conda
6
+ Extracting pybind11-abi-4-hd8ed1ab_3.tar.bz2
7
+ Extracting python_abi-3.11-5_cp311.conda
8
+ Extracting tzdata-2024b-hc8b5060_0.conda
9
+ Extracting libgomp-14.2.0-h77fa898_1.conda
10
+ Extracting _openmp_mutex-4.5-2_gnu.tar.bz2
11
+ Extracting libgcc-14.2.0-h77fa898_1.conda
12
+ Extracting c-ares-1.34.4-hb9d3cd8_0.conda
13
+ Extracting libexpat-2.6.4-h5888daf_0.conda
14
+ Extracting libgcc-ng-14.2.0-h69a702a_1.conda
15
+ Extracting liblzma-5.6.3-hb9d3cd8_1.conda
16
+ Extracting libstdcxx-14.2.0-hc0a3c3a_1.conda
17
+ Extracting libzlib-1.3.1-hb9d3cd8_2.conda
18
+ Extracting ncurses-6.5-h2d0b736_2.conda
19
+ Extracting openssl-3.4.0-h7b32b05_1.conda
20
+ Extracting reproc-14.2.5.post0-hb9d3cd8_0.conda
21
+ Extracting bzip2-1.0.8-h4bc722e_7.conda
22
+ Extracting keyutils-1.6.1-h166bdaf_0.tar.bz2
23
+ Extracting libedit-3.1.20240808-pl5321h7949ede_0.conda
24
+ Extracting libev-4.33-hd590300_2.conda
25
+ Extracting libffi-3.4.2-h7f98852_5.tar.bz2
26
+ Extracting libiconv-1.17-hd590300_2.conda
27
+ Extracting libnsl-2.0.1-hd590300_0.conda
28
+ Extracting libsqlite-3.47.2-hee588c1_0.conda
29
+ Extracting libssh2-1.11.1-hf672d98_0.conda
30
+ Extracting libstdcxx-ng-14.2.0-h4852527_1.conda
31
+ Extracting libuuid-2.38.1-h0b41bf4_0.conda
32
+ Extracting libxcrypt-4.4.36-hd590300_1.conda
33
+ Extracting lz4-c-1.10.0-h5888daf_1.conda
34
+ Extracting lzo-2.10-hd590300_1001.conda
35
+ Extracting readline-8.2-h8228510_1.conda
36
+ Extracting reproc-cpp-14.2.5.post0-h5888daf_0.conda
37
+ Extracting tk-8.6.13-noxft_h4845f30_101.conda
38
+ Extracting fmt-11.0.2-h434a139_0.conda
39
+ Extracting krb5-1.21.3-h659f571_0.conda
40
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41
+ Extracting libsolv-0.7.30-h3509ff9_0.conda
42
+ Extracting libxml2-2.13.5-h0d44e9d_1.conda
43
+ Extracting python-3.11.11-h9e4cc4f_1_cpython.conda
44
+ Extracting yaml-cpp-0.8.0-h59595ed_0.conda
45
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46
+ Extracting libarchive-3.7.7-h4585015_3.conda
47
+ Extracting libcurl-8.11.1-h332b0f4_0.conda
48
+ Extracting menuinst-2.2.0-py311h38be061_0.conda
49
+ Extracting archspec-0.2.3-pyhd8ed1ab_0.conda
50
+ Extracting boltons-24.0.0-pyhd8ed1ab_1.conda
51
+ Extracting brotli-python-1.1.0-py311hfdbb021_2.conda
52
+ Extracting certifi-2024.12.14-pyhd8ed1ab_0.conda
53
+ Extracting charset-normalizer-3.4.1-pyhd8ed1ab_0.conda
54
+ Extracting colorama-0.4.6-pyhd8ed1ab_1.conda
55
+ Extracting distro-1.9.0-pyhd8ed1ab_1.conda
56
+ Extracting frozendict-2.4.6-py311h9ecbd09_0.conda
57
+ Extracting hpack-4.0.0-pyhd8ed1ab_1.conda
58
+ Extracting hyperframe-6.0.1-pyhd8ed1ab_1.conda
59
+ Extracting idna-3.10-pyhd8ed1ab_1.conda
60
+ Extracting jsonpointer-3.0.0-py311h38be061_1.conda
61
+ Extracting libmamba-1.5.12-h49b8a8d_0.conda
62
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63
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64
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65
+ Extracting pycosat-0.6.6-py311h9ecbd09_2.conda
66
+ Extracting pycparser-2.22-pyh29332c3_1.conda
67
+ Extracting pysocks-1.7.1-pyha55dd90_7.conda
68
+ Extracting ruamel.yaml.clib-0.2.8-py311h9ecbd09_1.conda
69
+ Extracting setuptools-65.6.3-pyhd8ed1ab_0.conda
70
+ Extracting truststore-0.10.0-pyhd8ed1ab_0.conda
71
+ Extracting wheel-0.45.1-pyhd8ed1ab_1.conda
72
+ Extracting cffi-1.17.1-py311hf29c0ef_0.conda
73
+ Extracting h2-4.1.0-pyhd8ed1ab_1.conda
74
+ Extracting jsonpatch-1.33-pyhd8ed1ab_1.conda
75
+ Extracting libmambapy-1.5.12-py311hb3373dd_0.conda
76
+ Extracting pip-24.3.1-pyh8b19718_2.conda
77
+ Extracting ruamel.yaml-0.18.10-py311h9ecbd09_0.conda
78
+ Extracting tqdm-4.67.1-pyhd8ed1ab_1.conda
79
+ Extracting zstandard-0.23.0-py311hbc35293_1.conda
80
+ Extracting conda-package-streaming-0.11.0-pyhd8ed1ab_0.conda
81
+ Extracting urllib3-2.3.0-pyhd8ed1ab_0.conda
82
+ Extracting requests-2.32.3-pyhd8ed1ab_1.conda
83
+ Extracting conda-package-handling-2.4.0-pyh7900ff3_2.conda
84
+ Extracting conda-24.11.2-py311h38be061_1.conda
85
+ Extracting conda-libmamba-solver-24.9.0-pyhd8ed1ab_0.conda
86
+ Extracting mamba-1.5.12-py311h3072747_0.conda
87
+
88
+ Installing base environment...
89
+
90
+ Transaction
91
+
92
+ Prefix: /usr/local
93
+
94
+ Updating specs:
95
+
96
+ - conda-forge/linux-64::_libgcc_mutex==0.1=conda_forge[md5=d7c89558ba9fa0495403155b64376d81]
97
+ - conda-forge/linux-64::ca-certificates==2024.12.14=hbcca054_0[md5=720523eb0d6a9b0f6120c16b2aa4e7de]
98
+ - conda-forge/linux-64::ld_impl_linux-64==2.43=h712a8e2_2[md5=048b02e3962f066da18efe3a21b77672]
99
+ - conda-forge/noarch::pybind11-abi==4=hd8ed1ab_3[md5=878f923dd6acc8aeb47a75da6c4098be]
100
+ - conda-forge/linux-64::python_abi==3.11=5_cp311[md5=139a8d40c8a2f430df31048949e450de]
101
+ - conda-forge/noarch::tzdata==2024b=hc8b5060_0[md5=8ac3367aafb1cc0a068483c580af8015]
102
+ - conda-forge/linux-64::libgomp==14.2.0=h77fa898_1[md5=cc3573974587f12dda90d96e3e55a702]
103
+ - conda-forge/linux-64::_openmp_mutex==4.5=2_gnu[md5=73aaf86a425cc6e73fcf236a5a46396d]
104
+ - conda-forge/linux-64::libgcc==14.2.0=h77fa898_1[md5=3cb76c3f10d3bc7f1105b2fc9db984df]
105
+ - conda-forge/linux-64::c-ares==1.34.4=hb9d3cd8_0[md5=e2775acf57efd5af15b8e3d1d74d72d3]
106
+ - conda-forge/linux-64::libexpat==2.6.4=h5888daf_0[md5=db833e03127376d461e1e13e76f09b6c]
107
+ - conda-forge/linux-64::libgcc-ng==14.2.0=h69a702a_1[md5=e39480b9ca41323497b05492a63bc35b]
108
+ - conda-forge/linux-64::liblzma==5.6.3=hb9d3cd8_1[md5=2ecf2f1c7e4e21fcfe6423a51a992d84]
109
+ - conda-forge/linux-64::libstdcxx==14.2.0=hc0a3c3a_1[md5=234a5554c53625688d51062645337328]
110
+ - conda-forge/linux-64::libzlib==1.3.1=hb9d3cd8_2[md5=edb0dca6bc32e4f4789199455a1dbeb8]
111
+ - conda-forge/linux-64::ncurses==6.5=h2d0b736_2[md5=04b34b9a40cdc48cfdab261ab176ff74]
112
+ - conda-forge/linux-64::openssl==3.4.0=h7b32b05_1[md5=4ce6875f75469b2757a65e10a5d05e31]
113
+ - conda-forge/linux-64::reproc==14.2.5.post0=hb9d3cd8_0[md5=69fbc0a9e42eb5fe6733d2d60d818822]
114
+ - conda-forge/linux-64::bzip2==1.0.8=h4bc722e_7[md5=62ee74e96c5ebb0af99386de58cf9553]
115
+ - conda-forge/linux-64::keyutils==1.6.1=h166bdaf_0[md5=30186d27e2c9fa62b45fb1476b7200e3]
116
+ - conda-forge/linux-64::libedit==3.1.20240808=pl5321h7949ede_0[md5=8247f80f3dc464d9322e85007e307fe8]
117
+ - conda-forge/linux-64::libev==4.33=hd590300_2[md5=172bf1cd1ff8629f2b1179945ed45055]
118
+ - conda-forge/linux-64::libffi==3.4.2=h7f98852_5[md5=d645c6d2ac96843a2bfaccd2d62b3ac3]
119
+ - conda-forge/linux-64::libiconv==1.17=hd590300_2[md5=d66573916ffcf376178462f1b61c941e]
120
+ - conda-forge/linux-64::libnsl==2.0.1=hd590300_0[md5=30fd6e37fe21f86f4bd26d6ee73eeec7]
121
+ - conda-forge/linux-64::libsqlite==3.47.2=hee588c1_0[md5=b58da17db24b6e08bcbf8fed2fb8c915]
122
+ - conda-forge/linux-64::libssh2==1.11.1=hf672d98_0[md5=be2de152d8073ef1c01b7728475f2fe7]
123
+ - conda-forge/linux-64::libstdcxx-ng==14.2.0=h4852527_1[md5=8371ac6457591af2cf6159439c1fd051]
124
+ - conda-forge/linux-64::libuuid==2.38.1=h0b41bf4_0[md5=40b61aab5c7ba9ff276c41cfffe6b80b]
125
+ - conda-forge/linux-64::libxcrypt==4.4.36=hd590300_1[md5=5aa797f8787fe7a17d1b0821485b5adc]
126
+ - conda-forge/linux-64::lz4-c==1.10.0=h5888daf_1[md5=9de5350a85c4a20c685259b889aa6393]
127
+ - conda-forge/linux-64::lzo==2.10=hd590300_1001[md5=ec7398d21e2651e0dcb0044d03b9a339]
128
+ - conda-forge/linux-64::readline==8.2=h8228510_1[md5=47d31b792659ce70f470b5c82fdfb7a4]
129
+ - conda-forge/linux-64::reproc-cpp==14.2.5.post0=h5888daf_0[md5=828302fca535f9cfeb598d5f7c204323]
130
+ - conda-forge/linux-64::tk==8.6.13=noxft_h4845f30_101[md5=d453b98d9c83e71da0741bb0ff4d76bc]
131
+ - conda-forge/linux-64::fmt==11.0.2=h434a139_0[md5=995f7e13598497691c1dc476d889bc04]
132
+ - conda-forge/linux-64::krb5==1.21.3=h659f571_0[md5=3f43953b7d3fb3aaa1d0d0723d91e368]
133
+ - conda-forge/linux-64::libnghttp2==1.64.0=h161d5f1_0[md5=19e57602824042dfd0446292ef90488b]
134
+ - conda-forge/linux-64::libsolv==0.7.30=h3509ff9_0[md5=02539b77d25aa4f65b20246549e256c3]
135
+ - conda-forge/linux-64::libxml2==2.13.5=h0d44e9d_1[md5=f5b05674697ae7d2c5932766695945e1]
136
+ - conda-forge/linux-64::python==3.11.11=h9e4cc4f_1_cpython[md5=8387070aa413ce9a8cc35a509fae938b]
137
+ - conda-forge/linux-64::yaml-cpp==0.8.0=h59595ed_0[md5=965eaacd7c18eb8361fd12bb9e7a57d7]
138
+ - conda-forge/linux-64::zstd==1.5.6=ha6fb4c9_0[md5=4d056880988120e29d75bfff282e0f45]
139
+ - conda-forge/linux-64::libarchive==3.7.7=h4585015_3[md5=a28808eae584c7f519943719b2a2b386]
140
+ - conda-forge/linux-64::libcurl==8.11.1=h332b0f4_0[md5=2b3e0081006dc21e8bf53a91c83a055c]
141
+ - conda-forge/linux-64::menuinst==2.2.0=py311h38be061_0[md5=56b688f5333037364ddf9c5cbdd4d672]
142
+ - conda-forge/noarch::archspec==0.2.3=pyhd8ed1ab_0[md5=192278292e20704f663b9c766909d67b]
143
+ - conda-forge/noarch::boltons==24.0.0=pyhd8ed1ab_1[md5=d88c38e66d85ecc9c7e2c4110676bbf4]
144
+ - conda-forge/linux-64::brotli-python==1.1.0=py311hfdbb021_2[md5=d21daab070d76490cb39a8f1d1729d79]
145
+ - conda-forge/noarch::certifi==2024.12.14=pyhd8ed1ab_0[md5=6feb87357ecd66733be3279f16a8c400]
146
+ - conda-forge/noarch::charset-normalizer==3.4.1=pyhd8ed1ab_0[md5=e83a31202d1c0a000fce3e9cf3825875]
147
+ - conda-forge/noarch::colorama==0.4.6=pyhd8ed1ab_1[md5=962b9857ee8e7018c22f2776ffa0b2d7]
148
+ - conda-forge/noarch::distro==1.9.0=pyhd8ed1ab_1[md5=0a2014fd9860f8b1eaa0b1f3d3771a08]
149
+ - conda-forge/linux-64::frozendict==2.4.6=py311h9ecbd09_0[md5=5ed089614b88920f8cc673fe3bc34558]
150
+ - conda-forge/noarch::hpack==4.0.0=pyhd8ed1ab_1[md5=2aa5ff7fa34a81b9196532c84c10d865]
151
+ - conda-forge/noarch::hyperframe==6.0.1=pyhd8ed1ab_1[md5=566e75c90c1d0c8c459eb0ad9833dc7a]
152
+ - conda-forge/noarch::idna==3.10=pyhd8ed1ab_1[md5=39a4f67be3286c86d696df570b1201b7]
153
+ - conda-forge/linux-64::jsonpointer==3.0.0=py311h38be061_1[md5=5ca76f61b00a15a9be0612d4d883badc]
154
+ - conda-forge/linux-64::libmamba==1.5.12=h49b8a8d_0[md5=d6aec145c4f2473c09c2699d74f6dfc0]
155
+ - conda-forge/noarch::packaging==24.2=pyhd8ed1ab_2[md5=3bfed7e6228ebf2f7b9eaa47f1b4e2aa]
156
+ - conda-forge/noarch::platformdirs==4.3.6=pyhd8ed1ab_1[md5=577852c7e53901ddccc7e6a9959ddebe]
157
+ - conda-forge/noarch::pluggy==1.5.0=pyhd8ed1ab_1[md5=e9dcbce5f45f9ee500e728ae58b605b6]
158
+ - conda-forge/linux-64::pycosat==0.6.6=py311h9ecbd09_2[md5=65a6d37c5a2868d5605cf2df3ea0236e]
159
+ - conda-forge/noarch::pycparser==2.22=pyh29332c3_1[md5=12c566707c80111f9799308d9e265aef]
160
+ - conda-forge/noarch::pysocks==1.7.1=pyha55dd90_7[md5=461219d1a5bd61342293efa2c0c90eac]
161
+ - conda-forge/linux-64::ruamel.yaml.clib==0.2.8=py311h9ecbd09_1[md5=e56869fca385961323e43783b89bef66]
162
+ - conda-forge/noarch::setuptools==65.6.3=pyhd8ed1ab_0[md5=9600fc9524d3f821e6a6d58c52f5bf5a]
163
+ - conda-forge/noarch::truststore==0.10.0=pyhd8ed1ab_0[md5=ad1c20cd193e3044bcf17798c33b9d67]
164
+ - conda-forge/noarch::wheel==0.45.1=pyhd8ed1ab_1[md5=75cb7132eb58d97896e173ef12ac9986]
165
+ - conda-forge/linux-64::cffi==1.17.1=py311hf29c0ef_0[md5=55553ecd5328336368db611f350b7039]
166
+ - conda-forge/noarch::h2==4.1.0=pyhd8ed1ab_1[md5=825927dc7b0f287ef8d4d0011bb113b1]
167
+ - conda-forge/noarch::jsonpatch==1.33=pyhd8ed1ab_1[md5=cb60ae9cf02b9fcb8004dec4089e5691]
168
+ - conda-forge/linux-64::libmambapy==1.5.12=py311hb3373dd_0[md5=47fb74d72998e00565428ccdcafc97cb]
169
+ - conda-forge/noarch::pip==24.3.1=pyh8b19718_2[md5=04e691b9fadd93a8a9fad87a81d4fd8f]
170
+ - conda-forge/linux-64::ruamel.yaml==0.18.10=py311h9ecbd09_0[md5=a3188715e28c25f1404b84c702e6fdf4]
171
+ - conda-forge/noarch::tqdm==4.67.1=pyhd8ed1ab_1[md5=9efbfdc37242619130ea42b1cc4ed861]
172
+ - conda-forge/linux-64::zstandard==0.23.0=py311hbc35293_1[md5=aec590674ba365e50ae83aa2d6e1efae]
173
+ - conda-forge/noarch::conda-package-streaming==0.11.0=pyhd8ed1ab_0[md5=bc9533d8616a97551ed144789bf9c1cd]
174
+ - conda-forge/noarch::urllib3==2.3.0=pyhd8ed1ab_0[md5=32674f8dbfb7b26410ed580dd3c10a29]
175
+ - conda-forge/noarch::requests==2.32.3=pyhd8ed1ab_1[md5=a9b9368f3701a417eac9edbcae7cb737]
176
+ - conda-forge/noarch::conda-package-handling==2.4.0=pyh7900ff3_2[md5=32c158f481b4fd7630c565030f7bc482]
177
+ - conda-forge/linux-64::conda==24.11.2=py311h38be061_1[md5=665bae7596e3f4131355ef85e30b8106]
178
+ - conda-forge/noarch::conda-libmamba-solver==24.9.0=pyhd8ed1ab_0[md5=45378d089c5f72c9c0d63d58414c645d]
179
+ - conda-forge/linux-64::mamba==1.5.12=py311h3072747_0[md5=d681a3fbf59b09439d72955897d7fc76]
180
+
181
+
182
+ Package Version Build Channel Size
183
+ ─────────────────────────────────────────────────────────────────────────────────────
184
+ Install:
185
+ ─────────────────────────────────────────────────────────────────────────────────────
186
+
187
+ + _libgcc_mutex 0.1 conda_forge conda-forge
188
+ + ca-certificates 2024.12.14 hbcca054_0 conda-forge
189
+ + ld_impl_linux-64 2.43 h712a8e2_2 conda-forge
190
+ + pybind11-abi 4 hd8ed1ab_3 conda-forge
191
+ + python_abi 3.11 5_cp311 conda-forge
192
+ + tzdata 2024b hc8b5060_0 conda-forge
193
+ + libgomp 14.2.0 h77fa898_1 conda-forge
194
+ + _openmp_mutex 4.5 2_gnu conda-forge
195
+ + libgcc 14.2.0 h77fa898_1 conda-forge
196
+ + c-ares 1.34.4 hb9d3cd8_0 conda-forge
197
+ + libexpat 2.6.4 h5888daf_0 conda-forge
198
+ + libgcc-ng 14.2.0 h69a702a_1 conda-forge
199
+ + liblzma 5.6.3 hb9d3cd8_1 conda-forge
200
+ + libstdcxx 14.2.0 hc0a3c3a_1 conda-forge
201
+ + libzlib 1.3.1 hb9d3cd8_2 conda-forge
202
+ + ncurses 6.5 h2d0b736_2 conda-forge
203
+ + openssl 3.4.0 h7b32b05_1 conda-forge
204
+ + reproc 14.2.5.post0 hb9d3cd8_0 conda-forge
205
+ + bzip2 1.0.8 h4bc722e_7 conda-forge
206
+ + keyutils 1.6.1 h166bdaf_0 conda-forge
207
+ + libedit 3.1.20240808 pl5321h7949ede_0 conda-forge
208
+ + libev 4.33 hd590300_2 conda-forge
209
+ + libffi 3.4.2 h7f98852_5 conda-forge
210
+ + libiconv 1.17 hd590300_2 conda-forge
211
+ + libnsl 2.0.1 hd590300_0 conda-forge
212
+ + libsqlite 3.47.2 hee588c1_0 conda-forge
213
+ + libssh2 1.11.1 hf672d98_0 conda-forge
214
+ + libstdcxx-ng 14.2.0 h4852527_1 conda-forge
215
+ + libuuid 2.38.1 h0b41bf4_0 conda-forge
216
+ + libxcrypt 4.4.36 hd590300_1 conda-forge
217
+ + lz4-c 1.10.0 h5888daf_1 conda-forge
218
+ + lzo 2.10 hd590300_1001 conda-forge
219
+ + readline 8.2 h8228510_1 conda-forge
220
+ + reproc-cpp 14.2.5.post0 h5888daf_0 conda-forge
221
+ + tk 8.6.13 noxft_h4845f30_101 conda-forge
222
+ + fmt 11.0.2 h434a139_0 conda-forge
223
+ + krb5 1.21.3 h659f571_0 conda-forge
224
+ + libnghttp2 1.64.0 h161d5f1_0 conda-forge
225
+ + libsolv 0.7.30 h3509ff9_0 conda-forge
226
+ + libxml2 2.13.5 h0d44e9d_1 conda-forge
227
+ + python 3.11.11 h9e4cc4f_1_cpython conda-forge
228
+ + yaml-cpp 0.8.0 h59595ed_0 conda-forge
229
+ + zstd 1.5.6 ha6fb4c9_0 conda-forge
230
+ + libarchive 3.7.7 h4585015_3 conda-forge
231
+ + libcurl 8.11.1 h332b0f4_0 conda-forge
232
+ + menuinst 2.2.0 py311h38be061_0 conda-forge
233
+ + archspec 0.2.3 pyhd8ed1ab_0 conda-forge
234
+ + boltons 24.0.0 pyhd8ed1ab_1 conda-forge
235
+ + brotli-python 1.1.0 py311hfdbb021_2 conda-forge
236
+ + certifi 2024.12.14 pyhd8ed1ab_0 conda-forge
237
+ + charset-normalizer 3.4.1 pyhd8ed1ab_0 conda-forge
238
+ + colorama 0.4.6 pyhd8ed1ab_1 conda-forge
239
+ + distro 1.9.0 pyhd8ed1ab_1 conda-forge
240
+ + frozendict 2.4.6 py311h9ecbd09_0 conda-forge
241
+ + hpack 4.0.0 pyhd8ed1ab_1 conda-forge
242
+ + hyperframe 6.0.1 pyhd8ed1ab_1 conda-forge
243
+ + idna 3.10 pyhd8ed1ab_1 conda-forge
244
+ + jsonpointer 3.0.0 py311h38be061_1 conda-forge
245
+ + libmamba 1.5.12 h49b8a8d_0 conda-forge
246
+ + packaging 24.2 pyhd8ed1ab_2 conda-forge
247
+ + platformdirs 4.3.6 pyhd8ed1ab_1 conda-forge
248
+ + pluggy 1.5.0 pyhd8ed1ab_1 conda-forge
249
+ + pycosat 0.6.6 py311h9ecbd09_2 conda-forge
250
+ + pycparser 2.22 pyh29332c3_1 conda-forge
251
+ + pysocks 1.7.1 pyha55dd90_7 conda-forge
252
+ + ruamel.yaml.clib 0.2.8 py311h9ecbd09_1 conda-forge
253
+ + setuptools 65.6.3 pyhd8ed1ab_0 conda-forge
254
+ + truststore 0.10.0 pyhd8ed1ab_0 conda-forge
255
+ + wheel 0.45.1 pyhd8ed1ab_1 conda-forge
256
+ + cffi 1.17.1 py311hf29c0ef_0 conda-forge
257
+ + h2 4.1.0 pyhd8ed1ab_1 conda-forge
258
+ + jsonpatch 1.33 pyhd8ed1ab_1 conda-forge
259
+ + libmambapy 1.5.12 py311hb3373dd_0 conda-forge
260
+ + pip 24.3.1 pyh8b19718_2 conda-forge
261
+ + ruamel.yaml 0.18.10 py311h9ecbd09_0 conda-forge
262
+ + tqdm 4.67.1 pyhd8ed1ab_1 conda-forge
263
+ + zstandard 0.23.0 py311hbc35293_1 conda-forge
264
+ + conda-package-streaming 0.11.0 pyhd8ed1ab_0 conda-forge
265
+ + urllib3 2.3.0 pyhd8ed1ab_0 conda-forge
266
+ + requests 2.32.3 pyhd8ed1ab_1 conda-forge
267
+ + conda-package-handling 2.4.0 pyh7900ff3_2 conda-forge
268
+ + conda 24.11.2 py311h38be061_1 conda-forge
269
+ + conda-libmamba-solver 24.9.0 pyhd8ed1ab_0 conda-forge
270
+ + mamba 1.5.12 py311h3072747_0 conda-forge
271
+
272
+ Summary:
273
+
274
+ Install: 84 packages
275
+
276
+ Total download: 0 B
277
+
278
+ ─────────────────────────────────────────────────────────────────────────────────────
279
+
280
+
281
+
282
+ Transaction starting
283
+ warning libmamba [python-3.11.11-h9e4cc4f_1_cpython] The following files were already present in the environment:
284
+ - bin/python
285
+ warning libmamba [charset-normalizer-3.4.1-pyhd8ed1ab_0] The following files were already present in the environment:
286
+ - bin/normalizer
287
+ warning libmamba [distro-1.9.0-pyhd8ed1ab_1] The following files were already present in the environment:
288
+ - bin/distro
289
+ warning libmamba [jsonpointer-3.0.0-py311h38be061_1] The following files were already present in the environment:
290
+ - bin/jsonpointer
291
+ warning libmamba [wheel-0.45.1-pyhd8ed1ab_1] The following files were already present in the environment:
292
+ - bin/wheel
293
+ warning libmamba [jsonpatch-1.33-pyhd8ed1ab_1] The following files were already present in the environment:
294
+ - bin/jsondiff
295
+ - bin/jsonpatch
296
+ warning libmamba [pip-24.3.1-pyh8b19718_2] The following files were already present in the environment:
297
+ - bin/pip
298
+ - bin/pip3
299
+ warning libmamba [tqdm-4.67.1-pyhd8ed1ab_1] The following files were already present in the environment:
300
+ - bin/tqdm
301
+
302
+ Transaction finished
303
+
304
+ To activate this environment, use:
305
+
306
+ micromamba activate /usr/local
307
+
308
+ Or to execute a single command in this environment, use:
309
+
310
+ micromamba run -p /usr/local mycommand
311
+
312
+ installation finished.
313
+ WARNING:
314
+ You currently have a PYTHONPATH environment variable set. This may cause
315
+ unexpected behavior when running the Python interpreter in Miniforge3-colab.
316
+ For best results, please verify that your PYTHONPATH only points to
317
+ directories of packages that are compatible with the Python interpreter
318
+ in Miniforge3-colab: /usr/local
lerobot/.dockerignore ADDED
@@ -0,0 +1,160 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright 2024 The HuggingFace Inc. team. All rights reserved.
2
+ #
3
+ # Licensed under the Apache License, Version 2.0 (the "License");
4
+ # you may not use this file except in compliance with the License.
5
+ # You may obtain a copy of the License at
6
+ #
7
+ # http://www.apache.org/licenses/LICENSE-2.0
8
+ #
9
+ # Unless required by applicable law or agreed to in writing, software
10
+ # distributed under the License is distributed on an "AS IS" BASIS,
11
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12
+ # See the License for the specific language governing permissions and
13
+ # limitations under the License.
14
+
15
+ # Misc
16
+ .git
17
+ tmp
18
+ wandb
19
+ data
20
+ outputs
21
+ .vscode
22
+ rl
23
+ media
24
+
25
+
26
+ # Logging
27
+ logs
28
+
29
+ # HPC
30
+ nautilus/*.yaml
31
+ *.key
32
+
33
+ # Slurm
34
+ sbatch*.sh
35
+
36
+ # Byte-compiled / optimized / DLL files
37
+ __pycache__/
38
+ *.py[cod]
39
+ *$py.class
40
+
41
+ # C extensions
42
+ *.so
43
+
44
+ # Distribution / packaging
45
+ .Python
46
+ build/
47
+ develop-eggs/
48
+ dist/
49
+ downloads/
50
+ eggs/
51
+ .eggs/
52
+ lib/
53
+ lib64/
54
+ parts/
55
+ sdist/
56
+ var/
57
+ wheels/
58
+ pip-wheel-metadata/
59
+ share/python-wheels/
60
+ *.egg-info/
61
+ .installed.cfg
62
+ *.egg
63
+ MANIFEST
64
+
65
+ # PyInstaller
66
+ # Usually these files are written by a python script from a template
67
+ # before PyInstaller builds the exe, so as to inject date/other infos into it.
68
+ *.manifest
69
+ *.spec
70
+
71
+ # Installer logs
72
+ pip-log.txt
73
+ pip-delete-this-directory.txt
74
+
75
+ # Unit test / coverage reports
76
+ !tests/artifacts
77
+ htmlcov/
78
+ .tox/
79
+ .nox/
80
+ .coverage
81
+ .coverage.*
82
+ nosetests.xml
83
+ coverage.xml
84
+ *.cover
85
+ *.py,cover
86
+ .hypothesis/
87
+ .pytest_cache/
88
+
89
+ # Ignore .cache except calibration
90
+ .cache/*
91
+ !.cache/calibration/
92
+ !.cache/calibration/**
93
+
94
+ # Translations
95
+ *.mo
96
+ *.pot
97
+
98
+ # Django stuff:
99
+ *.log
100
+ local_settings.py
101
+ db.sqlite3
102
+ db.sqlite3-journal
103
+
104
+ # Flask stuff:
105
+ instance/
106
+ .webassets-cache
107
+
108
+ # Scrapy stuff:
109
+ .scrapy
110
+
111
+ # Sphinx documentation
112
+ docs/_build/
113
+
114
+ # PyBuilder
115
+ target/
116
+
117
+ # Jupyter Notebook
118
+ .ipynb_checkpoints
119
+
120
+ # IPython
121
+ profile_default/
122
+ ipython_config.py
123
+
124
+ # pyenv
125
+ .python-version
126
+
127
+ # pipenv
128
+ # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
129
+ # However, in case of collaboration, if having platform-specific dependencies or dependencies
130
+ # having no cross-platform support, pipenv may install dependencies that don't work, or not
131
+ # install all needed dependencies.
132
+ #Pipfile.lock
133
+
134
+ # PEP 582; used by e.g. github.com/David-OConnor/pyflow
135
+ __pypackages__/
136
+
137
+ # Celery stuff
138
+ celerybeat-schedule
139
+ celerybeat.pid
140
+
141
+ # SageMath parsed files
142
+ *.sage.py
143
+
144
+ # Spyder project settings
145
+ .spyderproject
146
+ .spyproject
147
+
148
+ # Rope project settings
149
+ .ropeproject
150
+
151
+ # mkdocs documentation
152
+ /site
153
+
154
+ # mypy
155
+ .mypy_cache/
156
+ .dmypy.json
157
+ dmypy.json
158
+
159
+ # Pyre type checker
160
+ .pyre/
lerobot/.gitattributes ADDED
@@ -0,0 +1,21 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright 2024 The HuggingFace Inc. team. All rights reserved.
2
+ #
3
+ # Licensed under the Apache License, Version 2.0 (the "License");
4
+ # you may not use this file except in compliance with the License.
5
+ # You may obtain a copy of the License at
6
+ #
7
+ # http://www.apache.org/licenses/LICENSE-2.0
8
+ #
9
+ # Unless required by applicable law or agreed to in writing, software
10
+ # distributed under the License is distributed on an "AS IS" BASIS,
11
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12
+ # See the License for the specific language governing permissions and
13
+ # limitations under the License.
14
+ *.memmap filter=lfs diff=lfs merge=lfs -text
15
+ *.stl filter=lfs diff=lfs merge=lfs -text
16
+ *.safetensors filter=lfs diff=lfs merge=lfs -text
17
+ *.mp4 filter=lfs diff=lfs merge=lfs -text
18
+ *.arrow filter=lfs diff=lfs merge=lfs -text
19
+ *.json !text !filter !merge !diff
20
+ tests/artifacts/cameras/*.png filter=lfs diff=lfs merge=lfs -text
21
+ *.bag filter=lfs diff=lfs merge=lfs -text
lerobot/.github/ISSUE_TEMPLATE/bug-report.yml ADDED
@@ -0,0 +1,68 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright 2024 The HuggingFace Inc. team. All rights reserved.
2
+ #
3
+ # Licensed under the Apache License, Version 2.0 (the "License");
4
+ # you may not use this file except in compliance with the License.
5
+ # You may obtain a copy of the License at
6
+ #
7
+ # http://www.apache.org/licenses/LICENSE-2.0
8
+ #
9
+ # Unless required by applicable law or agreed to in writing, software
10
+ # distributed under the License is distributed on an "AS IS" BASIS,
11
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12
+ # See the License for the specific language governing permissions and
13
+ # limitations under the License.
14
+
15
+ name: "\U0001F41B Bug Report"
16
+ description: Submit a bug report to help us improve LeRobot
17
+ body:
18
+ - type: markdown
19
+ attributes:
20
+ value: |
21
+ Thanks for taking the time to submit a bug report! 🐛
22
+ If this is not a bug related to the LeRobot library directly, but instead a general question about your code or the library specifically please use our [discord](https://discord.gg/s3KuuzsPFb).
23
+
24
+ - type: textarea
25
+ id: system-info
26
+ attributes:
27
+ label: System Info
28
+ description: If needed, you can share your lerobot configuration with us by running `python -m lerobot.scripts.display_sys_info` and copy-pasting its outputs below
29
+ render: Shell
30
+ placeholder: lerobot version, OS, python version, numpy version, torch version, and lerobot's configuration
31
+ validations:
32
+ required: true
33
+
34
+ - type: checkboxes
35
+ id: information-scripts-examples
36
+ attributes:
37
+ label: Information
38
+ description: 'The problem arises when using:'
39
+ options:
40
+ - label: "One of the scripts in the examples/ folder of LeRobot"
41
+ - label: "My own task or dataset (give details below)"
42
+
43
+ - type: textarea
44
+ id: reproduction
45
+ validations:
46
+ required: true
47
+ attributes:
48
+ label: Reproduction
49
+ description: |
50
+ If needed, provide a simple code sample that reproduces the problem you ran into. It can be a Colab link or just a code snippet.
51
+ Sharing error messages or stack traces could be useful as well!
52
+ Important! Use code tags to correctly format your code. See https://help.github.com/en/github/writing-on-github/creating-and-highlighting-code-blocks#syntax-highlighting
53
+ Try to avoid screenshots, as they are hard to read and don't allow copy-and-pasting.
54
+
55
+ placeholder: |
56
+ Steps to reproduce the behavior:
57
+
58
+ 1.
59
+ 2.
60
+ 3.
61
+
62
+ - type: textarea
63
+ id: expected-behavior
64
+ validations:
65
+ required: true
66
+ attributes:
67
+ label: Expected behavior
68
+ description: "A clear and concise description of what you would expect to happen."
lerobot/.github/PULL_REQUEST_TEMPLATE.md ADDED
@@ -0,0 +1,34 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ## What this does
2
+ Explain what this PR does. Feel free to tag your PR with the appropriate label(s).
3
+
4
+ Examples:
5
+ | Title | Label |
6
+ |----------------------|-----------------|
7
+ | Fixes #[issue] | (🐛 Bug) |
8
+ | Adds new dataset | (🗃️ Dataset) |
9
+ | Optimizes something | (⚡️ Performance) |
10
+
11
+ ## How it was tested
12
+ Explain/show how you tested your changes.
13
+
14
+ Examples:
15
+ - Added `test_something` in `tests/test_stuff.py`.
16
+ - Added `new_feature` and checked that training converges with policy X on dataset/environment Y.
17
+ - Optimized `some_function`, it now runs X times faster than previously.
18
+
19
+ ## How to checkout & try? (for the reviewer)
20
+ Provide a simple way for the reviewer to try out your changes.
21
+
22
+ Examples:
23
+ ```bash
24
+ pytest -sx tests/test_stuff.py::test_something
25
+ ```
26
+ ```bash
27
+ python -m lerobot.scripts.train --some.option=true
28
+ ```
29
+
30
+ ## SECTION TO REMOVE BEFORE SUBMITTING YOUR PR
31
+ **Note**: Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
32
+ members/contributors who may be interested in your PR. Try to avoid tagging more than 3 people.
33
+
34
+ **Note**: Before submitting this PR, please read the [contributor guideline](https://github.com/huggingface/lerobot/blob/main/CONTRIBUTING.md#submitting-a-pull-request-pr).
lerobot/.github/workflows/build-docker-images.yml ADDED
@@ -0,0 +1,135 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright 2024 The HuggingFace Inc. team. All rights reserved.
2
+ #
3
+ # Licensed under the Apache License, Version 2.0 (the "License");
4
+ # you may not use this file except in compliance with the License.
5
+ # You may obtain a copy of the License at
6
+ #
7
+ # http://www.apache.org/licenses/LICENSE-2.0
8
+ #
9
+ # Unless required by applicable law or agreed to in writing, software
10
+ # distributed under the License is distributed on an "AS IS" BASIS,
11
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12
+ # See the License for the specific language governing permissions and
13
+ # limitations under the License.
14
+
15
+ # Inspired by
16
+ # https://github.com/huggingface/peft/blob/main/.github/workflows/build_docker_images.yml
17
+ name: Builds
18
+
19
+ on:
20
+ workflow_dispatch:
21
+ workflow_call:
22
+ schedule:
23
+ - cron: "0 1 * * *"
24
+
25
+ permissions: {}
26
+
27
+ env:
28
+ PYTHON_VERSION: "3.10"
29
+
30
+ jobs:
31
+ latest-cpu:
32
+ name: CPU
33
+ runs-on:
34
+ group: aws-general-8-plus
35
+ steps:
36
+ - name: Install Git LFS
37
+ run: |
38
+ sudo apt-get update
39
+ sudo apt-get install git-lfs
40
+ git lfs install
41
+
42
+ - name: Set up Docker Buildx
43
+ uses: docker/setup-buildx-action@b5ca514318bd6ebac0fb2aedd5d36ec1b5c232a2 # v3.10.0
44
+ with:
45
+ cache-binary: false
46
+
47
+ - name: Check out code
48
+ uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
49
+ with:
50
+ lfs: true
51
+ persist-credentials: false
52
+
53
+ - name: Login to DockerHub
54
+ uses: docker/login-action@74a5d142397b4f367a81961eba4e8cd7edddf772 # v3.4.0
55
+ with:
56
+ username: ${{ secrets.DOCKERHUB_USERNAME }}
57
+ password: ${{ secrets.DOCKERHUB_PASSWORD }}
58
+
59
+ - name: Build and Push CPU
60
+ uses: docker/build-push-action@ca052bb54ab0790a636c9b5f226502c73d547a25 # v5.4.0
61
+ with:
62
+ context: .
63
+ file: ./docker/lerobot-cpu/Dockerfile
64
+ push: true
65
+ tags: huggingface/lerobot-cpu
66
+ build-args: PYTHON_VERSION=${{ env.PYTHON_VERSION }}
67
+
68
+
69
+ latest-cuda:
70
+ name: GPU
71
+ runs-on:
72
+ group: aws-general-8-plus
73
+ steps:
74
+ - name: Install Git LFS
75
+ run: |
76
+ sudo apt-get update
77
+ sudo apt-get install git-lfs
78
+ git lfs install
79
+
80
+ - name: Set up Docker Buildx
81
+ uses: docker/setup-buildx-action@b5ca514318bd6ebac0fb2aedd5d36ec1b5c232a2 # v3.10.0
82
+ with:
83
+ cache-binary: false
84
+
85
+ - name: Check out code
86
+ uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
87
+ with:
88
+ lfs: true
89
+ persist-credentials: false
90
+
91
+ - name: Login to DockerHub
92
+ uses: docker/login-action@74a5d142397b4f367a81961eba4e8cd7edddf772 # v3.4.0
93
+ with:
94
+ username: ${{ secrets.DOCKERHUB_USERNAME }}
95
+ password: ${{ secrets.DOCKERHUB_PASSWORD }}
96
+
97
+ - name: Build and Push GPU
98
+ uses: docker/build-push-action@ca052bb54ab0790a636c9b5f226502c73d547a25 # v5.4.0
99
+ with:
100
+ context: .
101
+ file: ./docker/lerobot-gpu/Dockerfile
102
+ push: true
103
+ tags: huggingface/lerobot-gpu
104
+ build-args: PYTHON_VERSION=${{ env.PYTHON_VERSION }}
105
+
106
+
107
+ latest-cuda-dev:
108
+ name: GPU Dev
109
+ runs-on:
110
+ group: aws-general-8-plus
111
+ steps:
112
+ - name: Set up Docker Buildx
113
+ uses: docker/setup-buildx-action@b5ca514318bd6ebac0fb2aedd5d36ec1b5c232a2 # v3.10.0
114
+ with:
115
+ cache-binary: false
116
+
117
+ - name: Check out code
118
+ uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
119
+ with:
120
+ persist-credentials: false
121
+
122
+ - name: Login to DockerHub
123
+ uses: docker/login-action@74a5d142397b4f367a81961eba4e8cd7edddf772 # v3.4.0
124
+ with:
125
+ username: ${{ secrets.DOCKERHUB_USERNAME }}
126
+ password: ${{ secrets.DOCKERHUB_PASSWORD }}
127
+
128
+ - name: Build and Push GPU dev
129
+ uses: docker/build-push-action@ca052bb54ab0790a636c9b5f226502c73d547a25 # v5.4.0
130
+ with:
131
+ context: .
132
+ file: ./docker/lerobot-gpu-dev/Dockerfile
133
+ push: true
134
+ tags: huggingface/lerobot-gpu:dev
135
+ build-args: PYTHON_VERSION=${{ env.PYTHON_VERSION }}
lerobot/.github/workflows/build_documentation.yml ADDED
@@ -0,0 +1,23 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ name: Build documentation
2
+
3
+ on:
4
+ workflow_dispatch:
5
+ push:
6
+ paths:
7
+ - "docs/**"
8
+ branches:
9
+ - main
10
+ - doc-builder*
11
+ - v*-release
12
+
13
+
14
+ jobs:
15
+ build: # zizmor: ignore[excessive-permissions] We follow the same pattern as in Transformers
16
+ uses: huggingface/doc-builder/.github/workflows/build_main_documentation.yml@main
17
+ with:
18
+ commit_sha: ${{ github.sha }}
19
+ package: lerobot
20
+ additional_args: --not_python_module
21
+ secrets:
22
+ token: ${{ secrets.HUGGINGFACE_PUSH }}
23
+ hf_token: ${{ secrets.HF_DOC_BUILD_PUSH }}
lerobot/.github/workflows/build_pr_documentation.yml ADDED
@@ -0,0 +1,19 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ name: Build PR Documentation
2
+
3
+ on:
4
+ pull_request:
5
+ paths:
6
+ - "docs/**"
7
+
8
+ concurrency:
9
+ group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
10
+ cancel-in-progress: true
11
+
12
+ jobs:
13
+ build: # zizmor: ignore[excessive-permissions] We follow the same pattern as in Transformers
14
+ uses: huggingface/doc-builder/.github/workflows/build_pr_documentation.yml@main
15
+ with:
16
+ commit_sha: ${{ github.event.pull_request.head.sha }}
17
+ pr_number: ${{ github.event.number }}
18
+ package: lerobot
19
+ additional_args: --not_python_module
lerobot/.github/workflows/nightly-tests.yml ADDED
@@ -0,0 +1,93 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright 2024 The HuggingFace Inc. team. All rights reserved.
2
+ #
3
+ # Licensed under the Apache License, Version 2.0 (the "License");
4
+ # you may not use this file except in compliance with the License.
5
+ # You may obtain a copy of the License at
6
+ #
7
+ # http://www.apache.org/licenses/LICENSE-2.0
8
+ #
9
+ # Unless required by applicable law or agreed to in writing, software
10
+ # distributed under the License is distributed on an "AS IS" BASIS,
11
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12
+ # See the License for the specific language governing permissions and
13
+ # limitations under the License.
14
+
15
+ # Inspired by
16
+ # https://github.com/huggingface/peft/blob/main/.github/workflows/nightly.yml
17
+ name: Nightly
18
+
19
+ on:
20
+ workflow_dispatch:
21
+ schedule:
22
+ - cron: "0 2 * * *"
23
+
24
+ permissions: {}
25
+
26
+ # env:
27
+ # SLACK_API_TOKEN: ${{ secrets.SLACK_API_TOKEN }}
28
+ jobs:
29
+ run_all_tests_cpu:
30
+ name: CPU
31
+ strategy:
32
+ fail-fast: false
33
+ runs-on:
34
+ group: aws-general-8-plus
35
+ container:
36
+ image: huggingface/lerobot-cpu:latest # zizmor: ignore[unpinned-images]
37
+ options: --shm-size "16gb"
38
+ credentials:
39
+ username: ${{ secrets.DOCKERHUB_USERNAME }}
40
+ password: ${{ secrets.DOCKERHUB_PASSWORD }}
41
+ defaults:
42
+ run:
43
+ shell: bash
44
+ working-directory: /lerobot
45
+ steps:
46
+ - name: Tests
47
+ run: pytest -v --cov=./src/lerobot --disable-warnings tests
48
+
49
+ - name: Tests end-to-end
50
+ run: make test-end-to-end
51
+
52
+
53
+ run_all_tests_single_gpu:
54
+ name: GPU
55
+ strategy:
56
+ fail-fast: false
57
+ runs-on:
58
+ group: aws-g6-4xlarge-plus
59
+ env:
60
+ CUDA_VISIBLE_DEVICES: "0"
61
+ TEST_TYPE: "single_gpu"
62
+ container:
63
+ image: huggingface/lerobot-gpu:latest # zizmor: ignore[unpinned-images]
64
+ options: --gpus all --shm-size "16gb"
65
+ credentials:
66
+ username: ${{ secrets.DOCKERHUB_USERNAME }}
67
+ password: ${{ secrets.DOCKERHUB_PASSWORD }}
68
+ defaults:
69
+ run:
70
+ shell: bash
71
+ working-directory: /lerobot
72
+ steps:
73
+ - name: Nvidia-smi
74
+ run: nvidia-smi
75
+
76
+ - name: Test
77
+ run: pytest -v --cov=./src/lerobot --cov-report=xml --disable-warnings tests
78
+ # TODO(aliberts): Link with HF Codecov account
79
+ # - name: Upload coverage reports to Codecov with GitHub Action
80
+ # uses: codecov/codecov-action@v4
81
+ # with:
82
+ # files: ./coverage.xml
83
+ # verbose: true
84
+ - name: Tests end-to-end
85
+ env:
86
+ DEVICE: cuda
87
+ run: make test-end-to-end
88
+
89
+ # - name: Generate Report
90
+ # if: always()
91
+ # run: |
92
+ # pip install slack_sdk tabulate
93
+ # python scripts/log_reports.py >> $GITHUB_STEP_SUMMARY
lerobot/.github/workflows/quality.yml ADDED
@@ -0,0 +1,72 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright 2024 The HuggingFace Inc. team. All rights reserved.
2
+ #
3
+ # Licensed under the Apache License, Version 2.0 (the "License");
4
+ # you may not use this file except in compliance with the License.
5
+ # You may obtain a copy of the License at
6
+ #
7
+ # http://www.apache.org/licenses/LICENSE-2.0
8
+ #
9
+ # Unless required by applicable law or agreed to in writing, software
10
+ # distributed under the License is distributed on an "AS IS" BASIS,
11
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12
+ # See the License for the specific language governing permissions and
13
+ # limitations under the License.
14
+
15
+ name: Quality
16
+
17
+ on:
18
+ workflow_dispatch:
19
+ workflow_call:
20
+ pull_request:
21
+ push:
22
+ branches:
23
+ - main
24
+
25
+ permissions: {}
26
+
27
+ env:
28
+ PYTHON_VERSION: "3.10"
29
+
30
+ jobs:
31
+ style:
32
+ name: Style
33
+ runs-on: ubuntu-latest
34
+ steps:
35
+ - name: Checkout Repository
36
+ uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
37
+ with:
38
+ persist-credentials: false
39
+
40
+ - name: Set up Python
41
+ uses: actions/setup-python@7f4fc3e22c37d6ff65e88745f38bd3157c663f7c # v4.9.1
42
+ with:
43
+ python-version: ${{ env.PYTHON_VERSION }}
44
+
45
+ - name: Get Ruff Version from pre-commit-config.yaml
46
+ id: get-ruff-version
47
+ run: |
48
+ RUFF_VERSION=$(awk '/repo: https:\/\/github.com\/astral-sh\/ruff-pre-commit/{flag=1;next}/rev:/{if(flag){print $2;exit}}' .pre-commit-config.yaml)
49
+ echo "ruff_version=${RUFF_VERSION}" >> $GITHUB_OUTPUT
50
+
51
+ - name: Install Ruff
52
+ env:
53
+ RUFF_VERSION: ${{ steps.get-ruff-version.outputs.ruff_version }}
54
+ run: python -m pip install "ruff==${RUFF_VERSION}"
55
+
56
+ - name: Ruff check
57
+ run: ruff check --output-format=github
58
+
59
+ - name: Ruff format
60
+ run: ruff format --diff
61
+
62
+ typos:
63
+ name: Typos
64
+ runs-on: ubuntu-latest
65
+ steps:
66
+ - name: Checkout Repository
67
+ uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
68
+ with:
69
+ persist-credentials: false
70
+
71
+ - name: typos-action
72
+ uses: crate-ci/typos@db35ee91e80fbb447f33b0e5fbddb24d2a1a884f # v1.29.10
lerobot/.github/workflows/test-docker-build.yml ADDED
@@ -0,0 +1,82 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright 2024 The HuggingFace Inc. team. All rights reserved.
2
+ #
3
+ # Licensed under the Apache License, Version 2.0 (the "License");
4
+ # you may not use this file except in compliance with the License.
5
+ # You may obtain a copy of the License at
6
+ #
7
+ # http://www.apache.org/licenses/LICENSE-2.0
8
+ #
9
+ # Unless required by applicable law or agreed to in writing, software
10
+ # distributed under the License is distributed on an "AS IS" BASIS,
11
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12
+ # See the License for the specific language governing permissions and
13
+ # limitations under the License.
14
+
15
+ # Inspired by
16
+ # https://github.com/huggingface/peft/blob/main/.github/workflows/test-docker-build.yml
17
+ name: Test Dockerfiles
18
+
19
+ on:
20
+ pull_request:
21
+ paths:
22
+ # Run only when DockerFile files are modified
23
+ - "docker/**"
24
+
25
+ permissions: {}
26
+
27
+ env:
28
+ PYTHON_VERSION: "3.10"
29
+
30
+ jobs:
31
+ get_changed_files:
32
+ name: Detect modified Dockerfiles
33
+ runs-on: ubuntu-latest
34
+ outputs:
35
+ matrix: ${{ steps.set-matrix.outputs.matrix }}
36
+ steps:
37
+ - name: Check out code
38
+ uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
39
+ with:
40
+ persist-credentials: false
41
+
42
+ - name: Get changed files
43
+ id: changed-files
44
+ uses: tj-actions/changed-files@3f54ebb830831fc121d3263c1857cfbdc310cdb9 #v42
45
+ with:
46
+ files: docker/**
47
+ json: "true"
48
+
49
+ - name: Run step if only the files listed above change # zizmor: ignore[template-injection]
50
+ if: steps.changed-files.outputs.any_changed == 'true'
51
+ id: set-matrix
52
+ run: |
53
+ echo "matrix=${{ steps.changed-files.outputs.all_changed_files}}" >> $GITHUB_OUTPUT
54
+
55
+ build_modified_dockerfiles:
56
+ name: Build modified Docker images
57
+ needs: get_changed_files
58
+ runs-on:
59
+ group: aws-general-8-plus
60
+ if: needs.get_changed_files.outputs.matrix != ''
61
+ strategy:
62
+ fail-fast: false
63
+ matrix:
64
+ docker-file: ${{ fromJson(needs.get_changed_files.outputs.matrix) }}
65
+ steps:
66
+ - name: Set up Docker Buildx
67
+ uses: docker/setup-buildx-action@b5ca514318bd6ebac0fb2aedd5d36ec1b5c232a2 # v3.10.0
68
+ with:
69
+ cache-binary: false
70
+
71
+ - name: Check out code
72
+ uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
73
+ with:
74
+ persist-credentials: false
75
+
76
+ - name: Build Docker image
77
+ uses: docker/build-push-action@ca052bb54ab0790a636c9b5f226502c73d547a25 # v5.4.0
78
+ with:
79
+ file: ${{ matrix.docker-file }}
80
+ context: .
81
+ push: False
82
+ build-args: PYTHON_VERSION=${{ env.PYTHON_VERSION }}
lerobot/.github/workflows/test.yml ADDED
@@ -0,0 +1,150 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright 2024 The HuggingFace Inc. team. All rights reserved.
2
+ #
3
+ # Licensed under the Apache License, Version 2.0 (the "License");
4
+ # you may not use this file except in compliance with the License.
5
+ # You may obtain a copy of the License at
6
+ #
7
+ # http://www.apache.org/licenses/LICENSE-2.0
8
+ #
9
+ # Unless required by applicable law or agreed to in writing, software
10
+ # distributed under the License is distributed on an "AS IS" BASIS,
11
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12
+ # See the License for the specific language governing permissions and
13
+ # limitations under the License.
14
+
15
+ name: Tests
16
+
17
+ on:
18
+ pull_request:
19
+ paths:
20
+ - "src/**"
21
+ - "tests/**"
22
+ - "examples/**"
23
+ - ".github/**"
24
+ - "pyproject.toml"
25
+ - ".pre-commit-config.yaml"
26
+ - "Makefile"
27
+ - ".cache/**"
28
+ push:
29
+ branches:
30
+ - main
31
+ paths:
32
+ - "src/**"
33
+ - "tests/**"
34
+ - "examples/**"
35
+ - ".github/**"
36
+ - "pyproject.toml"
37
+ - ".pre-commit-config.yaml"
38
+ - "Makefile"
39
+ - ".cache/**"
40
+
41
+ permissions: {}
42
+
43
+ env:
44
+ UV_VERSION: "0.6.0"
45
+
46
+ jobs:
47
+ pytest:
48
+ name: Pytest
49
+ runs-on: ubuntu-latest
50
+ env:
51
+ MUJOCO_GL: egl
52
+ steps:
53
+ - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
54
+ with:
55
+ lfs: true # Ensure LFS files are pulled
56
+ persist-credentials: false
57
+
58
+ - name: Install apt dependencies
59
+ # portaudio19-dev is needed to install pyaudio
60
+ run: |
61
+ sudo apt-get update && \
62
+ sudo apt-get install -y libegl1-mesa-dev ffmpeg portaudio19-dev
63
+
64
+ - name: Install uv and python
65
+ uses: astral-sh/setup-uv@d4b2f3b6ecc6e67c4457f6d3e41ec42d3d0fcb86 # v5.4.2
66
+ with:
67
+ enable-cache: true
68
+ version: ${{ env.UV_VERSION }}
69
+ python-version: "3.10"
70
+
71
+ - name: Install lerobot (all extras)
72
+ run: uv sync --all-extras
73
+
74
+ - name: Test with pytest
75
+ run: |
76
+ uv run pytest tests -v --cov=./src/lerobot --durations=0 \
77
+ -W ignore::DeprecationWarning:imageio_ffmpeg._utils:7 \
78
+ -W ignore::UserWarning:torch.utils.data.dataloader:558 \
79
+ -W ignore::UserWarning:gymnasium.utils.env_checker:247 \
80
+ && rm -rf tests/outputs outputs
81
+
82
+ pytest-minimal:
83
+ name: Pytest (minimal install)
84
+ runs-on: ubuntu-latest
85
+ env:
86
+ MUJOCO_GL: egl
87
+ steps:
88
+ - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
89
+ with:
90
+ lfs: true # Ensure LFS files are pulled
91
+ persist-credentials: false
92
+
93
+ - name: Install apt dependencies
94
+ run: sudo apt-get update && sudo apt-get install -y ffmpeg
95
+
96
+ - name: Install uv and python
97
+ uses: astral-sh/setup-uv@d4b2f3b6ecc6e67c4457f6d3e41ec42d3d0fcb86 # v5.4.2
98
+ with:
99
+ enable-cache: true
100
+ version: ${{ env.UV_VERSION }}
101
+ python-version: "3.10"
102
+
103
+ - name: Install lerobot
104
+ run: uv sync --extra "test"
105
+
106
+ - name: Test with pytest
107
+ run: |
108
+ uv run pytest tests -v --cov=./src/lerobot --durations=0 \
109
+ -W ignore::DeprecationWarning:imageio_ffmpeg._utils:7 \
110
+ -W ignore::UserWarning:torch.utils.data.dataloader:558 \
111
+ -W ignore::UserWarning:gymnasium.utils.env_checker:247 \
112
+ && rm -rf tests/outputs outputs
113
+
114
+ end-to-end:
115
+ name: End-to-end
116
+ runs-on: ubuntu-latest
117
+ env:
118
+ MUJOCO_GL: egl
119
+ steps:
120
+ - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
121
+ with:
122
+ lfs: true # Ensure LFS files are pulled
123
+ persist-credentials: false
124
+
125
+ - name: Install apt dependencies
126
+ # portaudio19-dev is needed to install pyaudio
127
+ run: |
128
+ sudo apt-get update && \
129
+ sudo apt-get install -y libegl1-mesa-dev ffmpeg portaudio19-dev
130
+
131
+ - name: Install uv and python
132
+ uses: astral-sh/setup-uv@d4b2f3b6ecc6e67c4457f6d3e41ec42d3d0fcb86 # v5.4.2
133
+ with:
134
+ enable-cache: true
135
+ version: ${{ env.UV_VERSION }}
136
+ python-version: "3.10"
137
+
138
+ - name: Install lerobot (all extras)
139
+ run: |
140
+ uv venv
141
+ uv sync --all-extras
142
+
143
+ - name: venv
144
+ run: |
145
+ echo "PYTHON_PATH=${{ github.workspace }}/.venv/bin/python" >> $GITHUB_ENV
146
+
147
+ - name: Test end-to-end
148
+ run: |
149
+ make test-end-to-end \
150
+ && rm -rf outputs
lerobot/.github/workflows/trufflehog.yml ADDED
@@ -0,0 +1,35 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright 2024 The HuggingFace Inc. team. All rights reserved.
2
+ #
3
+ # Licensed under the Apache License, Version 2.0 (the "License");
4
+ # you may not use this file except in compliance with the License.
5
+ # You may obtain a copy of the License at
6
+ #
7
+ # http://www.apache.org/licenses/LICENSE-2.0
8
+ #
9
+ # Unless required by applicable law or agreed to in writing, software
10
+ # distributed under the License is distributed on an "AS IS" BASIS,
11
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12
+ # See the License for the specific language governing permissions and
13
+ # limitations under the License.
14
+
15
+ on:
16
+ push:
17
+
18
+ name: Secret Leaks
19
+
20
+ permissions: {}
21
+
22
+ jobs:
23
+ trufflehog:
24
+ runs-on: ubuntu-latest
25
+ steps:
26
+ - name: Checkout code
27
+ uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
28
+ with:
29
+ fetch-depth: 0
30
+ persist-credentials: false
31
+
32
+ - name: Secret Scanning
33
+ uses: trufflesecurity/trufflehog@90694bf9af66e7536abc5824e7a87246dbf933cb # v3.88.35
34
+ with:
35
+ extra_args: --only-verified
lerobot/.github/workflows/upload_pr_documentation.yml ADDED
@@ -0,0 +1,16 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ name: Upload PR Documentation
2
+
3
+ on: # zizmor: ignore[dangerous-triggers] We follow the same pattern as in Transformers
4
+ workflow_run:
5
+ workflows: [ "Build PR Documentation" ]
6
+ types:
7
+ - completed
8
+
9
+ jobs:
10
+ build: # zizmor: ignore[excessive-permissions] We follow the same pattern as in Transformers
11
+ uses: huggingface/doc-builder/.github/workflows/upload_pr_documentation.yml@main
12
+ with:
13
+ package_name: lerobot
14
+ secrets:
15
+ hf_token: ${{ secrets.HF_DOC_BUILD_PUSH }}
16
+ comment_bot_token: ${{ secrets.COMMENT_BOT_TOKEN }}
lerobot/.gitignore ADDED
@@ -0,0 +1,175 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright 2024 The HuggingFace Inc. team. All rights reserved.
2
+ #
3
+ # Licensed under the Apache License, Version 2.0 (the "License");
4
+ # you may not use this file except in compliance with the License.
5
+ # You may obtain a copy of the License at
6
+ #
7
+ # http://www.apache.org/licenses/LICENSE-2.0
8
+ #
9
+ # Unless required by applicable law or agreed to in writing, software
10
+ # distributed under the License is distributed on an "AS IS" BASIS,
11
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12
+ # See the License for the specific language governing permissions and
13
+ # limitations under the License.
14
+
15
+ # Dev scripts
16
+ .dev
17
+
18
+ # Logging
19
+ logs
20
+ tmp
21
+ wandb
22
+
23
+ # Data
24
+ data
25
+ outputs
26
+
27
+ # Apple
28
+ .DS_Store
29
+
30
+ # VS Code
31
+ .vscode
32
+ .devcontainer
33
+
34
+ # HPC
35
+ nautilus/*.yaml
36
+ *.key
37
+
38
+ # Slurm
39
+ sbatch*.sh
40
+
41
+ # Byte-compiled / optimized / DLL files
42
+ __pycache__/
43
+ *.py[cod]
44
+ *$py.class
45
+
46
+ # C extensions
47
+ *.so
48
+
49
+ # Distribution / packaging
50
+ .Python
51
+ build/
52
+ develop-eggs/
53
+ dist/
54
+ downloads/
55
+ eggs/
56
+ .eggs/
57
+ lib/
58
+ lib64/
59
+ parts/
60
+ sdist/
61
+ var/
62
+ wheels/
63
+ pip-wheel-metadata/
64
+ share/python-wheels/
65
+ *.egg-info/
66
+ .installed.cfg
67
+ *.egg
68
+ MANIFEST
69
+
70
+ # uv/poetry lock files
71
+ poetry.lock
72
+ uv.lock
73
+
74
+ # PyInstaller
75
+ # Usually these files are written by a python script from a template
76
+ # before PyInstaller builds the exe, so as to inject date/other infos into it.
77
+ *.manifest
78
+ *.spec
79
+
80
+ # Installer logs
81
+ pip-log.txt
82
+ pip-delete-this-directory.txt
83
+
84
+ # Unit test / coverage reports
85
+ !tests/artifacts
86
+ htmlcov/
87
+ .tox/
88
+ .nox/
89
+ .coverage
90
+ .coverage.*
91
+ nosetests.xml
92
+ coverage.xml
93
+ *.cover
94
+ *.py,cover
95
+ .hypothesis/
96
+ .pytest_cache/
97
+
98
+ # Ignore .cache
99
+ .cache/*
100
+
101
+ # Translations
102
+ *.mo
103
+ *.pot
104
+
105
+ # Django stuff:
106
+ *.log
107
+ local_settings.py
108
+ db.sqlite3
109
+ db.sqlite3-journal
110
+
111
+ # Flask stuff:
112
+ instance/
113
+ .webassets-cache
114
+
115
+ # Scrapy stuff:
116
+ .scrapy
117
+
118
+ # Sphinx documentation
119
+ docs/_build/
120
+
121
+ # PyBuilder
122
+ .pybuilder/
123
+ target/
124
+
125
+ # Jupyter Notebook
126
+ .ipynb_checkpoints
127
+
128
+ # IPython
129
+ profile_default/
130
+ ipython_config.py
131
+
132
+ # pyenv
133
+ .python-version
134
+
135
+ # PEP 582; used by e.g. github.com/David-OConnor/pyflow
136
+ __pypackages__/
137
+
138
+ # Celery stuff
139
+ celerybeat-schedule
140
+ celerybeat.pid
141
+
142
+ # SageMath parsed files
143
+ *.sage.py
144
+
145
+ # Environments
146
+ .env
147
+ .venv
148
+ env/
149
+ venv/
150
+ env.bak/
151
+ venv.bak/
152
+
153
+ # Spyder project settings
154
+ .spyderproject
155
+ .spyproject
156
+
157
+ # Rope project settings
158
+ .ropeproject
159
+
160
+ # mkdocs documentation
161
+ /site
162
+
163
+ # mypy
164
+ .mypy_cache/
165
+ .dmypy.json
166
+ dmypy.json
167
+
168
+ # Pyre type checker
169
+ .pyre/
170
+
171
+ # pytype static type analyzer
172
+ .pytype/
173
+
174
+ # Cython debug symbols
175
+ cython_debug/
lerobot/.pre-commit-config.yaml ADDED
@@ -0,0 +1,74 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright 2024 The HuggingFace Inc. team. All rights reserved.
2
+ #
3
+ # Licensed under the Apache License, Version 2.0 (the "License");
4
+ # you may not use this file except in compliance with the License.
5
+ # You may obtain a copy of the License at
6
+ #
7
+ # http://www.apache.org/licenses/LICENSE-2.0
8
+ #
9
+ # Unless required by applicable law or agreed to in writing, software
10
+ # distributed under the License is distributed on an "AS IS" BASIS,
11
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12
+ # See the License for the specific language governing permissions and
13
+ # limitations under the License.
14
+
15
+ exclude: "tests/artifacts/.*\\.safetensors$"
16
+ default_language_version:
17
+ python: python3.10
18
+ repos:
19
+ ##### Meta #####
20
+ - repo: meta
21
+ hooks:
22
+ - id: check-useless-excludes
23
+ - id: check-hooks-apply
24
+
25
+
26
+ ##### Style / Misc. #####
27
+ - repo: https://github.com/pre-commit/pre-commit-hooks
28
+ rev: v5.0.0
29
+ hooks:
30
+ - id: check-added-large-files
31
+ - id: debug-statements
32
+ - id: check-merge-conflict
33
+ - id: check-case-conflict
34
+ - id: check-yaml
35
+ - id: check-toml
36
+ - id: end-of-file-fixer
37
+ - id: trailing-whitespace
38
+
39
+ - repo: https://github.com/adhtruong/mirrors-typos
40
+ rev: v1.33.1
41
+ hooks:
42
+ - id: typos
43
+ args: [--force-exclude]
44
+
45
+ - repo: https://github.com/asottile/pyupgrade
46
+ rev: v3.20.0
47
+ hooks:
48
+ - id: pyupgrade
49
+
50
+ - repo: https://github.com/astral-sh/ruff-pre-commit
51
+ rev: v0.11.13
52
+ hooks:
53
+ - id: ruff
54
+ args: [--fix]
55
+ - id: ruff-format
56
+
57
+
58
+ ##### Security #####
59
+ - repo: https://github.com/gitleaks/gitleaks
60
+ rev: v8.27.2
61
+ hooks:
62
+ - id: gitleaks
63
+
64
+ - repo: https://github.com/woodruffw/zizmor-pre-commit
65
+ rev: v1.9.0
66
+ hooks:
67
+ - id: zizmor
68
+
69
+ - repo: https://github.com/PyCQA/bandit
70
+ rev: 1.8.3
71
+ hooks:
72
+ - id: bandit
73
+ args: ["-c", "pyproject.toml"]
74
+ additional_dependencies: ["bandit[toml]"]
lerobot/CODE_OF_CONDUCT.md ADDED
@@ -0,0 +1,133 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+ # Contributor Covenant Code of Conduct
3
+
4
+ ## Our Pledge
5
+
6
+ We as members, contributors, and leaders pledge to make participation in our
7
+ community a harassment-free experience for everyone, regardless of age, body
8
+ size, visible or invisible disability, ethnicity, sex characteristics, gender
9
+ identity and expression, level of experience, education, socio-economic status,
10
+ nationality, personal appearance, race, caste, color, religion, or sexual
11
+ identity and orientation.
12
+
13
+ We pledge to act and interact in ways that contribute to an open, welcoming,
14
+ diverse, inclusive, and healthy community.
15
+
16
+ ## Our Standards
17
+
18
+ Examples of behavior that contributes to a positive environment for our
19
+ community include:
20
+
21
+ * Demonstrating empathy and kindness toward other people
22
+ * Being respectful of differing opinions, viewpoints, and experiences
23
+ * Giving and gracefully accepting constructive feedback
24
+ * Accepting responsibility and apologizing to those affected by our mistakes,
25
+ and learning from the experience
26
+ * Focusing on what is best not just for us as individuals, but for the overall
27
+ community
28
+
29
+ Examples of unacceptable behavior include:
30
+
31
+ * The use of sexualized language or imagery, and sexual attention or advances of
32
+ any kind
33
+ * Trolling, insulting or derogatory comments, and personal or political attacks
34
+ * Public or private harassment
35
+ * Publishing others' private information, such as a physical or email address,
36
+ without their explicit permission
37
+ * Other conduct which could reasonably be considered inappropriate in a
38
+ professional setting
39
+
40
+ ## Enforcement Responsibilities
41
+
42
+ Community leaders are responsible for clarifying and enforcing our standards of
43
+ acceptable behavior and will take appropriate and fair corrective action in
44
+ response to any behavior that they deem inappropriate, threatening, offensive,
45
+ or harmful.
46
+
47
+ Community leaders have the right and responsibility to remove, edit, or reject
48
+ comments, commits, code, wiki edits, issues, and other contributions that are
49
+ not aligned to this Code of Conduct, and will communicate reasons for moderation
50
+ decisions when appropriate.
51
+
52
+ ## Scope
53
+
54
+ This Code of Conduct applies within all community spaces, and also applies when
55
+ an individual is officially representing the community in public spaces.
56
+ Examples of representing our community include using an official email address,
57
+ posting via an official social media account, or acting as an appointed
58
+ representative at an online or offline event.
59
+
60
+ ## Enforcement
61
+
62
+ Instances of abusive, harassing, or otherwise unacceptable behavior may be
63
+ reported to the community leaders responsible for enforcement at
64
+ [feedback@huggingface.co](mailto:feedback@huggingface.co).
65
+ All complaints will be reviewed and investigated promptly and fairly.
66
+
67
+ All community leaders are obligated to respect the privacy and security of the
68
+ reporter of any incident.
69
+
70
+ ## Enforcement Guidelines
71
+
72
+ Community leaders will follow these Community Impact Guidelines in determining
73
+ the consequences for any action they deem in violation of this Code of Conduct:
74
+
75
+ ### 1. Correction
76
+
77
+ **Community Impact**: Use of inappropriate language or other behavior deemed
78
+ unprofessional or unwelcome in the community.
79
+
80
+ **Consequence**: A private, written warning from community leaders, providing
81
+ clarity around the nature of the violation and an explanation of why the
82
+ behavior was inappropriate. A public apology may be requested.
83
+
84
+ ### 2. Warning
85
+
86
+ **Community Impact**: A violation through a single incident or series of
87
+ actions.
88
+
89
+ **Consequence**: A warning with consequences for continued behavior. No
90
+ interaction with the people involved, including unsolicited interaction with
91
+ those enforcing the Code of Conduct, for a specified period of time. This
92
+ includes avoiding interactions in community spaces as well as external channels
93
+ like social media. Violating these terms may lead to a temporary or permanent
94
+ ban.
95
+
96
+ ### 3. Temporary Ban
97
+
98
+ **Community Impact**: A serious violation of community standards, including
99
+ sustained inappropriate behavior.
100
+
101
+ **Consequence**: A temporary ban from any sort of interaction or public
102
+ communication with the community for a specified period of time. No public or
103
+ private interaction with the people involved, including unsolicited interaction
104
+ with those enforcing the Code of Conduct, is allowed during this period.
105
+ Violating these terms may lead to a permanent ban.
106
+
107
+ ### 4. Permanent Ban
108
+
109
+ **Community Impact**: Demonstrating a pattern of violation of community
110
+ standards, including sustained inappropriate behavior, harassment of an
111
+ individual, or aggression toward or disparagement of classes of individuals.
112
+
113
+ **Consequence**: A permanent ban from any sort of public interaction within the
114
+ community.
115
+
116
+ ## Attribution
117
+
118
+ This Code of Conduct is adapted from the [Contributor Covenant][homepage],
119
+ version 2.1, available at
120
+ [https://www.contributor-covenant.org/version/2/1/code_of_conduct.html][v2.1].
121
+
122
+ Community Impact Guidelines were inspired by
123
+ [Mozilla's code of conduct enforcement ladder][Mozilla CoC].
124
+
125
+ For answers to common questions about this code of conduct, see the FAQ at
126
+ [https://www.contributor-covenant.org/faq][FAQ]. Translations are available at
127
+ [https://www.contributor-covenant.org/translations][translations].
128
+
129
+ [homepage]: https://www.contributor-covenant.org
130
+ [v2.1]: https://www.contributor-covenant.org/version/2/1/code_of_conduct.html
131
+ [Mozilla CoC]: https://github.com/mozilla/diversity
132
+ [FAQ]: https://www.contributor-covenant.org/faq
133
+ [translations]: https://www.contributor-covenant.org/translations
lerobot/CONTRIBUTING.md ADDED
@@ -0,0 +1,305 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # How to contribute to 🤗 LeRobot?
2
+
3
+ Everyone is welcome to contribute, and we value everybody's contribution. Code
4
+ is thus not the only way to help the community. Answering questions, helping
5
+ others, reaching out and improving the documentations are immensely valuable to
6
+ the community.
7
+
8
+ It also helps us if you spread the word: reference the library from blog posts
9
+ on the awesome projects it made possible, shout out on Twitter when it has
10
+ helped you, or simply ⭐️ the repo to say "thank you".
11
+
12
+ Whichever way you choose to contribute, please be mindful to respect our
13
+ [code of conduct](https://github.com/huggingface/lerobot/blob/main/CODE_OF_CONDUCT.md).
14
+
15
+ ## You can contribute in so many ways!
16
+
17
+ Some of the ways you can contribute to 🤗 LeRobot:
18
+ * Fixing outstanding issues with the existing code.
19
+ * Implementing new models, datasets or simulation environments.
20
+ * Contributing to the examples or to the documentation.
21
+ * Submitting issues related to bugs or desired new features.
22
+
23
+ Following the guides below, feel free to open issues and PRs and to coordinate your efforts with the community on our [Discord Channel](https://discord.gg/VjFz58wn3R). For specific inquiries, reach out to [Remi Cadene](mailto:remi.cadene@huggingface.co).
24
+
25
+ If you are not sure how to contribute or want to know the next features we working on, look on this project page: [LeRobot TODO](https://github.com/orgs/huggingface/projects/46)
26
+
27
+ ## Submitting a new issue or feature request
28
+
29
+ Do your best to follow these guidelines when submitting an issue or a feature
30
+ request. It will make it easier for us to come back to you quickly and with good
31
+ feedback.
32
+
33
+ ### Did you find a bug?
34
+
35
+ The 🤗 LeRobot library is robust and reliable thanks to the users who notify us of
36
+ the problems they encounter. So thank you for reporting an issue.
37
+
38
+ First, we would really appreciate it if you could **make sure the bug was not
39
+ already reported** (use the search bar on Github under Issues).
40
+
41
+ Did not find it? :( So we can act quickly on it, please follow these steps:
42
+
43
+ * Include your **OS type and version**, the versions of **Python** and **PyTorch**.
44
+ * A short, self-contained, code snippet that allows us to reproduce the bug in
45
+ less than 30s.
46
+ * The full traceback if an exception is raised.
47
+ * Attach any other additional information, like screenshots, you think may help.
48
+
49
+ ### Do you want a new feature?
50
+
51
+ A good feature request addresses the following points:
52
+
53
+ 1. Motivation first:
54
+ * Is it related to a problem/frustration with the library? If so, please explain
55
+ why. Providing a code snippet that demonstrates the problem is best.
56
+ * Is it related to something you would need for a project? We'd love to hear
57
+ about it!
58
+ * Is it something you worked on and think could benefit the community?
59
+ Awesome! Tell us what problem it solved for you.
60
+ 2. Write a *paragraph* describing the feature.
61
+ 3. Provide a **code snippet** that demonstrates its future use.
62
+ 4. In case this is related to a paper, please attach a link.
63
+ 5. Attach any additional information (drawings, screenshots, etc.) you think may help.
64
+
65
+ If your issue is well written we're already 80% of the way there by the time you
66
+ post it.
67
+
68
+ ## Adding new policies, datasets or environments
69
+
70
+ Look at our implementations for [datasets](./src/lerobot/datasets/), [policies](./src/lerobot/policies/),
71
+ environments ([aloha](https://github.com/huggingface/gym-aloha),
72
+ [xarm](https://github.com/huggingface/gym-xarm),
73
+ [pusht](https://github.com/huggingface/gym-pusht))
74
+ and follow the same api design.
75
+
76
+ When implementing a new dataset loadable with LeRobotDataset follow these steps:
77
+ - Update `available_datasets_per_env` in `lerobot/__init__.py`
78
+
79
+ When implementing a new environment (e.g. `gym_aloha`), follow these steps:
80
+ - Update `available_tasks_per_env` and `available_datasets_per_env` in `lerobot/__init__.py`
81
+
82
+ When implementing a new policy class (e.g. `DiffusionPolicy`) follow these steps:
83
+ - Update `available_policies` and `available_policies_per_env`, in `lerobot/__init__.py`
84
+ - Set the required `name` class attribute.
85
+ - Update variables in `tests/test_available.py` by importing your new Policy class
86
+
87
+ ## Submitting a pull request (PR)
88
+
89
+ Before writing code, we strongly advise you to search through the existing PRs or
90
+ issues to make sure that nobody is already working on the same thing. If you are
91
+ unsure, it is always a good idea to open an issue to get some feedback.
92
+
93
+ You will need basic `git` proficiency to be able to contribute to
94
+ 🤗 LeRobot. `git` is not the easiest tool to use but it has the greatest
95
+ manual. Type `git --help` in a shell and enjoy. If you prefer books, [Pro
96
+ Git](https://git-scm.com/book/en/v2) is a very good reference.
97
+
98
+ Follow these steps to start contributing:
99
+
100
+ 1. Fork the [repository](https://github.com/huggingface/lerobot) by
101
+ clicking on the 'Fork' button on the repository's page. This creates a copy of the code
102
+ under your GitHub user account.
103
+
104
+ 2. Clone your fork to your local disk, and add the base repository as a remote. The following command
105
+ assumes you have your public SSH key uploaded to GitHub. See the following guide for more
106
+ [information](https://docs.github.com/en/repositories/creating-and-managing-repositories/cloning-a-repository).
107
+
108
+ ```bash
109
+ git clone git@github.com:<your Github handle>/lerobot.git
110
+ cd lerobot
111
+ git remote add upstream https://github.com/huggingface/lerobot.git
112
+ ```
113
+
114
+ 3. Create a new branch to hold your development changes, and do this for every new PR you work on.
115
+
116
+ Start by synchronizing your `main` branch with the `upstream/main` branch (more details in the [GitHub Docs](https://docs.github.com/en/github/collaborating-with-issues-and-pull-requests/syncing-a-fork)):
117
+
118
+ ```bash
119
+ git checkout main
120
+ git fetch upstream
121
+ git rebase upstream/main
122
+ ```
123
+
124
+ Once your `main` branch is synchronized, create a new branch from it:
125
+
126
+ ```bash
127
+ git checkout -b a-descriptive-name-for-my-changes
128
+ ```
129
+
130
+ 🚨 **Do not** work on the `main` branch.
131
+
132
+ 4. for development, we advise to use a tool like `poetry` or `uv` instead of just `pip` to easily track our dependencies.
133
+ Follow the instructions to [install poetry](https://python-poetry.org/docs/#installation) (use a version >=2.1.0) or to [install uv](https://docs.astral.sh/uv/getting-started/installation/#installation-methods) if you don't have one of them already.
134
+
135
+ Set up a development environment with conda or miniconda:
136
+ ```bash
137
+ conda create -y -n lerobot-dev python=3.10 && conda activate lerobot-dev
138
+ ```
139
+
140
+ If you're using `uv`, it can manage python versions so you can instead do:
141
+ ```bash
142
+ uv venv --python 3.10 && source .venv/bin/activate
143
+ ```
144
+
145
+ To develop on 🤗 LeRobot, you will at least need to install the `dev` and `test` extras dependencies along with the core library:
146
+
147
+ using `poetry`
148
+ ```bash
149
+ poetry sync --extras "dev test"
150
+ ```
151
+
152
+ using `uv`
153
+ ```bash
154
+ uv sync --extra dev --extra test
155
+ ```
156
+
157
+ You can also install the project with all its dependencies (including environments):
158
+
159
+ using `poetry`
160
+ ```bash
161
+ poetry sync --all-extras
162
+ ```
163
+
164
+ using `uv`
165
+ ```bash
166
+ uv sync --all-extras
167
+ ```
168
+
169
+ > **Note:** If you don't install simulation environments with `--all-extras`, the tests that require them will be skipped when running the pytest suite locally. However, they *will* be tested in the CI. In general, we advise you to install everything and test locally before pushing.
170
+
171
+ Whichever command you chose to install the project (e.g. `poetry sync --all-extras`), you should run it again when pulling code with an updated version of `pyproject.toml` and `poetry.lock` in order to synchronize your virtual environment with the new dependencies.
172
+
173
+ The equivalent of `pip install some-package`, would just be:
174
+
175
+ using `poetry`
176
+ ```bash
177
+ poetry add some-package
178
+ ```
179
+
180
+ using `uv`
181
+ ```bash
182
+ uv add some-package
183
+ ```
184
+
185
+ When making changes to the poetry sections of the `pyproject.toml`, you should run the following command to lock dependencies.
186
+ using `poetry`
187
+ ```bash
188
+ poetry lock
189
+ ```
190
+
191
+ using `uv`
192
+ ```bash
193
+ uv lock
194
+ ```
195
+
196
+
197
+ 5. Develop the features on your branch.
198
+
199
+ As you work on the features, you should make sure that the test suite
200
+ passes. You should run the tests impacted by your changes like this (see
201
+ below an explanation regarding the environment variable):
202
+
203
+ ```bash
204
+ pytest tests/<TEST_TO_RUN>.py
205
+ ```
206
+
207
+ 6. Follow our style.
208
+
209
+ `lerobot` relies on `ruff` to format its source code
210
+ consistently. Set up [`pre-commit`](https://pre-commit.com/) to run these checks
211
+ automatically as Git commit hooks.
212
+
213
+ Install `pre-commit` hooks:
214
+ ```bash
215
+ pre-commit install
216
+ ```
217
+
218
+ You can run these hooks whenever you need on staged files with:
219
+ ```bash
220
+ pre-commit
221
+ ```
222
+
223
+ Once you're happy with your changes, add changed files using `git add` and
224
+ make a commit with `git commit` to record your changes locally:
225
+
226
+ ```bash
227
+ git add modified_file.py
228
+ git commit
229
+ ```
230
+
231
+ Note, if you already committed some changes that have a wrong formatting, you can use:
232
+ ```bash
233
+ pre-commit run --all-files
234
+ ```
235
+
236
+ Please write [good commit messages](https://chris.beams.io/posts/git-commit/).
237
+
238
+ It is a good idea to sync your copy of the code with the original
239
+ repository regularly. This way you can quickly account for changes:
240
+
241
+ ```bash
242
+ git fetch upstream
243
+ git rebase upstream/main
244
+ ```
245
+
246
+ Push the changes to your account using:
247
+
248
+ ```bash
249
+ git push -u origin a-descriptive-name-for-my-changes
250
+ ```
251
+
252
+ 6. Once you are satisfied (**and the checklist below is happy too**), go to the
253
+ webpage of your fork on GitHub. Click on 'Pull request' to send your changes
254
+ to the project maintainers for review.
255
+
256
+ 7. It's ok if maintainers ask you for changes. It happens to core contributors
257
+ too! So everyone can see the changes in the Pull request, work in your local
258
+ branch and push the changes to your fork. They will automatically appear in
259
+ the pull request.
260
+
261
+
262
+ ### Checklist
263
+
264
+ 1. The title of your pull request should be a summary of its contribution;
265
+ 2. If your pull request addresses an issue, please mention the issue number in
266
+ the pull request description to make sure they are linked (and people
267
+ consulting the issue know you are working on it);
268
+ 3. To indicate a work in progress please prefix the title with `[WIP]`, or preferably mark
269
+ the PR as a draft PR. These are useful to avoid duplicated work, and to differentiate
270
+ it from PRs ready to be merged;
271
+ 4. Make sure existing tests pass;
272
+
273
+ ### Tests
274
+
275
+ An extensive test suite is included to test the library behavior and several examples. Library tests can be found in the [tests folder](https://github.com/huggingface/lerobot/tree/main/tests).
276
+
277
+ Install [git lfs](https://git-lfs.com/) to retrieve test artifacts (if you don't have it already).
278
+
279
+ On Mac:
280
+ ```bash
281
+ brew install git-lfs
282
+ git lfs install
283
+ ```
284
+
285
+ On Ubuntu:
286
+ ```bash
287
+ sudo apt-get install git-lfs
288
+ git lfs install
289
+ ```
290
+
291
+ Pull artifacts if they're not in [tests/artifacts](tests/artifacts)
292
+ ```bash
293
+ git lfs pull
294
+ ```
295
+
296
+ We use `pytest` in order to run the tests. From the root of the
297
+ repository, here's how to run tests with `pytest` for the library:
298
+
299
+ ```bash
300
+ python -m pytest -sv ./tests
301
+ ```
302
+
303
+
304
+ You can specify a smaller set of tests in order to test only the feature
305
+ you're working on.
lerobot/LICENSE ADDED
@@ -0,0 +1,507 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ Copyright 2024 The Hugging Face team. All rights reserved.
2
+
3
+ Apache License
4
+ Version 2.0, January 2004
5
+ http://www.apache.org/licenses/
6
+
7
+ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
8
+
9
+ 1. Definitions.
10
+
11
+ "License" shall mean the terms and conditions for use, reproduction,
12
+ and distribution as defined by Sections 1 through 9 of this document.
13
+
14
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lerobot/MANIFEST.in ADDED
@@ -0,0 +1,2 @@
 
 
 
1
+ include src/lerobot/templates/lerobot_modelcard_template.md
2
+ include src/lerobot/datasets/card_template.md
lerobot/Makefile ADDED
@@ -0,0 +1,180 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright 2024 The HuggingFace Inc. team. All rights reserved.
2
+ #
3
+ # Licensed under the Apache License, Version 2.0 (the "License");
4
+ # you may not use this file except in compliance with the License.
5
+ # You may obtain a copy of the License at
6
+ #
7
+ # http://www.apache.org/licenses/LICENSE-2.0
8
+ #
9
+ # Unless required by applicable law or agreed to in writing, software
10
+ # distributed under the License is distributed on an "AS IS" BASIS,
11
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12
+ # See the License for the specific language governing permissions and
13
+ # limitations under the License.
14
+
15
+ .PHONY: tests
16
+
17
+ PYTHON_PATH := $(shell which python)
18
+
19
+ # If uv is installed and a virtual environment exists, use it
20
+ UV_CHECK := $(shell command -v uv)
21
+ ifneq ($(UV_CHECK),)
22
+ PYTHON_PATH := $(shell .venv/bin/python)
23
+ endif
24
+
25
+ export PATH := $(dir $(PYTHON_PATH)):$(PATH)
26
+
27
+ DEVICE ?= cpu
28
+
29
+ build-cpu:
30
+ docker build -t lerobot:latest -f docker/lerobot-cpu/Dockerfile .
31
+
32
+ build-gpu:
33
+ docker build -t lerobot:latest -f docker/lerobot-gpu/Dockerfile .
34
+
35
+ test-end-to-end:
36
+ ${MAKE} DEVICE=$(DEVICE) test-act-ete-train
37
+ ${MAKE} DEVICE=$(DEVICE) test-act-ete-train-resume
38
+ ${MAKE} DEVICE=$(DEVICE) test-act-ete-eval
39
+ ${MAKE} DEVICE=$(DEVICE) test-diffusion-ete-train
40
+ ${MAKE} DEVICE=$(DEVICE) test-diffusion-ete-eval
41
+ ${MAKE} DEVICE=$(DEVICE) test-tdmpc-ete-train
42
+ ${MAKE} DEVICE=$(DEVICE) test-tdmpc-ete-eval
43
+ ${MAKE} DEVICE=$(DEVICE) test-smolvla-ete-train
44
+ ${MAKE} DEVICE=$(DEVICE) test-smolvla-ete-eval
45
+
46
+ test-act-ete-train:
47
+ python -m lerobot.scripts.train \
48
+ --policy.type=act \
49
+ --policy.dim_model=64 \
50
+ --policy.n_action_steps=20 \
51
+ --policy.chunk_size=20 \
52
+ --policy.device=$(DEVICE) \
53
+ --policy.push_to_hub=false \
54
+ --env.type=aloha \
55
+ --env.episode_length=5 \
56
+ --dataset.repo_id=lerobot/aloha_sim_transfer_cube_human \
57
+ --dataset.image_transforms.enable=true \
58
+ --dataset.episodes="[0]" \
59
+ --batch_size=2 \
60
+ --steps=4 \
61
+ --eval_freq=2 \
62
+ --eval.n_episodes=1 \
63
+ --eval.batch_size=1 \
64
+ --save_freq=2 \
65
+ --save_checkpoint=true \
66
+ --log_freq=1 \
67
+ --wandb.enable=false \
68
+ --output_dir=tests/outputs/act/
69
+
70
+ test-act-ete-train-resume:
71
+ python -m lerobot.scripts.train \
72
+ --config_path=tests/outputs/act/checkpoints/000002/pretrained_model/train_config.json \
73
+ --resume=true
74
+
75
+ test-act-ete-eval:
76
+ python -m lerobot.scripts.eval \
77
+ --policy.path=tests/outputs/act/checkpoints/000004/pretrained_model \
78
+ --policy.device=$(DEVICE) \
79
+ --env.type=aloha \
80
+ --env.episode_length=5 \
81
+ --eval.n_episodes=1 \
82
+ --eval.batch_size=1
83
+
84
+ test-diffusion-ete-train:
85
+ python -m lerobot.scripts.train \
86
+ --policy.type=diffusion \
87
+ --policy.down_dims='[64,128,256]' \
88
+ --policy.diffusion_step_embed_dim=32 \
89
+ --policy.num_inference_steps=10 \
90
+ --policy.device=$(DEVICE) \
91
+ --policy.push_to_hub=false \
92
+ --env.type=pusht \
93
+ --env.episode_length=5 \
94
+ --dataset.repo_id=lerobot/pusht \
95
+ --dataset.image_transforms.enable=true \
96
+ --dataset.episodes="[0]" \
97
+ --batch_size=2 \
98
+ --steps=2 \
99
+ --eval_freq=2 \
100
+ --eval.n_episodes=1 \
101
+ --eval.batch_size=1 \
102
+ --save_checkpoint=true \
103
+ --save_freq=2 \
104
+ --log_freq=1 \
105
+ --wandb.enable=false \
106
+ --output_dir=tests/outputs/diffusion/
107
+
108
+ test-diffusion-ete-eval:
109
+ python -m lerobot.scripts.eval \
110
+ --policy.path=tests/outputs/diffusion/checkpoints/000002/pretrained_model \
111
+ --policy.device=$(DEVICE) \
112
+ --env.type=pusht \
113
+ --env.episode_length=5 \
114
+ --eval.n_episodes=1 \
115
+ --eval.batch_size=1
116
+
117
+ test-tdmpc-ete-train:
118
+ python -m lerobot.scripts.train \
119
+ --policy.type=tdmpc \
120
+ --policy.device=$(DEVICE) \
121
+ --policy.push_to_hub=false \
122
+ --env.type=xarm \
123
+ --env.task=XarmLift-v0 \
124
+ --env.episode_length=5 \
125
+ --dataset.repo_id=lerobot/xarm_lift_medium \
126
+ --dataset.image_transforms.enable=true \
127
+ --dataset.episodes="[0]" \
128
+ --batch_size=2 \
129
+ --steps=2 \
130
+ --eval_freq=2 \
131
+ --eval.n_episodes=1 \
132
+ --eval.batch_size=1 \
133
+ --save_checkpoint=true \
134
+ --save_freq=2 \
135
+ --log_freq=1 \
136
+ --wandb.enable=false \
137
+ --output_dir=tests/outputs/tdmpc/
138
+
139
+ test-tdmpc-ete-eval:
140
+ python -m lerobot.scripts.eval \
141
+ --policy.path=tests/outputs/tdmpc/checkpoints/000002/pretrained_model \
142
+ --policy.device=$(DEVICE) \
143
+ --env.type=xarm \
144
+ --env.episode_length=5 \
145
+ --env.task=XarmLift-v0 \
146
+ --eval.n_episodes=1 \
147
+ --eval.batch_size=1
148
+
149
+
150
+ test-smolvla-ete-train:
151
+ python -m lerobot.scripts.train \
152
+ --policy.type=smolvla \
153
+ --policy.n_action_steps=20 \
154
+ --policy.chunk_size=20 \
155
+ --policy.device=$(DEVICE) \
156
+ --policy.push_to_hub=false \
157
+ --env.type=aloha \
158
+ --env.episode_length=5 \
159
+ --dataset.repo_id=lerobot/aloha_sim_transfer_cube_human \
160
+ --dataset.image_transforms.enable=true \
161
+ --dataset.episodes="[0]" \
162
+ --batch_size=2 \
163
+ --steps=4 \
164
+ --eval_freq=2 \
165
+ --eval.n_episodes=1 \
166
+ --eval.batch_size=1 \
167
+ --save_freq=2 \
168
+ --save_checkpoint=true \
169
+ --log_freq=1 \
170
+ --wandb.enable=false \
171
+ --output_dir=tests/outputs/smolvla/
172
+
173
+ test-smolvla-ete-eval:
174
+ python -m lerobot.scripts.eval \
175
+ --policy.path=tests/outputs/smolvla/checkpoints/000004/pretrained_model \
176
+ --policy.device=$(DEVICE) \
177
+ --env.type=aloha \
178
+ --env.episode_length=5 \
179
+ --eval.n_episodes=1 \
180
+ --eval.batch_size=1
lerobot/README.md ADDED
@@ -0,0 +1,412 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ <p align="center">
2
+ <picture>
3
+ <source media="(prefers-color-scheme: dark)" srcset="media/lerobot-logo-thumbnail.png">
4
+ <source media="(prefers-color-scheme: light)" srcset="media/lerobot-logo-thumbnail.png">
5
+ <img alt="LeRobot, Hugging Face Robotics Library" src="media/lerobot-logo-thumbnail.png" style="max-width: 100%;">
6
+ </picture>
7
+ <br/>
8
+ <br/>
9
+ </p>
10
+
11
+ <div align="center">
12
+
13
+ [![Tests](https://github.com/huggingface/lerobot/actions/workflows/nightly-tests.yml/badge.svg?branch=main)](https://github.com/huggingface/lerobot/actions/workflows/nightly-tests.yml?query=branch%3Amain)
14
+ [![Coverage](https://codecov.io/gh/huggingface/lerobot/branch/main/graph/badge.svg?token=TODO)](https://codecov.io/gh/huggingface/lerobot)
15
+ [![Python versions](https://img.shields.io/pypi/pyversions/lerobot)](https://www.python.org/downloads/)
16
+ [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://github.com/huggingface/lerobot/blob/main/LICENSE)
17
+ [![Status](https://img.shields.io/pypi/status/lerobot)](https://pypi.org/project/lerobot/)
18
+ [![Version](https://img.shields.io/pypi/v/lerobot)](https://pypi.org/project/lerobot/)
19
+ [![Examples](https://img.shields.io/badge/Examples-green.svg)](https://github.com/huggingface/lerobot/tree/main/examples)
20
+ [![Contributor Covenant](https://img.shields.io/badge/Contributor%20Covenant-v2.1%20adopted-ff69b4.svg)](https://github.com/huggingface/lerobot/blob/main/CODE_OF_CONDUCT.md)
21
+ [![Discord](https://dcbadge.vercel.app/api/server/C5P34WJ68S?style=flat)](https://discord.gg/s3KuuzsPFb)
22
+
23
+ </div>
24
+
25
+ <h2 align="center">
26
+ <p><a href="https://huggingface.co/docs/lerobot/so101">
27
+ Build Your Own SO-101 Robot!</a></p>
28
+ </h2>
29
+
30
+ <div align="center">
31
+ <div style="display: flex; gap: 1rem; justify-content: center; align-items: center;" >
32
+ <img
33
+ src="media/so101/so101.webp?raw=true"
34
+ alt="SO-101 follower arm"
35
+ title="SO-101 follower arm"
36
+ style="width: 40%;"
37
+ />
38
+ <img
39
+ src="media/so101/so101-leader.webp?raw=true"
40
+ alt="SO-101 leader arm"
41
+ title="SO-101 leader arm"
42
+ style="width: 40%;"
43
+ />
44
+ </div>
45
+
46
+
47
+ <p><strong>Meet the updated SO100, the SO-101 – Just €114 per arm!</strong></p>
48
+ <p>Train it in minutes with a few simple moves on your laptop.</p>
49
+ <p>Then sit back and watch your creation act autonomously! 🤯</p>
50
+
51
+ <p><a href="https://huggingface.co/docs/lerobot/so101">
52
+ See the full SO-101 tutorial here.</a></p>
53
+
54
+ <p>Want to take it to the next level? Make your SO-101 mobile by building LeKiwi!</p>
55
+ <p>Check out the <a href="https://huggingface.co/docs/lerobot/lekiwi">LeKiwi tutorial</a> and bring your robot to life on wheels.</p>
56
+
57
+ <img src="media/lekiwi/kiwi.webp?raw=true" alt="LeKiwi mobile robot" title="LeKiwi mobile robot" width="50%">
58
+ </div>
59
+
60
+ <br/>
61
+
62
+ <h3 align="center">
63
+ <p>LeRobot: State-of-the-art AI for real-world robotics</p>
64
+ </h3>
65
+
66
+ ---
67
+
68
+ 🤗 LeRobot aims to provide models, datasets, and tools for real-world robotics in PyTorch. The goal is to lower the barrier to entry to robotics so that everyone can contribute and benefit from sharing datasets and pretrained models.
69
+
70
+ 🤗 LeRobot contains state-of-the-art approaches that have been shown to transfer to the real-world with a focus on imitation learning and reinforcement learning.
71
+
72
+ 🤗 LeRobot already provides a set of pretrained models, datasets with human collected demonstrations, and simulation environments to get started without assembling a robot. In the coming weeks, the plan is to add more and more support for real-world robotics on the most affordable and capable robots out there.
73
+
74
+ 🤗 LeRobot hosts pretrained models and datasets on this Hugging Face community page: [huggingface.co/lerobot](https://huggingface.co/lerobot)
75
+
76
+ #### Examples of pretrained models on simulation environments
77
+
78
+ <table>
79
+ <tr>
80
+ <td><img src="media/gym/aloha_act.gif" width="100%" alt="ACT policy on ALOHA env"/></td>
81
+ <td><img src="media/gym/simxarm_tdmpc.gif" width="100%" alt="TDMPC policy on SimXArm env"/></td>
82
+ <td><img src="media/gym/pusht_diffusion.gif" width="100%" alt="Diffusion policy on PushT env"/></td>
83
+ </tr>
84
+ <tr>
85
+ <td align="center">ACT policy on ALOHA env</td>
86
+ <td align="center">TDMPC policy on SimXArm env</td>
87
+ <td align="center">Diffusion policy on PushT env</td>
88
+ </tr>
89
+ </table>
90
+
91
+ ### Acknowledgment
92
+
93
+ - The LeRobot team 🤗 for building SmolVLA [Paper](https://arxiv.org/abs/2506.01844), [Blog](https://huggingface.co/blog/smolvla).
94
+ - Thanks to Tony Zhao, Zipeng Fu and colleagues for open sourcing ACT policy, ALOHA environments and datasets. Ours are adapted from [ALOHA](https://tonyzhaozh.github.io/aloha) and [Mobile ALOHA](https://mobile-aloha.github.io).
95
+ - Thanks to Cheng Chi, Zhenjia Xu and colleagues for open sourcing Diffusion policy, Pusht environment and datasets, as well as UMI datasets. Ours are adapted from [Diffusion Policy](https://diffusion-policy.cs.columbia.edu) and [UMI Gripper](https://umi-gripper.github.io).
96
+ - Thanks to Nicklas Hansen, Yunhai Feng and colleagues for open sourcing TDMPC policy, Simxarm environments and datasets. Ours are adapted from [TDMPC](https://github.com/nicklashansen/tdmpc) and [FOWM](https://www.yunhaifeng.com/FOWM).
97
+ - Thanks to Antonio Loquercio and Ashish Kumar for their early support.
98
+ - Thanks to [Seungjae (Jay) Lee](https://sjlee.cc/), [Mahi Shafiullah](https://mahis.life/) and colleagues for open sourcing [VQ-BeT](https://sjlee.cc/vq-bet/) policy and helping us adapt the codebase to our repository. The policy is adapted from [VQ-BeT repo](https://github.com/jayLEE0301/vq_bet_official).
99
+
100
+
101
+ ## Installation
102
+
103
+ Download our source code:
104
+ ```bash
105
+ git clone https://github.com/huggingface/lerobot.git
106
+ cd lerobot
107
+ ```
108
+
109
+ Create a virtual environment with Python 3.10 and activate it, e.g. with [`miniconda`](https://docs.anaconda.com/free/miniconda/index.html):
110
+ ```bash
111
+ conda create -y -n lerobot python=3.10
112
+ conda activate lerobot
113
+ ```
114
+
115
+ When using `miniconda`, install `ffmpeg` in your environment:
116
+ ```bash
117
+ conda install ffmpeg -c conda-forge
118
+ ```
119
+
120
+ > **NOTE:** This usually installs `ffmpeg 7.X` for your platform compiled with the `libsvtav1` encoder. If `libsvtav1` is not supported (check supported encoders with `ffmpeg -encoders`), you can:
121
+ > - _[On any platform]_ Explicitly install `ffmpeg 7.X` using:
122
+ > ```bash
123
+ > conda install ffmpeg=7.1.1 -c conda-forge
124
+ > ```
125
+ > - _[On Linux only]_ Install [ffmpeg build dependencies](https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu#GettheDependencies) and [compile ffmpeg from source with libsvtav1](https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu#libsvtav1), and make sure you use the corresponding ffmpeg binary to your install with `which ffmpeg`.
126
+
127
+ Install 🤗 LeRobot:
128
+ ```bash
129
+ pip install -e .
130
+ ```
131
+
132
+ > **NOTE:** If you encounter build errors, you may need to install additional dependencies (`cmake`, `build-essential`, and `ffmpeg libs`). On Linux, run:
133
+ `sudo apt-get install cmake build-essential python3-dev pkg-config libavformat-dev libavcodec-dev libavdevice-dev libavutil-dev libswscale-dev libswresample-dev libavfilter-dev`. For other systems, see: [Compiling PyAV](https://pyav.org/docs/develop/overview/installation.html#bring-your-own-ffmpeg)
134
+
135
+ For simulations, 🤗 LeRobot comes with gymnasium environments that can be installed as extras:
136
+ - [aloha](https://github.com/huggingface/gym-aloha)
137
+ - [xarm](https://github.com/huggingface/gym-xarm)
138
+ - [pusht](https://github.com/huggingface/gym-pusht)
139
+
140
+ For instance, to install 🤗 LeRobot with aloha and pusht, use:
141
+ ```bash
142
+ pip install -e ".[aloha, pusht]"
143
+ ```
144
+
145
+ To use [Weights and Biases](https://docs.wandb.ai/quickstart) for experiment tracking, log in with
146
+ ```bash
147
+ wandb login
148
+ ```
149
+
150
+ (note: you will also need to enable WandB in the configuration. See below.)
151
+
152
+ ### Visualize datasets
153
+
154
+ Check out [example 1](./examples/1_load_lerobot_dataset.py) that illustrates how to use our dataset class which automatically downloads data from the Hugging Face hub.
155
+
156
+ You can also locally visualize episodes from a dataset on the hub by executing our script from the command line:
157
+ ```bash
158
+ python -m lerobot.scripts.visualize_dataset \
159
+ --repo-id lerobot/pusht \
160
+ --episode-index 0
161
+ ```
162
+
163
+ or from a dataset in a local folder with the `root` option and the `--local-files-only` (in the following case the dataset will be searched for in `./my_local_data_dir/lerobot/pusht`)
164
+ ```bash
165
+ python -m lerobot.scripts.visualize_dataset \
166
+ --repo-id lerobot/pusht \
167
+ --root ./my_local_data_dir \
168
+ --local-files-only 1 \
169
+ --episode-index 0
170
+ ```
171
+
172
+
173
+ It will open `rerun.io` and display the camera streams, robot states and actions, like this:
174
+
175
+ https://github-production-user-asset-6210df.s3.amazonaws.com/4681518/328035972-fd46b787-b532-47e2-bb6f-fd536a55a7ed.mov?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAVCODYLSA53PQK4ZA%2F20240505%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20240505T172924Z&X-Amz-Expires=300&X-Amz-Signature=d680b26c532eeaf80740f08af3320d22ad0b8a4e4da1bcc4f33142c15b509eda&X-Amz-SignedHeaders=host&actor_id=24889239&key_id=0&repo_id=748713144
176
+
177
+
178
+ Our script can also visualize datasets stored on a distant server. See `python -m lerobot.scripts.visualize_dataset --help` for more instructions.
179
+
180
+ ### The `LeRobotDataset` format
181
+
182
+ A dataset in `LeRobotDataset` format is very simple to use. It can be loaded from a repository on the Hugging Face hub or a local folder simply with e.g. `dataset = LeRobotDataset("lerobot/aloha_static_coffee")` and can be indexed into like any Hugging Face and PyTorch dataset. For instance `dataset[0]` will retrieve a single temporal frame from the dataset containing observation(s) and an action as PyTorch tensors ready to be fed to a model.
183
+
184
+ A specificity of `LeRobotDataset` is that, rather than retrieving a single frame by its index, we can retrieve several frames based on their temporal relationship with the indexed frame, by setting `delta_timestamps` to a list of relative times with respect to the indexed frame. For example, with `delta_timestamps = {"observation.image": [-1, -0.5, -0.2, 0]}` one can retrieve, for a given index, 4 frames: 3 "previous" frames 1 second, 0.5 seconds, and 0.2 seconds before the indexed frame, and the indexed frame itself (corresponding to the 0 entry). See example [1_load_lerobot_dataset.py](examples/1_load_lerobot_dataset.py) for more details on `delta_timestamps`.
185
+
186
+ Under the hood, the `LeRobotDataset` format makes use of several ways to serialize data which can be useful to understand if you plan to work more closely with this format. We tried to make a flexible yet simple dataset format that would cover most type of features and specificities present in reinforcement learning and robotics, in simulation and in real-world, with a focus on cameras and robot states but easily extended to other types of sensory inputs as long as they can be represented by a tensor.
187
+
188
+ Here are the important details and internal structure organization of a typical `LeRobotDataset` instantiated with `dataset = LeRobotDataset("lerobot/aloha_static_coffee")`. The exact features will change from dataset to dataset but not the main aspects:
189
+
190
+ ```
191
+ dataset attributes:
192
+ ├ hf_dataset: a Hugging Face dataset (backed by Arrow/parquet). Typical features example:
193
+ │ ├ observation.images.cam_high (VideoFrame):
194
+ │ │ VideoFrame = {'path': path to a mp4 video, 'timestamp' (float32): timestamp in the video}
195
+ │ ├ observation.state (list of float32): position of an arm joints (for instance)
196
+ │ ... (more observations)
197
+ │ ├ action (list of float32): goal position of an arm joints (for instance)
198
+ │ ├ episode_index (int64): index of the episode for this sample
199
+ │ ├ frame_index (int64): index of the frame for this sample in the episode ; starts at 0 for each episode
200
+ │ ├ timestamp (float32): timestamp in the episode
201
+ │ ├ next.done (bool): indicates the end of an episode ; True for the last frame in each episode
202
+ │ └ index (int64): general index in the whole dataset
203
+ ├ episode_data_index: contains 2 tensors with the start and end indices of each episode
204
+ │ ├ from (1D int64 tensor): first frame index for each episode — shape (num episodes,) starts with 0
205
+ │ └ to: (1D int64 tensor): last frame index for each episode — shape (num episodes,)
206
+ ├ stats: a dictionary of statistics (max, mean, min, std) for each feature in the dataset, for instance
207
+ │ ├ observation.images.cam_high: {'max': tensor with same number of dimensions (e.g. `(c, 1, 1)` for images, `(c,)` for states), etc.}
208
+ │ ...
209
+ ├ info: a dictionary of metadata on the dataset
210
+ │ ├ codebase_version (str): this is to keep track of the codebase version the dataset was created with
211
+ │ ├ fps (float): frame per second the dataset is recorded/synchronized to
212
+ │ ├ video (bool): indicates if frames are encoded in mp4 video files to save space or stored as png files
213
+ │ └ encoding (dict): if video, this documents the main options that were used with ffmpeg to encode the videos
214
+ ├ videos_dir (Path): where the mp4 videos or png images are stored/accessed
215
+ └ camera_keys (list of string): the keys to access camera features in the item returned by the dataset (e.g. `["observation.images.cam_high", ...]`)
216
+ ```
217
+
218
+ A `LeRobotDataset` is serialised using several widespread file formats for each of its parts, namely:
219
+ - hf_dataset stored using Hugging Face datasets library serialization to parquet
220
+ - videos are stored in mp4 format to save space
221
+ - metadata are stored in plain json/jsonl files
222
+
223
+ Dataset can be uploaded/downloaded from the HuggingFace hub seamlessly. To work on a local dataset, you can specify its location with the `root` argument if it's not in the default `~/.cache/huggingface/lerobot` location.
224
+
225
+ ### Evaluate a pretrained policy
226
+
227
+ Check out [example 2](./examples/2_evaluate_pretrained_policy.py) that illustrates how to download a pretrained policy from Hugging Face hub, and run an evaluation on its corresponding environment.
228
+
229
+ We also provide a more capable script to parallelize the evaluation over multiple environments during the same rollout. Here is an example with a pretrained model hosted on [lerobot/diffusion_pusht](https://huggingface.co/lerobot/diffusion_pusht):
230
+ ```bash
231
+ python -m lerobot.scripts.eval \
232
+ --policy.path=lerobot/diffusion_pusht \
233
+ --env.type=pusht \
234
+ --eval.batch_size=10 \
235
+ --eval.n_episodes=10 \
236
+ --policy.use_amp=false \
237
+ --policy.device=cuda
238
+ ```
239
+
240
+ Note: After training your own policy, you can re-evaluate the checkpoints with:
241
+
242
+ ```bash
243
+ python -m lerobot.scripts.eval --policy.path={OUTPUT_DIR}/checkpoints/last/pretrained_model
244
+ ```
245
+
246
+ See `python -m lerobot.scripts.eval --help` for more instructions.
247
+
248
+ ### Train your own policy
249
+
250
+ Check out [example 3](./examples/3_train_policy.py) that illustrates how to train a model using our core library in python, and [example 4](./examples/4_train_policy_with_script.md) that shows how to use our training script from command line.
251
+
252
+ To use wandb for logging training and evaluation curves, make sure you've run `wandb login` as a one-time setup step. Then, when running the training command above, enable WandB in the configuration by adding `--wandb.enable=true`.
253
+
254
+ A link to the wandb logs for the run will also show up in yellow in your terminal. Here is an example of what they look like in your browser. Please also check [here](./examples/4_train_policy_with_script.md#typical-logs-and-metrics) for the explanation of some commonly used metrics in logs.
255
+
256
+ ![](media/wandb.png)
257
+
258
+ Note: For efficiency, during training every checkpoint is evaluated on a low number of episodes. You may use `--eval.n_episodes=500` to evaluate on more episodes than the default. Or, after training, you may want to re-evaluate your best checkpoints on more episodes or change the evaluation settings. See `python -m lerobot.scripts.eval --help` for more instructions.
259
+
260
+ #### Reproduce state-of-the-art (SOTA)
261
+
262
+ We provide some pretrained policies on our [hub page](https://huggingface.co/lerobot) that can achieve state-of-the-art performances.
263
+ You can reproduce their training by loading the config from their run. Simply running:
264
+ ```bash
265
+ python -m lerobot.scripts.train --config_path=lerobot/diffusion_pusht
266
+ ```
267
+ reproduces SOTA results for Diffusion Policy on the PushT task.
268
+
269
+ ## Contribute
270
+
271
+ If you would like to contribute to 🤗 LeRobot, please check out our [contribution guide](https://github.com/huggingface/lerobot/blob/main/CONTRIBUTING.md).
272
+
273
+ <!-- ### Add a new dataset
274
+
275
+ To add a dataset to the hub, you need to login using a write-access token, which can be generated from the [Hugging Face settings](https://huggingface.co/settings/tokens):
276
+ ```bash
277
+ huggingface-cli login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential
278
+ ```
279
+
280
+ Then point to your raw dataset folder (e.g. `data/aloha_static_pingpong_test_raw`), and push your dataset to the hub with:
281
+ ```bash
282
+ python lerobot/scripts/push_dataset_to_hub.py \
283
+ --raw-dir data/aloha_static_pingpong_test_raw \
284
+ --out-dir data \
285
+ --repo-id lerobot/aloha_static_pingpong_test \
286
+ --raw-format aloha_hdf5
287
+ ```
288
+
289
+ See `python lerobot/scripts/push_dataset_to_hub.py --help` for more instructions.
290
+
291
+ If your dataset format is not supported, implement your own in `lerobot/datasets/push_dataset_to_hub/${raw_format}_format.py` by copying examples like [pusht_zarr](https://github.com/huggingface/lerobot/blob/main/lerobot/datasets/push_dataset_to_hub/pusht_zarr_format.py), [umi_zarr](https://github.com/huggingface/lerobot/blob/main/lerobot/datasets/push_dataset_to_hub/umi_zarr_format.py), [aloha_hdf5](https://github.com/huggingface/lerobot/blob/main/lerobot/datasets/push_dataset_to_hub/aloha_hdf5_format.py), or [xarm_pkl](https://github.com/huggingface/lerobot/blob/main/lerobot/datasets/push_dataset_to_hub/xarm_pkl_format.py). -->
292
+
293
+
294
+ ### Add a pretrained policy
295
+
296
+ Once you have trained a policy you may upload it to the Hugging Face hub using a hub id that looks like `${hf_user}/${repo_name}` (e.g. [lerobot/diffusion_pusht](https://huggingface.co/lerobot/diffusion_pusht)).
297
+
298
+ You first need to find the checkpoint folder located inside your experiment directory (e.g. `outputs/train/2024-05-05/20-21-12_aloha_act_default/checkpoints/002500`). Within that there is a `pretrained_model` directory which should contain:
299
+ - `config.json`: A serialized version of the policy configuration (following the policy's dataclass config).
300
+ - `model.safetensors`: A set of `torch.nn.Module` parameters, saved in [Hugging Face Safetensors](https://huggingface.co/docs/safetensors/index) format.
301
+ - `train_config.json`: A consolidated configuration containing all parameters used for training. The policy configuration should match `config.json` exactly. This is useful for anyone who wants to evaluate your policy or for reproducibility.
302
+
303
+ To upload these to the hub, run the following:
304
+ ```bash
305
+ huggingface-cli upload ${hf_user}/${repo_name} path/to/pretrained_model
306
+ ```
307
+
308
+ See [eval.py](https://github.com/huggingface/lerobot/blob/main/lerobot/scripts/eval.py) for an example of how other people may use your policy.
309
+
310
+
311
+ ### Improve your code with profiling
312
+
313
+ An example of a code snippet to profile the evaluation of a policy:
314
+ ```python
315
+ from torch.profiler import profile, record_function, ProfilerActivity
316
+
317
+ def trace_handler(prof):
318
+ prof.export_chrome_trace(f"tmp/trace_schedule_{prof.step_num}.json")
319
+
320
+ with profile(
321
+ activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA],
322
+ schedule=torch.profiler.schedule(
323
+ wait=2,
324
+ warmup=2,
325
+ active=3,
326
+ ),
327
+ on_trace_ready=trace_handler
328
+ ) as prof:
329
+ with record_function("eval_policy"):
330
+ for i in range(num_episodes):
331
+ prof.step()
332
+ # insert code to profile, potentially whole body of eval_policy function
333
+ ```
334
+
335
+ ## Citation
336
+
337
+ If you want, you can cite this work with:
338
+ ```bibtex
339
+ @misc{cadene2024lerobot,
340
+ author = {Cadene, Remi and Alibert, Simon and Soare, Alexander and Gallouedec, Quentin and Zouitine, Adil and Palma, Steven and Kooijmans, Pepijn and Aractingi, Michel and Shukor, Mustafa and Aubakirova, Dana and Russi, Martino and Capuano, Francesco and Pascale, Caroline and Choghari, Jade and Moss, Jess and Wolf, Thomas},
341
+ title = {LeRobot: State-of-the-art Machine Learning for Real-World Robotics in Pytorch},
342
+ howpublished = "\url{https://github.com/huggingface/lerobot}",
343
+ year = {2024}
344
+ }
345
+ ```
346
+
347
+ Additionally, if you are using any of the particular policy architecture, pretrained models, or datasets, it is recommended to cite the original authors of the work as they appear below:
348
+ - [SmolVLA](https://arxiv.org/abs/2506.01844)
349
+ ```bibtex
350
+ @article{shukor2025smolvla,
351
+ title={SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics},
352
+ author={Shukor, Mustafa and Aubakirova, Dana and Capuano, Francesco and Kooijmans, Pepijn and Palma, Steven and Zouitine, Adil and Aractingi, Michel and Pascal, Caroline and Russi, Martino and Marafioti, Andres and Alibert, Simon and Cord, Matthieu and Wolf, Thomas and Cadene, Remi},
353
+ journal={arXiv preprint arXiv:2506.01844},
354
+ year={2025}
355
+ }
356
+ ```
357
+
358
+ - [Diffusion Policy](https://diffusion-policy.cs.columbia.edu)
359
+ ```bibtex
360
+ @article{chi2024diffusionpolicy,
361
+ author = {Cheng Chi and Zhenjia Xu and Siyuan Feng and Eric Cousineau and Yilun Du and Benjamin Burchfiel and Russ Tedrake and Shuran Song},
362
+ title ={Diffusion Policy: Visuomotor Policy Learning via Action Diffusion},
363
+ journal = {The International Journal of Robotics Research},
364
+ year = {2024},
365
+ }
366
+ ```
367
+ - [ACT or ALOHA](https://tonyzhaozh.github.io/aloha)
368
+ ```bibtex
369
+ @article{zhao2023learning,
370
+ title={Learning fine-grained bimanual manipulation with low-cost hardware},
371
+ author={Zhao, Tony Z and Kumar, Vikash and Levine, Sergey and Finn, Chelsea},
372
+ journal={arXiv preprint arXiv:2304.13705},
373
+ year={2023}
374
+ }
375
+ ```
376
+
377
+ - [TDMPC](https://www.nicklashansen.com/td-mpc/)
378
+
379
+ ```bibtex
380
+ @inproceedings{Hansen2022tdmpc,
381
+ title={Temporal Difference Learning for Model Predictive Control},
382
+ author={Nicklas Hansen and Xiaolong Wang and Hao Su},
383
+ booktitle={ICML},
384
+ year={2022}
385
+ }
386
+ ```
387
+
388
+ - [VQ-BeT](https://sjlee.cc/vq-bet/)
389
+ ```bibtex
390
+ @article{lee2024behavior,
391
+ title={Behavior generation with latent actions},
392
+ author={Lee, Seungjae and Wang, Yibin and Etukuru, Haritheja and Kim, H Jin and Shafiullah, Nur Muhammad Mahi and Pinto, Lerrel},
393
+ journal={arXiv preprint arXiv:2403.03181},
394
+ year={2024}
395
+ }
396
+ ```
397
+
398
+
399
+ - [HIL-SERL](https://hil-serl.github.io/)
400
+ ```bibtex
401
+ @Article{luo2024hilserl,
402
+ title={Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning},
403
+ author={Jianlan Luo and Charles Xu and Jeffrey Wu and Sergey Levine},
404
+ year={2024},
405
+ eprint={2410.21845},
406
+ archivePrefix={arXiv},
407
+ primaryClass={cs.RO}
408
+ }
409
+ ```
410
+ ## Star History
411
+
412
+ [![Star History Chart](https://api.star-history.com/svg?repos=huggingface/lerobot&type=Timeline)](https://star-history.com/#huggingface/lerobot&Timeline)
lerobot/benchmarks/video/README.md ADDED
@@ -0,0 +1,271 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Video benchmark
2
+
3
+
4
+ ## Questions
5
+ What is the optimal trade-off between:
6
+ - maximizing loading time with random access,
7
+ - minimizing memory space on disk,
8
+ - maximizing success rate of policies,
9
+ - compatibility across devices/platforms for decoding videos (e.g. video players, web browsers).
10
+
11
+ How to encode videos?
12
+ - Which video codec (`-vcodec`) to use? h264, h265, AV1?
13
+ - What pixel format to use (`-pix_fmt`)? `yuv444p` or `yuv420p`?
14
+ - How much compression (`-crf`)? No compression with `0`, intermediate compression with `25` or extreme with `50+`?
15
+ - Which frequency to chose for key frames (`-g`)? A key frame every `10` frames?
16
+
17
+ How to decode videos?
18
+ - Which `decoder`? `torchvision`, `torchaudio`, `ffmpegio`, `decord`, or `nvc`?
19
+ - What scenarios to use for the requesting timestamps during benchmark? (`timestamps_mode`)
20
+
21
+
22
+ ## Variables
23
+ **Image content & size**
24
+ We don't expect the same optimal settings for a dataset of images from a simulation, or from real-world in an apartment, or in a factory, or outdoor, or with lots of moving objects in the scene, etc. Similarly, loading times might not vary linearly with the image size (resolution).
25
+ For these reasons, we run this benchmark on four representative datasets:
26
+ - `lerobot/pusht_image`: (96 x 96 pixels) simulation with simple geometric shapes, fixed camera.
27
+ - `aliberts/aloha_mobile_shrimp_image`: (480 x 640 pixels) real-world indoor, moving camera.
28
+ - `aliberts/paris_street`: (720 x 1280 pixels) real-world outdoor, moving camera.
29
+ - `aliberts/kitchen`: (1080 x 1920 pixels) real-world indoor, fixed camera.
30
+
31
+ Note: The datasets used for this benchmark need to be image datasets, not video datasets.
32
+
33
+ **Data augmentations**
34
+ We might revisit this benchmark and find better settings if we train our policies with various data augmentations to make them more robust (e.g. robust to color changes, compression, etc.).
35
+
36
+ ### Encoding parameters
37
+ | parameter | values |
38
+ |-------------|--------------------------------------------------------------|
39
+ | **vcodec** | `libx264`, `libx265`, `libsvtav1` |
40
+ | **pix_fmt** | `yuv444p`, `yuv420p` |
41
+ | **g** | `1`, `2`, `3`, `4`, `5`, `6`, `10`, `15`, `20`, `40`, `None` |
42
+ | **crf** | `0`, `5`, `10`, `15`, `20`, `25`, `30`, `40`, `50`, `None` |
43
+
44
+ Note that `crf` value might be interpreted differently by various video codecs. In other words, the same value used with one codec doesn't necessarily translate into the same compression level with another codec. In fact, the default value (`None`) isn't the same amongst the different video codecs. Importantly, it is also the case for many other ffmpeg arguments like `g` which specifies the frequency of the key frames.
45
+
46
+ For a comprehensive list and documentation of these parameters, see the ffmpeg documentation depending on the video codec used:
47
+ - h264: https://trac.ffmpeg.org/wiki/Encode/H.264
48
+ - h265: https://trac.ffmpeg.org/wiki/Encode/H.265
49
+ - AV1: https://trac.ffmpeg.org/wiki/Encode/AV1
50
+
51
+ ### Decoding parameters
52
+ **Decoder**
53
+ We tested two video decoding backends from torchvision:
54
+ - `pyav`
55
+ - `video_reader` (requires to build torchvision from source)
56
+
57
+ **Requested timestamps**
58
+ Given the way video decoding works, once a keyframe has been loaded, the decoding of subsequent frames is fast.
59
+ This of course is affected by the `-g` parameter during encoding, which specifies the frequency of the keyframes. Given our typical use cases in robotics policies which might request a few timestamps in different random places, we want to replicate these use cases with the following scenarios:
60
+ - `1_frame`: 1 frame,
61
+ - `2_frames`: 2 consecutive frames (e.g. `[t, t + 1 / fps]`),
62
+ - `6_frames`: 6 consecutive frames (e.g. `[t + i / fps for i in range(6)]`)
63
+
64
+ Note that this differs significantly from a typical use case like watching a movie, in which every frame is loaded sequentially from the beginning to the end and it's acceptable to have big values for `-g`.
65
+
66
+ Additionally, because some policies might request single timestamps that are a few frames apart, we also have the following scenario:
67
+ - `2_frames_4_space`: 2 frames with 4 consecutive frames of spacing in between (e.g `[t, t + 5 / fps]`),
68
+
69
+ However, due to how video decoding is implemented with `pyav`, we don't have access to an accurate seek so in practice this scenario is essentially the same as `6_frames` since all 6 frames between `t` and `t + 5 / fps` will be decoded.
70
+
71
+
72
+ ## Metrics
73
+ **Data compression ratio (lower is better)**
74
+ `video_images_size_ratio` is the ratio of the memory space on disk taken by the encoded video over the memory space taken by the original images. For instance, `video_images_size_ratio=25%` means that the video takes 4 times less memory space on disk compared to the original images.
75
+
76
+ **Loading time ratio (lower is better)**
77
+ `video_images_load_time_ratio` is the ratio of the time it takes to decode frames from the video at a given timestamps over the time it takes to load the exact same original images. Lower is better. For instance, `video_images_load_time_ratio=200%` means that decoding from video is 2 times slower than loading the original images.
78
+
79
+ **Average Mean Square Error (lower is better)**
80
+ `avg_mse` is the average mean square error between each decoded frame and its corresponding original image over all requested timestamps, and also divided by the number of pixels in the image to be comparable when switching to different image sizes.
81
+
82
+ **Average Peak Signal to Noise Ratio (higher is better)**
83
+ `avg_psnr` measures the ratio between the maximum possible power of a signal and the power of corrupting noise that affects the fidelity of its representation. Higher PSNR indicates better quality.
84
+
85
+ **Average Structural Similarity Index Measure (higher is better)**
86
+ `avg_ssim` evaluates the perceived quality of images by comparing luminance, contrast, and structure. SSIM values range from -1 to 1, where 1 indicates perfect similarity.
87
+
88
+ One aspect that can't be measured here with those metrics is the compatibility of the encoding across platforms, in particular on web browser, for visualization purposes.
89
+ h264, h265 and AV1 are all commonly used codecs and should not pose an issue. However, the chroma subsampling (`pix_fmt`) format might affect compatibility:
90
+ - `yuv420p` is more widely supported across various platforms, including web browsers.
91
+ - `yuv444p` offers higher color fidelity but might not be supported as broadly.
92
+
93
+
94
+ <!-- **Loss of a pretrained policy (higher is better)** (not available)
95
+ `loss_pretrained` is the result of evaluating with the selected encoding/decoding settings a policy pretrained on original images. It is easier to understand than `avg_l2_error`.
96
+
97
+ **Success rate after retraining (higher is better)** (not available)
98
+ `success_rate` is the result of training and evaluating a policy with the selected encoding/decoding settings. It is the most difficult metric to get but also the very best. -->
99
+
100
+
101
+ ## How the benchmark works
102
+ The benchmark evaluates both encoding and decoding of video frames on the first episode of each dataset.
103
+
104
+ **Encoding:** for each `vcodec` and `pix_fmt` pair, we use a default value for `g` and `crf` upon which we change a single value (either `g` or `crf`) to one of the specified values (we don't test every combination of those as this would be computationally too heavy).
105
+ This gives a unique set of encoding parameters which is used to encode the episode.
106
+
107
+ **Decoding:** Then, for each of those unique encodings, we iterate through every combination of the decoding parameters `backend` and `timestamps_mode`. For each of them, we record the metrics of a number of samples (given by `--num-samples`). This is parallelized for efficiency and the number of processes can be controlled with `--num-workers`. Ideally, it's best to have a `--num-samples` that is divisible by `--num-workers`.
108
+
109
+ Intermediate results saved for each `vcodec` and `pix_fmt` combination in csv tables.
110
+ These are then all concatenated to a single table ready for analysis.
111
+
112
+ ## Caveats
113
+ We tried to measure the most impactful parameters for both encoding and decoding. However, for computational reasons we can't test out every combination.
114
+
115
+ Additional encoding parameters exist that are not included in this benchmark. In particular:
116
+ - `-preset` which allows for selecting encoding presets. This represents a collection of options that will provide a certain encoding speed to compression ratio. By leaving this parameter unspecified, it is considered to be `medium` for libx264 and libx265 and `8` for libsvtav1.
117
+ - `-tune` which allows to optimize the encoding for certain aspects (e.g. film quality, fast decoding, etc.).
118
+
119
+ See the documentation mentioned above for more detailed info on these settings and for a more comprehensive list of other parameters.
120
+
121
+ Similarly on the decoding side, other decoders exist but are not implemented in our current benchmark. To name a few:
122
+ - `torchaudio`
123
+ - `ffmpegio`
124
+ - `decord`
125
+ - `nvc`
126
+
127
+ Note as well that since we are mostly interested in the performance at decoding time (also because encoding is done only once before uploading a dataset), we did not measure encoding times nor have any metrics regarding encoding.
128
+ However, besides the necessity to build ffmpeg from source, encoding did not pose any issue and it didn't take a significant amount of time during this benchmark.
129
+
130
+
131
+ ## Install
132
+ Building ffmpeg from source is required to include libx265 and libaom/libsvtav1 (av1) video codecs ([compilation guide](https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu)).
133
+
134
+ **Note:** While you still need to build torchvision with a conda-installed `ffmpeg<4.3` to use the `video_reader` decoder (as described in [#220](https://github.com/huggingface/lerobot/pull/220)), you also need another version which is custom-built with all the video codecs for encoding. For the script to then use that version, you can prepend the command above with `PATH="$HOME/bin:$PATH"`, which is where ffmpeg should be built.
135
+
136
+
137
+ ## Adding a video decoder
138
+ Right now, we're only benchmarking the two video decoder available with torchvision: `pyav` and `video_reader`.
139
+ You can easily add a new decoder to benchmark by adding it to this function in the script:
140
+ ```diff
141
+ def decode_video_frames(
142
+ video_path: str,
143
+ timestamps: list[float],
144
+ tolerance_s: float,
145
+ backend: str,
146
+ ) -> torch.Tensor:
147
+ if backend in ["pyav", "video_reader"]:
148
+ return decode_video_frames_torchvision(
149
+ video_path, timestamps, tolerance_s, backend
150
+ )
151
+ + elif backend == ["your_decoder"]:
152
+ + return your_decoder_function(
153
+ + video_path, timestamps, tolerance_s, backend
154
+ + )
155
+ else:
156
+ raise NotImplementedError(backend)
157
+ ```
158
+
159
+
160
+ ## Example
161
+ For a quick run, you can try these parameters:
162
+ ```bash
163
+ python benchmark/video/run_video_benchmark.py \
164
+ --output-dir outputs/video_benchmark \
165
+ --repo-ids \
166
+ lerobot/pusht_image \
167
+ aliberts/aloha_mobile_shrimp_image \
168
+ --vcodec libx264 libx265 \
169
+ --pix-fmt yuv444p yuv420p \
170
+ --g 2 20 None \
171
+ --crf 10 40 None \
172
+ --timestamps-modes 1_frame 2_frames \
173
+ --backends pyav video_reader \
174
+ --num-samples 5 \
175
+ --num-workers 5 \
176
+ --save-frames 0
177
+ ```
178
+
179
+
180
+ ## Results
181
+
182
+ ### Reproduce
183
+ We ran the benchmark with the following parameters:
184
+ ```bash
185
+ # h264 and h265 encodings
186
+ python benchmark/video/run_video_benchmark.py \
187
+ --output-dir outputs/video_benchmark \
188
+ --repo-ids \
189
+ lerobot/pusht_image \
190
+ aliberts/aloha_mobile_shrimp_image \
191
+ aliberts/paris_street \
192
+ aliberts/kitchen \
193
+ --vcodec libx264 libx265 \
194
+ --pix-fmt yuv444p yuv420p \
195
+ --g 1 2 3 4 5 6 10 15 20 40 None \
196
+ --crf 0 5 10 15 20 25 30 40 50 None \
197
+ --timestamps-modes 1_frame 2_frames 6_frames \
198
+ --backends pyav video_reader \
199
+ --num-samples 50 \
200
+ --num-workers 5 \
201
+ --save-frames 1
202
+
203
+ # av1 encoding (only compatible with yuv420p and pyav decoder)
204
+ python benchmark/video/run_video_benchmark.py \
205
+ --output-dir outputs/video_benchmark \
206
+ --repo-ids \
207
+ lerobot/pusht_image \
208
+ aliberts/aloha_mobile_shrimp_image \
209
+ aliberts/paris_street \
210
+ aliberts/kitchen \
211
+ --vcodec libsvtav1 \
212
+ --pix-fmt yuv420p \
213
+ --g 1 2 3 4 5 6 10 15 20 40 None \
214
+ --crf 0 5 10 15 20 25 30 40 50 None \
215
+ --timestamps-modes 1_frame 2_frames 6_frames \
216
+ --backends pyav \
217
+ --num-samples 50 \
218
+ --num-workers 5 \
219
+ --save-frames 1
220
+ ```
221
+
222
+ The full results are available [here](https://docs.google.com/spreadsheets/d/1OYJB43Qu8fC26k_OyoMFgGBBKfQRCi4BIuYitQnq3sw/edit?usp=sharing)
223
+
224
+
225
+ ### Parameters selected for LeRobotDataset
226
+ Considering these results, we chose what we think is the best set of encoding parameter:
227
+ - vcodec: `libsvtav1`
228
+ - pix-fmt: `yuv420p`
229
+ - g: `2`
230
+ - crf: `30`
231
+
232
+ Since we're using av1 encoding, we're choosing the `pyav` decoder as `video_reader` does not support it (and `pyav` doesn't require a custom build of `torchvision`).
233
+
234
+ ### Summary
235
+
236
+ These tables show the results for `g=2` and `crf=30`, using `timestamps-modes=6_frames` and `backend=pyav`
237
+
238
+ | video_images_size_ratio | vcodec | pix_fmt | | | |
239
+ |------------------------------------|------------|---------|-----------|-----------|-----------|
240
+ | | libx264 | | libx265 | | libsvtav1 |
241
+ | repo_id | yuv420p | yuv444p | yuv420p | yuv444p | yuv420p |
242
+ | lerobot/pusht_image | **16.97%** | 17.58% | 18.57% | 18.86% | 22.06% |
243
+ | aliberts/aloha_mobile_shrimp_image | 2.14% | 2.11% | 1.38% | **1.37%** | 5.59% |
244
+ | aliberts/paris_street | 2.12% | 2.13% | **1.54%** | **1.54%** | 4.43% |
245
+ | aliberts/kitchen | 1.40% | 1.39% | **1.00%** | **1.00%** | 2.52% |
246
+
247
+ | video_images_load_time_ratio | vcodec | pix_fmt | | | |
248
+ |------------------------------------|---------|---------|----------|---------|-----------|
249
+ | | libx264 | | libx265 | | libsvtav1 |
250
+ | repo_id | yuv420p | yuv444p | yuv420p | yuv444p | yuv420p |
251
+ | lerobot/pusht_image | 6.45 | 5.19 | **1.90** | 2.12 | 2.47 |
252
+ | aliberts/aloha_mobile_shrimp_image | 11.80 | 7.92 | 0.71 | 0.85 | **0.48** |
253
+ | aliberts/paris_street | 2.21 | 2.05 | 0.36 | 0.49 | **0.30** |
254
+ | aliberts/kitchen | 1.46 | 1.46 | 0.28 | 0.51 | **0.26** |
255
+
256
+ | | | vcodec | pix_fmt | | | |
257
+ |------------------------------------|----------|----------|--------------|----------|-----------|--------------|
258
+ | | | libx264 | | libx265 | | libsvtav1 |
259
+ | repo_id | metric | yuv420p | yuv444p | yuv420p | yuv444p | yuv420p |
260
+ | lerobot/pusht_image | avg_mse | 2.90E-04 | **2.03E-04** | 3.13E-04 | 2.29E-04 | 2.19E-04 |
261
+ | | avg_psnr | 35.44 | 37.07 | 35.49 | **37.30** | 37.20 |
262
+ | | avg_ssim | 98.28% | **98.85%** | 98.31% | 98.84% | 98.72% |
263
+ | aliberts/aloha_mobile_shrimp_image | avg_mse | 2.76E-04 | 2.59E-04 | 3.17E-04 | 3.06E-04 | **1.30E-04** |
264
+ | | avg_psnr | 35.91 | 36.21 | 35.88 | 36.09 | **40.17** |
265
+ | | avg_ssim | 95.19% | 95.18% | 95.00% | 95.05% | **97.73%** |
266
+ | aliberts/paris_street | avg_mse | 6.89E-04 | 6.70E-04 | 4.03E-03 | 4.02E-03 | **3.09E-04** |
267
+ | | avg_psnr | 33.48 | 33.68 | 32.05 | 32.15 | **35.40** |
268
+ | | avg_ssim | 93.76% | 93.75% | 89.46% | 89.46% | **95.46%** |
269
+ | aliberts/kitchen | avg_mse | 2.50E-04 | 2.24E-04 | 4.28E-04 | 4.18E-04 | **1.53E-04** |
270
+ | | avg_psnr | 36.73 | 37.33 | 36.56 | 36.75 | **39.12** |
271
+ | | avg_ssim | 95.47% | 95.58% | 95.52% | 95.53% | **96.82%** |
lerobot/benchmarks/video/capture_camera_feed.py ADDED
@@ -0,0 +1,102 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/usr/bin/env python
2
+
3
+ # Copyright 2024 The HuggingFace Inc. team. All rights reserved.
4
+ #
5
+ # Licensed under the Apache License, Version 2.0 (the "License");
6
+ # you may not use this file except in compliance with the License.
7
+ # You may obtain a copy of the License at
8
+ #
9
+ # http://www.apache.org/licenses/LICENSE-2.0
10
+ #
11
+ # Unless required by applicable law or agreed to in writing, software
12
+ # distributed under the License is distributed on an "AS IS" BASIS,
13
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14
+ # See the License for the specific language governing permissions and
15
+ # limitations under the License.
16
+ """Capture video feed from a camera as raw images."""
17
+
18
+ import argparse
19
+ import datetime as dt
20
+ import os
21
+ import time
22
+ from pathlib import Path
23
+
24
+ import cv2
25
+ import rerun as rr
26
+
27
+ # see https://rerun.io/docs/howto/visualization/limit-ram
28
+ RERUN_MEMORY_LIMIT = os.getenv("LEROBOT_RERUN_MEMORY_LIMIT", "5%")
29
+
30
+
31
+ def display_and_save_video_stream(output_dir: Path, fps: int, width: int, height: int, duration: int):
32
+ rr.init("lerobot_capture_camera_feed")
33
+ rr.spawn(memory_limit=RERUN_MEMORY_LIMIT)
34
+
35
+ now = dt.datetime.now()
36
+ capture_dir = output_dir / f"{now:%Y-%m-%d}" / f"{now:%H-%M-%S}"
37
+ if not capture_dir.exists():
38
+ capture_dir.mkdir(parents=True, exist_ok=True)
39
+
40
+ # Opens the default webcam
41
+ cap = cv2.VideoCapture(0)
42
+ if not cap.isOpened():
43
+ print("Error: Could not open video stream.")
44
+ return
45
+
46
+ cap.set(cv2.CAP_PROP_FPS, fps)
47
+ cap.set(cv2.CAP_PROP_FRAME_WIDTH, width)
48
+ cap.set(cv2.CAP_PROP_FRAME_HEIGHT, height)
49
+
50
+ frame_index = 0
51
+ start_time = time.time()
52
+ while time.time() - start_time < duration:
53
+ ret, frame = cap.read()
54
+
55
+ if not ret:
56
+ print("Error: Could not read frame.")
57
+ break
58
+ rr.log("video/stream", rr.Image(frame), static=True)
59
+ cv2.imwrite(str(capture_dir / f"frame_{frame_index:06d}.png"), frame)
60
+ frame_index += 1
61
+
62
+ # Release the capture
63
+ cap.release()
64
+
65
+ # TODO(Steven): Add a graceful shutdown via a close() method for the Viewer context, though not currently supported in the Rerun API.
66
+
67
+
68
+ if __name__ == "__main__":
69
+ parser = argparse.ArgumentParser()
70
+
71
+ parser.add_argument(
72
+ "--output-dir",
73
+ type=Path,
74
+ default=Path("outputs/cam_capture/"),
75
+ help="Directory where the capture images are written. A subfolder named with the current date & time will be created inside it for each capture.",
76
+ )
77
+ parser.add_argument(
78
+ "--fps",
79
+ type=int,
80
+ default=30,
81
+ help="Frames Per Second of the capture.",
82
+ )
83
+ parser.add_argument(
84
+ "--width",
85
+ type=int,
86
+ default=1280,
87
+ help="Width of the captured images.",
88
+ )
89
+ parser.add_argument(
90
+ "--height",
91
+ type=int,
92
+ default=720,
93
+ help="Height of the captured images.",
94
+ )
95
+ parser.add_argument(
96
+ "--duration",
97
+ type=int,
98
+ default=20,
99
+ help="Duration in seconds for which the video stream should be captured.",
100
+ )
101
+ args = parser.parse_args()
102
+ display_and_save_video_stream(**vars(args))
lerobot/benchmarks/video/run_video_benchmark.py ADDED
@@ -0,0 +1,490 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/usr/bin/env python
2
+
3
+ # Copyright 2024 The HuggingFace Inc. team. All rights reserved.
4
+ #
5
+ # Licensed under the Apache License, Version 2.0 (the "License");
6
+ # you may not use this file except in compliance with the License.
7
+ # You may obtain a copy of the License at
8
+ #
9
+ # http://www.apache.org/licenses/LICENSE-2.0
10
+ #
11
+ # Unless required by applicable law or agreed to in writing, software
12
+ # distributed under the License is distributed on an "AS IS" BASIS,
13
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14
+ # See the License for the specific language governing permissions and
15
+ # limitations under the License.
16
+ """Assess the performance of video decoding in various configurations.
17
+
18
+ This script will benchmark different video encoding and decoding parameters.
19
+ See the provided README.md or run `python benchmark/video/run_video_benchmark.py --help` for usage info.
20
+ """
21
+
22
+ import argparse
23
+ import datetime as dt
24
+ import random
25
+ import shutil
26
+ from collections import OrderedDict
27
+ from concurrent.futures import ThreadPoolExecutor, as_completed
28
+ from pathlib import Path
29
+
30
+ import einops
31
+ import numpy as np
32
+ import pandas as pd
33
+ import PIL
34
+ import torch
35
+ from skimage.metrics import mean_squared_error, peak_signal_noise_ratio, structural_similarity
36
+ from tqdm import tqdm
37
+
38
+ from lerobot.datasets.lerobot_dataset import LeRobotDataset
39
+ from lerobot.datasets.video_utils import (
40
+ decode_video_frames_torchvision,
41
+ encode_video_frames,
42
+ )
43
+ from lerobot.utils.benchmark import TimeBenchmark
44
+
45
+ BASE_ENCODING = OrderedDict(
46
+ [
47
+ ("vcodec", "libx264"),
48
+ ("pix_fmt", "yuv444p"),
49
+ ("g", 2),
50
+ ("crf", None),
51
+ # TODO(aliberts): Add fastdecode
52
+ # ("fastdecode", 0),
53
+ ]
54
+ )
55
+
56
+
57
+ # TODO(rcadene, aliberts): move to `utils.py` folder when we want to refactor
58
+ def parse_int_or_none(value) -> int | None:
59
+ if value.lower() == "none":
60
+ return None
61
+ try:
62
+ return int(value)
63
+ except ValueError as e:
64
+ raise argparse.ArgumentTypeError(f"Invalid int or None: {value}") from e
65
+
66
+
67
+ def check_datasets_formats(repo_ids: list) -> None:
68
+ for repo_id in repo_ids:
69
+ dataset = LeRobotDataset(repo_id)
70
+ if len(dataset.meta.video_keys) > 0:
71
+ raise ValueError(
72
+ f"Use only image dataset for running this benchmark. Video dataset provided: {repo_id}"
73
+ )
74
+
75
+
76
+ def get_directory_size(directory: Path) -> int:
77
+ total_size = 0
78
+ for item in directory.rglob("*"):
79
+ if item.is_file():
80
+ total_size += item.stat().st_size
81
+ return total_size
82
+
83
+
84
+ def load_original_frames(imgs_dir: Path, timestamps: list[float], fps: int) -> torch.Tensor:
85
+ frames = []
86
+ for ts in timestamps:
87
+ idx = int(ts * fps)
88
+ frame = PIL.Image.open(imgs_dir / f"frame_{idx:06d}.png")
89
+ frame = torch.from_numpy(np.array(frame))
90
+ frame = frame.type(torch.float32) / 255
91
+ frame = einops.rearrange(frame, "h w c -> c h w")
92
+ frames.append(frame)
93
+ return torch.stack(frames)
94
+
95
+
96
+ def save_decoded_frames(
97
+ imgs_dir: Path, save_dir: Path, frames: torch.Tensor, timestamps: list[float], fps: int
98
+ ) -> None:
99
+ if save_dir.exists() and len(list(save_dir.glob("frame_*.png"))) == len(timestamps):
100
+ return
101
+
102
+ save_dir.mkdir(parents=True, exist_ok=True)
103
+ for i, ts in enumerate(timestamps):
104
+ idx = int(ts * fps)
105
+ frame_hwc = (frames[i].permute((1, 2, 0)) * 255).type(torch.uint8).cpu().numpy()
106
+ PIL.Image.fromarray(frame_hwc).save(save_dir / f"frame_{idx:06d}_decoded.png")
107
+ shutil.copyfile(imgs_dir / f"frame_{idx:06d}.png", save_dir / f"frame_{idx:06d}_original.png")
108
+
109
+
110
+ def save_first_episode(imgs_dir: Path, dataset: LeRobotDataset) -> None:
111
+ ep_num_images = dataset.episode_data_index["to"][0].item()
112
+ if imgs_dir.exists() and len(list(imgs_dir.glob("frame_*.png"))) == ep_num_images:
113
+ return
114
+
115
+ imgs_dir.mkdir(parents=True, exist_ok=True)
116
+ hf_dataset = dataset.hf_dataset.with_format(None)
117
+
118
+ # We only save images from the first camera
119
+ img_keys = [key for key in hf_dataset.features if key.startswith("observation.image")]
120
+ imgs_dataset = hf_dataset.select_columns(img_keys[0])
121
+
122
+ for i, item in enumerate(
123
+ tqdm(imgs_dataset, desc=f"saving {dataset.repo_id} first episode images", leave=False)
124
+ ):
125
+ img = item[img_keys[0]]
126
+ img.save(str(imgs_dir / f"frame_{i:06d}.png"), quality=100)
127
+
128
+ if i >= ep_num_images - 1:
129
+ break
130
+
131
+
132
+ def sample_timestamps(timestamps_mode: str, ep_num_images: int, fps: int) -> list[float]:
133
+ # Start at 5 to allow for 2_frames_4_space and 6_frames
134
+ idx = random.randint(5, ep_num_images - 1)
135
+ match timestamps_mode:
136
+ case "1_frame":
137
+ frame_indexes = [idx]
138
+ case "2_frames":
139
+ frame_indexes = [idx - 1, idx]
140
+ case "2_frames_4_space":
141
+ frame_indexes = [idx - 5, idx]
142
+ case "6_frames":
143
+ frame_indexes = [idx - i for i in range(6)][::-1]
144
+ case _:
145
+ raise ValueError(timestamps_mode)
146
+
147
+ return [idx / fps for idx in frame_indexes]
148
+
149
+
150
+ def decode_video_frames(
151
+ video_path: str,
152
+ timestamps: list[float],
153
+ tolerance_s: float,
154
+ backend: str,
155
+ ) -> torch.Tensor:
156
+ if backend in ["pyav", "video_reader"]:
157
+ return decode_video_frames_torchvision(video_path, timestamps, tolerance_s, backend)
158
+ else:
159
+ raise NotImplementedError(backend)
160
+
161
+
162
+ def benchmark_decoding(
163
+ imgs_dir: Path,
164
+ video_path: Path,
165
+ timestamps_mode: str,
166
+ backend: str,
167
+ ep_num_images: int,
168
+ fps: int,
169
+ num_samples: int = 50,
170
+ num_workers: int = 4,
171
+ save_frames: bool = False,
172
+ ) -> dict:
173
+ def process_sample(sample: int):
174
+ time_benchmark = TimeBenchmark()
175
+ timestamps = sample_timestamps(timestamps_mode, ep_num_images, fps)
176
+ num_frames = len(timestamps)
177
+ result = {
178
+ "psnr_values": [],
179
+ "ssim_values": [],
180
+ "mse_values": [],
181
+ }
182
+
183
+ with time_benchmark:
184
+ frames = decode_video_frames(video_path, timestamps=timestamps, tolerance_s=5e-1, backend=backend)
185
+ result["load_time_video_ms"] = time_benchmark.result_ms / num_frames
186
+
187
+ with time_benchmark:
188
+ original_frames = load_original_frames(imgs_dir, timestamps, fps)
189
+ result["load_time_images_ms"] = time_benchmark.result_ms / num_frames
190
+
191
+ frames_np, original_frames_np = frames.numpy(), original_frames.numpy()
192
+ for i in range(num_frames):
193
+ result["mse_values"].append(mean_squared_error(original_frames_np[i], frames_np[i]))
194
+ result["psnr_values"].append(
195
+ peak_signal_noise_ratio(original_frames_np[i], frames_np[i], data_range=1.0)
196
+ )
197
+ result["ssim_values"].append(
198
+ structural_similarity(original_frames_np[i], frames_np[i], data_range=1.0, channel_axis=0)
199
+ )
200
+
201
+ if save_frames and sample == 0:
202
+ save_dir = video_path.with_suffix("") / f"{timestamps_mode}_{backend}"
203
+ save_decoded_frames(imgs_dir, save_dir, frames, timestamps, fps)
204
+
205
+ return result
206
+
207
+ load_times_video_ms = []
208
+ load_times_images_ms = []
209
+ mse_values = []
210
+ psnr_values = []
211
+ ssim_values = []
212
+
213
+ # A sample is a single set of decoded frames specified by timestamps_mode (e.g. a single frame, 2 frames, etc.).
214
+ # For each sample, we record metrics (loading time and quality metrics) which are then averaged over all samples.
215
+ # As these samples are independent, we run them in parallel threads to speed up the benchmark.
216
+ with ThreadPoolExecutor(max_workers=num_workers) as executor:
217
+ futures = [executor.submit(process_sample, i) for i in range(num_samples)]
218
+ for future in tqdm(as_completed(futures), total=num_samples, desc="samples", leave=False):
219
+ result = future.result()
220
+ load_times_video_ms.append(result["load_time_video_ms"])
221
+ load_times_images_ms.append(result["load_time_images_ms"])
222
+ psnr_values.extend(result["psnr_values"])
223
+ ssim_values.extend(result["ssim_values"])
224
+ mse_values.extend(result["mse_values"])
225
+
226
+ avg_load_time_video_ms = float(np.array(load_times_video_ms).mean())
227
+ avg_load_time_images_ms = float(np.array(load_times_images_ms).mean())
228
+ video_images_load_time_ratio = avg_load_time_video_ms / avg_load_time_images_ms
229
+
230
+ return {
231
+ "avg_load_time_video_ms": avg_load_time_video_ms,
232
+ "avg_load_time_images_ms": avg_load_time_images_ms,
233
+ "video_images_load_time_ratio": video_images_load_time_ratio,
234
+ "avg_mse": float(np.mean(mse_values)),
235
+ "avg_psnr": float(np.mean(psnr_values)),
236
+ "avg_ssim": float(np.mean(ssim_values)),
237
+ }
238
+
239
+
240
+ def benchmark_encoding_decoding(
241
+ dataset: LeRobotDataset,
242
+ video_path: Path,
243
+ imgs_dir: Path,
244
+ encoding_cfg: dict,
245
+ decoding_cfg: dict,
246
+ num_samples: int,
247
+ num_workers: int,
248
+ save_frames: bool,
249
+ overwrite: bool = False,
250
+ seed: int = 1337,
251
+ ) -> list[dict]:
252
+ fps = dataset.fps
253
+
254
+ if overwrite or not video_path.is_file():
255
+ tqdm.write(f"encoding {video_path}")
256
+ encode_video_frames(
257
+ imgs_dir=imgs_dir,
258
+ video_path=video_path,
259
+ fps=fps,
260
+ vcodec=encoding_cfg["vcodec"],
261
+ pix_fmt=encoding_cfg["pix_fmt"],
262
+ g=encoding_cfg.get("g"),
263
+ crf=encoding_cfg.get("crf"),
264
+ # fast_decode=encoding_cfg.get("fastdecode"),
265
+ overwrite=True,
266
+ )
267
+
268
+ ep_num_images = dataset.episode_data_index["to"][0].item()
269
+ width, height = tuple(dataset[0][dataset.meta.camera_keys[0]].shape[-2:])
270
+ num_pixels = width * height
271
+ video_size_bytes = video_path.stat().st_size
272
+ images_size_bytes = get_directory_size(imgs_dir)
273
+ video_images_size_ratio = video_size_bytes / images_size_bytes
274
+
275
+ random.seed(seed)
276
+ benchmark_table = []
277
+ for timestamps_mode in tqdm(
278
+ decoding_cfg["timestamps_modes"], desc="decodings (timestamps_modes)", leave=False
279
+ ):
280
+ for backend in tqdm(decoding_cfg["backends"], desc="decodings (backends)", leave=False):
281
+ benchmark_row = benchmark_decoding(
282
+ imgs_dir,
283
+ video_path,
284
+ timestamps_mode,
285
+ backend,
286
+ ep_num_images,
287
+ fps,
288
+ num_samples,
289
+ num_workers,
290
+ save_frames,
291
+ )
292
+ benchmark_row.update(
293
+ **{
294
+ "repo_id": dataset.repo_id,
295
+ "resolution": f"{width} x {height}",
296
+ "num_pixels": num_pixels,
297
+ "video_size_bytes": video_size_bytes,
298
+ "images_size_bytes": images_size_bytes,
299
+ "video_images_size_ratio": video_images_size_ratio,
300
+ "timestamps_mode": timestamps_mode,
301
+ "backend": backend,
302
+ },
303
+ **encoding_cfg,
304
+ )
305
+ benchmark_table.append(benchmark_row)
306
+
307
+ return benchmark_table
308
+
309
+
310
+ def main(
311
+ output_dir: Path,
312
+ repo_ids: list[str],
313
+ vcodec: list[str],
314
+ pix_fmt: list[str],
315
+ g: list[int],
316
+ crf: list[int],
317
+ # fastdecode: list[int],
318
+ timestamps_modes: list[str],
319
+ backends: list[str],
320
+ num_samples: int,
321
+ num_workers: int,
322
+ save_frames: bool,
323
+ ):
324
+ check_datasets_formats(repo_ids)
325
+ encoding_benchmarks = {
326
+ "g": g,
327
+ "crf": crf,
328
+ # "fastdecode": fastdecode,
329
+ }
330
+ decoding_benchmarks = {
331
+ "timestamps_modes": timestamps_modes,
332
+ "backends": backends,
333
+ }
334
+ headers = ["repo_id", "resolution", "num_pixels"]
335
+ headers += list(BASE_ENCODING.keys())
336
+ headers += [
337
+ "timestamps_mode",
338
+ "backend",
339
+ "video_size_bytes",
340
+ "images_size_bytes",
341
+ "video_images_size_ratio",
342
+ "avg_load_time_video_ms",
343
+ "avg_load_time_images_ms",
344
+ "video_images_load_time_ratio",
345
+ "avg_mse",
346
+ "avg_psnr",
347
+ "avg_ssim",
348
+ ]
349
+ file_paths = []
350
+ for video_codec in tqdm(vcodec, desc="encodings (vcodec)"):
351
+ for pixel_format in tqdm(pix_fmt, desc="encodings (pix_fmt)", leave=False):
352
+ benchmark_table = []
353
+ for repo_id in tqdm(repo_ids, desc="encodings (datasets)", leave=False):
354
+ dataset = LeRobotDataset(repo_id)
355
+ imgs_dir = output_dir / "images" / dataset.repo_id.replace("/", "_")
356
+ # We only use the first episode
357
+ save_first_episode(imgs_dir, dataset)
358
+ for key, values in tqdm(encoding_benchmarks.items(), desc="encodings (g, crf)", leave=False):
359
+ for value in tqdm(values, desc=f"encodings ({key})", leave=False):
360
+ encoding_cfg = BASE_ENCODING.copy()
361
+ encoding_cfg["vcodec"] = video_codec
362
+ encoding_cfg["pix_fmt"] = pixel_format
363
+ encoding_cfg[key] = value
364
+ args_path = Path("_".join(str(value) for value in encoding_cfg.values()))
365
+ video_path = output_dir / "videos" / args_path / f"{repo_id.replace('/', '_')}.mp4"
366
+ benchmark_table += benchmark_encoding_decoding(
367
+ dataset,
368
+ video_path,
369
+ imgs_dir,
370
+ encoding_cfg,
371
+ decoding_benchmarks,
372
+ num_samples,
373
+ num_workers,
374
+ save_frames,
375
+ )
376
+
377
+ # Save intermediate results
378
+ benchmark_df = pd.DataFrame(benchmark_table, columns=headers)
379
+ now = dt.datetime.now()
380
+ csv_path = (
381
+ output_dir
382
+ / f"{now:%Y-%m-%d}_{now:%H-%M-%S}_{video_codec}_{pixel_format}_{num_samples}-samples.csv"
383
+ )
384
+ benchmark_df.to_csv(csv_path, header=True, index=False)
385
+ file_paths.append(csv_path)
386
+ del benchmark_df
387
+
388
+ # Concatenate all results
389
+ df_list = [pd.read_csv(csv_path) for csv_path in file_paths]
390
+ concatenated_df = pd.concat(df_list, ignore_index=True)
391
+ concatenated_path = output_dir / f"{now:%Y-%m-%d}_{now:%H-%M-%S}_all_{num_samples}-samples.csv"
392
+ concatenated_df.to_csv(concatenated_path, header=True, index=False)
393
+
394
+
395
+ if __name__ == "__main__":
396
+ parser = argparse.ArgumentParser()
397
+ parser.add_argument(
398
+ "--output-dir",
399
+ type=Path,
400
+ default=Path("outputs/video_benchmark"),
401
+ help="Directory where the video benchmark outputs are written.",
402
+ )
403
+ parser.add_argument(
404
+ "--repo-ids",
405
+ type=str,
406
+ nargs="*",
407
+ default=[
408
+ "lerobot/pusht_image",
409
+ "aliberts/aloha_mobile_shrimp_image",
410
+ "aliberts/paris_street",
411
+ "aliberts/kitchen",
412
+ ],
413
+ help="Datasets repo-ids to test against. First episodes only are used. Must be images.",
414
+ )
415
+ parser.add_argument(
416
+ "--vcodec",
417
+ type=str,
418
+ nargs="*",
419
+ default=["libx264", "hevc", "libsvtav1"],
420
+ help="Video codecs to be tested",
421
+ )
422
+ parser.add_argument(
423
+ "--pix-fmt",
424
+ type=str,
425
+ nargs="*",
426
+ default=["yuv444p", "yuv420p"],
427
+ help="Pixel formats (chroma subsampling) to be tested",
428
+ )
429
+ parser.add_argument(
430
+ "--g",
431
+ type=parse_int_or_none,
432
+ nargs="*",
433
+ default=[1, 2, 3, 4, 5, 6, 10, 15, 20, 40, 100, None],
434
+ help="Group of pictures sizes to be tested.",
435
+ )
436
+ parser.add_argument(
437
+ "--crf",
438
+ type=parse_int_or_none,
439
+ nargs="*",
440
+ default=[0, 5, 10, 15, 20, 25, 30, 40, 50, None],
441
+ help="Constant rate factors to be tested.",
442
+ )
443
+ # parser.add_argument(
444
+ # "--fastdecode",
445
+ # type=int,
446
+ # nargs="*",
447
+ # default=[0, 1],
448
+ # help="Use the fastdecode tuning option. 0 disables it. "
449
+ # "For libx264 and libx265/hevc, only 1 is possible. "
450
+ # "For libsvtav1, 1, 2 or 3 are possible values with a higher number meaning a faster decoding optimization",
451
+ # )
452
+ parser.add_argument(
453
+ "--timestamps-modes",
454
+ type=str,
455
+ nargs="*",
456
+ default=[
457
+ "1_frame",
458
+ "2_frames",
459
+ "2_frames_4_space",
460
+ "6_frames",
461
+ ],
462
+ help="Timestamps scenarios to be tested.",
463
+ )
464
+ parser.add_argument(
465
+ "--backends",
466
+ type=str,
467
+ nargs="*",
468
+ default=["pyav", "video_reader"],
469
+ help="Torchvision decoding backend to be tested.",
470
+ )
471
+ parser.add_argument(
472
+ "--num-samples",
473
+ type=int,
474
+ default=50,
475
+ help="Number of samples for each encoding x decoding config.",
476
+ )
477
+ parser.add_argument(
478
+ "--num-workers",
479
+ type=int,
480
+ default=10,
481
+ help="Number of processes for parallelized sample processing.",
482
+ )
483
+ parser.add_argument(
484
+ "--save-frames",
485
+ type=int,
486
+ default=0,
487
+ help="Whether to save decoded frames or not. Enter a non-zero number for true.",
488
+ )
489
+ args = parser.parse_args()
490
+ main(**vars(args))
lerobot/docker/lerobot-cpu/Dockerfile ADDED
@@ -0,0 +1,29 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Configure image
2
+ ARG PYTHON_VERSION=3.10
3
+ FROM python:${PYTHON_VERSION}-slim
4
+
5
+ # Configure environment variables
6
+ ARG PYTHON_VERSION
7
+ ENV DEBIAN_FRONTEND=noninteractive
8
+ ENV MUJOCO_GL="egl"
9
+ ENV PATH="/opt/venv/bin:$PATH"
10
+
11
+ # Install dependencies and set up Python in a single layer
12
+ RUN apt-get update && apt-get install -y --no-install-recommends \
13
+ build-essential cmake git \
14
+ libglib2.0-0 libgl1-mesa-glx libegl1-mesa ffmpeg \
15
+ speech-dispatcher libgeos-dev \
16
+ && ln -s /usr/bin/python${PYTHON_VERSION} /usr/bin/python \
17
+ && python -m venv /opt/venv \
18
+ && apt-get clean && rm -rf /var/lib/apt/lists/* \
19
+ && echo "source /opt/venv/bin/activate" >> /root/.bashrc
20
+
21
+ # Clone repository and install LeRobot in a single layer
22
+ COPY . /lerobot
23
+ WORKDIR /lerobot
24
+ RUN /opt/venv/bin/pip install --upgrade --no-cache-dir pip \
25
+ && /opt/venv/bin/pip install --no-cache-dir ".[test, aloha, xarm, pusht, smolvla]" \
26
+ --extra-index-url https://download.pytorch.org/whl/cpu
27
+
28
+ # Execute in bash shell rather than python
29
+ CMD ["/bin/bash"]
lerobot/docker/lerobot-gpu-dev/Dockerfile ADDED
@@ -0,0 +1,68 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ FROM nvidia/cuda:12.2.2-devel-ubuntu22.04
2
+
3
+ # Configure image
4
+ ARG PYTHON_VERSION=3.10
5
+ ARG DEBIAN_FRONTEND=noninteractive
6
+
7
+ # Install apt dependencies
8
+ RUN apt-get update && apt-get install -y --no-install-recommends \
9
+ build-essential cmake \
10
+ git git-lfs openssh-client \
11
+ nano vim less util-linux tree \
12
+ htop atop nvtop \
13
+ sed gawk grep curl wget zip unzip \
14
+ tcpdump sysstat screen tmux \
15
+ libglib2.0-0 libgl1-mesa-glx libegl1-mesa \
16
+ speech-dispatcher portaudio19-dev libgeos-dev \
17
+ python${PYTHON_VERSION} python${PYTHON_VERSION}-venv python${PYTHON_VERSION}-dev \
18
+ && apt-get clean && rm -rf /var/lib/apt/lists/*
19
+
20
+ # Install ffmpeg build dependencies. See:
21
+ # https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu
22
+ # TODO(aliberts): create image to build dependencies from source instead
23
+ RUN apt-get update && apt-get install -y --no-install-recommends \
24
+ autoconf automake yasm \
25
+ libass-dev \
26
+ libfreetype6-dev \
27
+ libgnutls28-dev \
28
+ libunistring-dev \
29
+ libmp3lame-dev \
30
+ libtool \
31
+ libvorbis-dev \
32
+ meson \
33
+ ninja-build \
34
+ pkg-config \
35
+ texinfo \
36
+ yasm \
37
+ zlib1g-dev \
38
+ nasm \
39
+ libx264-dev \
40
+ libx265-dev libnuma-dev \
41
+ libvpx-dev \
42
+ libfdk-aac-dev \
43
+ libopus-dev \
44
+ libsvtav1-dev libsvtav1enc-dev libsvtav1dec-dev \
45
+ libdav1d-dev
46
+
47
+ # Install gh cli tool
48
+ RUN (type -p wget >/dev/null || (apt update && apt-get install wget -y)) \
49
+ && mkdir -p -m 755 /etc/apt/keyrings \
50
+ && wget -qO- https://cli.github.com/packages/githubcli-archive-keyring.gpg | tee /etc/apt/keyrings/githubcli-archive-keyring.gpg > /dev/null \
51
+ && chmod go+r /etc/apt/keyrings/githubcli-archive-keyring.gpg \
52
+ && echo "deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/githubcli-archive-keyring.gpg] https://cli.github.com/packages stable main" | tee /etc/apt/sources.list.d/github-cli.list > /dev/null \
53
+ && apt update \
54
+ && apt install gh -y \
55
+ && apt clean && rm -rf /var/lib/apt/lists/*
56
+
57
+ # Setup `python`
58
+ RUN ln -s /usr/bin/python3 /usr/bin/python
59
+
60
+ # Install poetry
61
+ RUN curl -sSL https://install.python-poetry.org | python -
62
+ ENV PATH="/root/.local/bin:$PATH"
63
+ RUN echo 'if [ "$HOME" != "/root" ]; then ln -sf /root/.local/bin/poetry $HOME/.local/bin/poetry; fi' >> /root/.bashrc
64
+ RUN poetry config virtualenvs.create false
65
+ RUN poetry config virtualenvs.in-project true
66
+
67
+ # Set EGL as the rendering backend for MuJoCo
68
+ ENV MUJOCO_GL="egl"
lerobot/docker/lerobot-gpu/Dockerfile ADDED
@@ -0,0 +1,24 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ FROM nvidia/cuda:12.4.1-base-ubuntu22.04
2
+
3
+ # Configure environment variables
4
+ ARG PYTHON_VERSION=3.10
5
+ ENV DEBIAN_FRONTEND=noninteractive
6
+ ENV MUJOCO_GL="egl"
7
+ ENV PATH="/opt/venv/bin:$PATH"
8
+
9
+ # Install dependencies and set up Python in a single layer
10
+ RUN apt-get update && apt-get install -y --no-install-recommends \
11
+ build-essential cmake git \
12
+ libglib2.0-0 libgl1-mesa-glx libegl1-mesa ffmpeg \
13
+ speech-dispatcher libgeos-dev \
14
+ python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
15
+ && ln -s /usr/bin/python${PYTHON_VERSION} /usr/bin/python \
16
+ && python -m venv /opt/venv \
17
+ && apt-get clean && rm -rf /var/lib/apt/lists/* \
18
+ && echo "source /opt/venv/bin/activate" >> /root/.bashrc
19
+
20
+ # Clone repository and install LeRobot in a single layer
21
+ COPY . /lerobot
22
+ WORKDIR /lerobot
23
+ RUN /opt/venv/bin/pip install --upgrade --no-cache-dir pip \
24
+ && /opt/venv/bin/pip install --no-cache-dir ".[test, aloha, xarm, pusht, dynamixel, smolvla]"
lerobot/docs/README.md ADDED
@@ -0,0 +1,137 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ <!---
2
+ Copyright 2020 The HuggingFace Team. All rights reserved.
3
+
4
+ Licensed under the Apache License, Version 2.0 (the "License");
5
+ you may not use this file except in compliance with the License.
6
+ You may obtain a copy of the License at
7
+
8
+ http://www.apache.org/licenses/LICENSE-2.0
9
+
10
+ Unless required by applicable law or agreed to in writing, software
11
+ distributed under the License is distributed on an "AS IS" BASIS,
12
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13
+ See the License for the specific language governing permissions and
14
+ limitations under the License.
15
+ -->
16
+
17
+ # Generating the documentation
18
+
19
+ To generate the documentation, you first have to build it. Several packages are necessary to build the doc,
20
+ you can install them with the following command, at the root of the code repository:
21
+
22
+ ```bash
23
+ pip install -e ".[docs]"
24
+ ```
25
+
26
+ You will also need `nodejs`. Please refer to their [installation page](https://nodejs.org/en/download)
27
+
28
+ ---
29
+ **NOTE**
30
+
31
+ You only need to generate the documentation to inspect it locally (if you're planning changes and want to
32
+ check how they look before committing for instance). You don't have to `git commit` the built documentation.
33
+
34
+ ---
35
+
36
+ ## Building the documentation
37
+
38
+ Once you have setup the `doc-builder` and additional packages, you can generate the documentation by
39
+ typing the following command:
40
+
41
+ ```bash
42
+ doc-builder build lerobot docs/source/ --build_dir ~/tmp/test-build
43
+ ```
44
+
45
+ You can adapt the `--build_dir` to set any temporary folder that you prefer. This command will create it and generate
46
+ the MDX files that will be rendered as the documentation on the main website. You can inspect them in your favorite
47
+ Markdown editor.
48
+
49
+ ## Previewing the documentation
50
+
51
+ To preview the docs, first install the `watchdog` module with:
52
+
53
+ ```bash
54
+ pip install watchdog
55
+ ```
56
+
57
+ Then run the following command:
58
+
59
+ ```bash
60
+ doc-builder preview lerobot docs/source/
61
+ ```
62
+
63
+ The docs will be viewable at [http://localhost:3000](http://localhost:3000). You can also preview the docs once you have opened a PR. You will see a bot add a comment to a link where the documentation with your changes lives.
64
+
65
+ ---
66
+ **NOTE**
67
+
68
+ The `preview` command only works with existing doc files. When you add a completely new file, you need to update `_toctree.yml` & restart `preview` command (`ctrl-c` to stop it & call `doc-builder preview ...` again).
69
+
70
+ ---
71
+
72
+ ## Adding a new element to the navigation bar
73
+
74
+ Accepted files are Markdown (.md).
75
+
76
+ Create a file with its extension and put it in the source directory. You can then link it to the toc-tree by putting
77
+ the filename without the extension in the [`_toctree.yml`](https://github.com/huggingface/lerobot/blob/main/docs/source/_toctree.yml) file.
78
+
79
+ ## Renaming section headers and moving sections
80
+
81
+ It helps to keep the old links working when renaming the section header and/or moving sections from one document to another. This is because the old links are likely to be used in Issues, Forums, and Social media and it'd make for a much more superior user experience if users reading those months later could still easily navigate to the originally intended information.
82
+
83
+ Therefore, we simply keep a little map of moved sections at the end of the document where the original section was. The key is to preserve the original anchor.
84
+
85
+ So if you renamed a section from: "Section A" to "Section B", then you can add at the end of the file:
86
+
87
+ ```
88
+ Sections that were moved:
89
+
90
+ [ <a href="#section-b">Section A</a><a id="section-a"></a> ]
91
+ ```
92
+ and of course, if you moved it to another file, then:
93
+
94
+ ```
95
+ Sections that were moved:
96
+
97
+ [ <a href="../new-file#section-b">Section A</a><a id="section-a"></a> ]
98
+ ```
99
+
100
+ Use the relative style to link to the new file so that the versioned docs continue to work.
101
+
102
+ For an example of a rich moved sections set please see the very end of [the transformers Trainer doc](https://github.com/huggingface/transformers/blob/main/docs/source/en/main_classes/trainer.md).
103
+
104
+ ### Adding a new tutorial
105
+
106
+ Adding a new tutorial or section is done in two steps:
107
+
108
+ - Add a new file under `./source`. This file can either be ReStructuredText (.rst) or Markdown (.md).
109
+ - Link that file in `./source/_toctree.yml` on the correct toc-tree.
110
+
111
+ Make sure to put your new file under the proper section. If you have a doubt, feel free to ask in a Github Issue or PR.
112
+
113
+ ### Writing source documentation
114
+
115
+ Values that should be put in `code` should either be surrounded by backticks: \`like so\`. Note that argument names
116
+ and objects like True, None or any strings should usually be put in `code`.
117
+
118
+ #### Writing a multi-line code block
119
+
120
+ Multi-line code blocks can be useful for displaying examples. They are done between two lines of three backticks as usual in Markdown:
121
+
122
+
123
+ ````
124
+ ```
125
+ # first line of code
126
+ # second line
127
+ # etc
128
+ ```
129
+ ````
130
+
131
+ #### Adding an image
132
+
133
+ Due to the rapidly growing repository, it is important to make sure that no files that would significantly weigh down the repository are added. This includes images, videos, and other non-text files. We prefer to leverage a hf.co hosted `dataset` like
134
+ the ones hosted on [`hf-internal-testing`](https://huggingface.co/hf-internal-testing) in which to place these files and reference
135
+ them by URL. We recommend putting them in the following dataset: [huggingface/documentation-images](https://huggingface.co/datasets/huggingface/documentation-images).
136
+ If an external contribution, feel free to add the images to your PR and ask a Hugging Face member to migrate your images
137
+ to this dataset.
lerobot/docs/source/_toctree.yml ADDED
@@ -0,0 +1,44 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ - sections:
2
+ - local: index
3
+ title: LeRobot
4
+ - local: installation
5
+ title: Installation
6
+ title: Get started
7
+ - sections:
8
+ - local: il_robots
9
+ title: Imitation Learning for Robots
10
+ - local: il_sim
11
+ title: Imitation Learning in Sim
12
+ - local: cameras
13
+ title: Cameras
14
+ - local: integrate_hardware
15
+ title: Bring Your Own Hardware
16
+ - local: hilserl
17
+ title: Train a Robot with RL
18
+ - local: hilserl_sim
19
+ title: Train RL in Simulation
20
+ title: "Tutorials"
21
+ - sections:
22
+ - local: smolvla
23
+ title: Finetune SmolVLA
24
+ title: "Policies"
25
+ - sections:
26
+ - local: so101
27
+ title: SO-101
28
+ - local: so100
29
+ title: SO-100
30
+ - local: koch
31
+ title: Koch v1.1
32
+ - local: lekiwi
33
+ title: LeKiwi
34
+ title: "Robots"
35
+ - sections:
36
+ - local: notebooks
37
+ title: Notebooks
38
+ title: "Resources"
39
+ - sections:
40
+ - local: contributing
41
+ title: Contribute to LeRobot
42
+ - local: backwardcomp
43
+ title: Backward compatibility
44
+ title: "About"
lerobot/docs/source/backwardcomp.mdx ADDED
@@ -0,0 +1,82 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Backward compatibility
2
+
3
+ ## Hardware API redesign
4
+
5
+ PR [#777](https://github.com/huggingface/lerobot/pull/777) improves the LeRobot calibration but is **not backward-compatible**. Below is a overview of what changed and how you can continue to work with datasets created before this pull request.
6
+
7
+ ### What changed?
8
+
9
+ | | Before PR #777 | After PR #777 |
10
+ | --------------------------------- | ------------------------------------------------- | --------------------------------------------------------------------------- |
11
+ | **Joint range** | Degrees `-180...180°` | **Normalised range** Joints: `–100...100` Gripper: `0...100` |
12
+ | **Zero position (SO100 / SO101)** | Arm fully extended horizontally | **In middle of the range for each joint** |
13
+ | **Boundary handling** | Software safeguards to detect ±180 ° wrap-arounds | No wrap-around logic needed due to mid-range zero |
14
+
15
+ ---
16
+
17
+ ### Impact on existing datasets
18
+
19
+ * Recorded trajectories created **before** PR #777 will replay incorrectly if loaded directly:
20
+ * Joint angles are offset and incorrectly normalized.
21
+ * Any models directly finetuned or trained on the old data will need their inputs and outputs converted.
22
+
23
+ ### Using datasets made with the previous calibration system
24
+ We provide a migration example script for replaying an episode recorded with the previous calibration here: `examples/backward_compatibility/replay.py`.
25
+ Below we take you through the modifications that are done in the example script to make the previous calibration datasets work.
26
+
27
+ ```diff
28
+ + key = f"{name.removeprefix('main_')}.pos"
29
+ action[key] = action_array[i].item()
30
+ + action["shoulder_lift.pos"] = -(action["shoulder_lift.pos"] - 90)
31
+ + action["elbow_flex.pos"] -= 90
32
+ ```
33
+
34
+ Let's break this down.
35
+ New codebase uses `.pos` suffix for the position observations and we have removed `main_` prefix:
36
+ ```python
37
+ key = f"{name.removeprefix('main_')}.pos"
38
+ ```
39
+
40
+ For `"shoulder_lift"` (id = 2), the 0 position is changed by -90 degrees and the direction is reversed compared to old calibration/code.
41
+ ```python
42
+ action["shoulder_lift.pos"] = -(action["shoulder_lift.pos"] - 90)
43
+ ```
44
+ For `"elbow_flex"` (id = 3), the 0 position is changed by -90 degrees compared to old calibration/code.
45
+ ```python
46
+ action["elbow_flex.pos"] -= 90
47
+ ```
48
+
49
+ To use degrees normalization we then set the `--robot.use_degrees` option to `true`.
50
+ ```diff
51
+ python examples/backward_compatibility/replay.py \
52
+ --robot.type=so101_follower \
53
+ --robot.port=/dev/tty.usbmodem5A460814411 \
54
+ --robot.id=blue \
55
+ + --robot.use_degrees=true \
56
+ --dataset.repo_id=my_dataset_id \
57
+ --dataset.episode=0
58
+ ```
59
+
60
+ ### Using policies trained with the previous calibration system
61
+
62
+ Policies output actions in the same format as the datasets (`torch.Tensors`). Therefore, the same transformations should be applied.
63
+
64
+ To find these transformations, we recommend to first try and and replay an episode of the dataset your policy was trained on using the section above.
65
+ Then, add these same transformations on your inference script (shown here in the `record.py` script):
66
+ ```diff
67
+ action_values = predict_action(
68
+ observation_frame,
69
+ policy,
70
+ get_safe_torch_device(policy.config.device),
71
+ policy.config.use_amp,
72
+ task=single_task,
73
+ robot_type=robot.robot_type,
74
+ )
75
+ action = {key: action_values[i].item() for i, key in enumerate(robot.action_features)}
76
+
77
+ + action["shoulder_lift.pos"] = -(action["shoulder_lift.pos"] - 90)
78
+ + action["elbow_flex.pos"] -= 90
79
+ robot.send_action(action)
80
+ ```
81
+
82
+ If you have questions or run into migration issues, feel free to ask them on [Discord](https://discord.gg/s3KuuzsPFb)
lerobot/docs/source/cameras.mdx ADDED
@@ -0,0 +1,173 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Cameras
2
+
3
+ LeRobot offers multiple options for video capture, including phone cameras, built-in laptop cameras, external webcams, and Intel RealSense cameras. To efficiently record frames from most cameras, you can use either the `OpenCVCamera` or `RealSenseCamera` class. For additional compatibility details on the `OpenCVCamera` class, refer to the [Video I/O with OpenCV Overview](https://docs.opencv.org/4.x/d0/da7/videoio_overview.html).
4
+
5
+ ### Finding your camera
6
+
7
+ To instantiate a camera, you need a camera identifier. This identifier might change if you reboot your computer or re-plug your camera, a behavior mostly dependant on your operating system.
8
+
9
+ To find the camera indices of the cameras plugged into your system, run the following script:
10
+ ```bash
11
+ python -m lerobot.find_cameras opencv # or realsense for Intel Realsense cameras
12
+ ```
13
+
14
+ The output will look something like this if you have two cameras connected:
15
+ ```
16
+ --- Detected Cameras ---
17
+ Camera #0:
18
+ Name: OpenCV Camera @ 0
19
+ Type: OpenCV
20
+ Id: 0
21
+ Backend api: AVFOUNDATION
22
+ Default stream profile:
23
+ Format: 16.0
24
+ Width: 1920
25
+ Height: 1080
26
+ Fps: 15.0
27
+ --------------------
28
+ (more cameras ...)
29
+ ```
30
+
31
+ > [!WARNING]
32
+ > When using Intel RealSense cameras in `macOS`, you could get this [error](https://github.com/IntelRealSense/librealsense/issues/12307): `Error finding RealSense cameras: failed to set power state`, this can be solved by running the same command with `sudo` permissions. Note that using RealSense cameras in `macOS` is unstable.
33
+
34
+
35
+ ## Use Cameras
36
+
37
+ Below are two examples, demonstrating how to work with the API.
38
+
39
+ - **Asynchronous frame capture** using an OpenCV-based camera
40
+ - **Color and depth capture** using an Intel RealSense camera
41
+
42
+
43
+ <hfoptions id="shell_restart">
44
+ <hfoption id="Open CV Camera">
45
+
46
+ ```python
47
+ from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
48
+ from lerobot.cameras.opencv.camera_opencv import OpenCVCamera
49
+ from lerobot.cameras.configs import ColorMode, Cv2Rotation
50
+
51
+ # Construct an `OpenCVCameraConfig` with your desired FPS, resolution, color mode, and rotation.
52
+ config = OpenCVCameraConfig(
53
+ index_or_path=0,
54
+ fps=15,
55
+ width=1920,
56
+ height=1080,
57
+ color_mode=ColorMode.RGB,
58
+ rotation=Cv2Rotation.NO_ROTATION
59
+ )
60
+
61
+ # Instantiate and connect an `OpenCVCamera`, performing a warm-up read (default).
62
+ camera = OpenCVCamera(config)
63
+ camera.connect()
64
+
65
+ # Read frames asynchronously in a loop via `async_read(timeout_ms)`
66
+ try:
67
+ for i in range(10):
68
+ frame = camera.async_read(timeout_ms=200)
69
+ print(f"Async frame {i} shape:", frame.shape)
70
+ finally:
71
+ camera.disconnect()
72
+ ```
73
+
74
+ </hfoption>
75
+ <hfoption id="Intel Realsense Camera">
76
+
77
+ ```python
78
+ from lerobot.cameras.realsense.configuration_realsense import RealSenseCameraConfig
79
+ from lerobot.cameras.realsense.camera_realsense import RealSenseCamera
80
+ from lerobot.cameras.configs import ColorMode, Cv2Rotation
81
+
82
+ # Create a `RealSenseCameraConfig` specifying your camera’s serial number and enabling depth.
83
+ config = RealSenseCameraConfig(
84
+ serial_number_or_name="233522074606",
85
+ fps=15,
86
+ width=640,
87
+ height=480,
88
+ color_mode=ColorMode.RGB,
89
+ use_depth=True,
90
+ rotation=Cv2Rotation.NO_ROTATION
91
+ )
92
+
93
+ # Instantiate and connect a `RealSenseCamera` with warm-up read (default).
94
+ camera = RealSenseCamera(config)
95
+ camera.connect()
96
+
97
+ # Capture a color frame via `read()` and a depth map via `read_depth()`.
98
+ try:
99
+ color_frame = camera.read()
100
+ depth_map = camera.read_depth()
101
+ print("Color frame shape:", color_frame.shape)
102
+ print("Depth map shape:", depth_map.shape)
103
+ finally:
104
+ camera.disconnect()
105
+ ```
106
+ </hfoption>
107
+ </hfoptions>
108
+
109
+
110
+ ## Use your phone
111
+ <hfoptions id="use phone">
112
+ <hfoption id="Mac">
113
+
114
+ To use your iPhone as a camera on macOS, enable the Continuity Camera feature:
115
+ - Ensure your Mac is running macOS 13 or later, and your iPhone is on iOS 16 or later.
116
+ - Sign in both devices with the same Apple ID.
117
+ - Connect your devices with a USB cable or turn on Wi-Fi and Bluetooth for a wireless connection.
118
+
119
+ For more details, visit [Apple support](https://support.apple.com/en-gb/guide/mac-help/mchl77879b8a/mac).
120
+
121
+ Your iPhone should be detected automatically when running the camera setup script in the next section.
122
+
123
+ </hfoption>
124
+ <hfoption id="Linux">
125
+
126
+ If you want to use your phone as a camera on Linux, follow these steps to set up a virtual camera
127
+
128
+ 1. *Install `v4l2loopback-dkms` and `v4l-utils`*. Those packages are required to create virtual camera devices (`v4l2loopback`) and verify their settings with the `v4l2-ctl` utility from `v4l-utils`. Install them using:
129
+ ```python
130
+ sudo apt install v4l2loopback-dkms v4l-utils
131
+ ```
132
+ 2. *Install [DroidCam](https://droidcam.app) on your phone*. This app is available for both iOS and Android.
133
+ 3. *Install [OBS Studio](https://obsproject.com)*. This software will help you manage the camera feed. Install it using [Flatpak](https://flatpak.org):
134
+ ```python
135
+ flatpak install flathub com.obsproject.Studio
136
+ ```
137
+ 4. *Install the DroidCam OBS plugin*. This plugin integrates DroidCam with OBS Studio. Install it with:
138
+ ```python
139
+ flatpak install flathub com.obsproject.Studio.Plugin.DroidCam
140
+ ```
141
+ 5. *Start OBS Studio*. Launch with:
142
+ ```python
143
+ flatpak run com.obsproject.Studio
144
+ ```
145
+ 6. *Add your phone as a source*. Follow the instructions [here](https://droidcam.app/obs/usage). Be sure to set the resolution to `640x480`.
146
+ 7. *Adjust resolution settings*. In OBS Studio, go to `File > Settings > Video`. Change the `Base(Canvas) Resolution` and the `Output(Scaled) Resolution` to `640x480` by manually typing it in.
147
+ 8. *Start virtual camera*. In OBS Studio, follow the instructions [here](https://obsproject.com/kb/virtual-camera-guide).
148
+ 9. *Verify the virtual camera setup*. Use `v4l2-ctl` to list the devices:
149
+ ```python
150
+ v4l2-ctl --list-devices
151
+ ```
152
+ You should see an entry like:
153
+ ```
154
+ VirtualCam (platform:v4l2loopback-000):
155
+ /dev/video1
156
+ ```
157
+ 10. *Check the camera resolution*. Use `v4l2-ctl` to ensure that the virtual camera output resolution is `640x480`. Change `/dev/video1` to the port of your virtual camera from the output of `v4l2-ctl --list-devices`.
158
+ ```python
159
+ v4l2-ctl -d /dev/video1 --get-fmt-video
160
+ ```
161
+ You should see an entry like:
162
+ ```
163
+ >>> Format Video Capture:
164
+ >>> Width/Height : 640/480
165
+ >>> Pixel Format : 'YUYV' (YUYV 4:2:2)
166
+ ```
167
+
168
+ Troubleshooting: If the resolution is not correct you will have to delete the Virtual Camera port and try again as it cannot be changed.
169
+
170
+ If everything is set up correctly, you can proceed with the rest of the tutorial.
171
+
172
+ </hfoption>
173
+ </hfoptions>
lerobot/docs/source/contributing.md ADDED
@@ -0,0 +1,305 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # How to contribute to 🤗 LeRobot?
2
+
3
+ Everyone is welcome to contribute, and we value everybody's contribution. Code
4
+ is thus not the only way to help the community. Answering questions, helping
5
+ others, reaching out and improving the documentations are immensely valuable to
6
+ the community.
7
+
8
+ It also helps us if you spread the word: reference the library from blog posts
9
+ on the awesome projects it made possible, shout out on Twitter when it has
10
+ helped you, or simply ⭐️ the repo to say "thank you".
11
+
12
+ Whichever way you choose to contribute, please be mindful to respect our
13
+ [code of conduct](https://github.com/huggingface/lerobot/blob/main/CODE_OF_CONDUCT.md).
14
+
15
+ ## You can contribute in so many ways!
16
+
17
+ Some of the ways you can contribute to 🤗 LeRobot:
18
+ * Fixing outstanding issues with the existing code.
19
+ * Implementing new models, datasets or simulation environments.
20
+ * Contributing to the examples or to the documentation.
21
+ * Submitting issues related to bugs or desired new features.
22
+
23
+ Following the guides below, feel free to open issues and PRs and to coordinate your efforts with the community on our [Discord Channel](https://discord.gg/VjFz58wn3R). For specific inquiries, reach out to [Remi Cadene](mailto:remi.cadene@huggingface.co).
24
+
25
+ If you are not sure how to contribute or want to know the next features we working on, look on this project page: [LeRobot TODO](https://github.com/orgs/huggingface/projects/46)
26
+
27
+ ## Submitting a new issue or feature request
28
+
29
+ Do your best to follow these guidelines when submitting an issue or a feature
30
+ request. It will make it easier for us to come back to you quickly and with good
31
+ feedback.
32
+
33
+ ### Did you find a bug?
34
+
35
+ The 🤗 LeRobot library is robust and reliable thanks to the users who notify us of
36
+ the problems they encounter. So thank you for reporting an issue.
37
+
38
+ First, we would really appreciate it if you could **make sure the bug was not
39
+ already reported** (use the search bar on Github under Issues).
40
+
41
+ Did not find it? :( So we can act quickly on it, please follow these steps:
42
+
43
+ * Include your **OS type and version**, the versions of **Python** and **PyTorch**.
44
+ * A short, self-contained, code snippet that allows us to reproduce the bug in
45
+ less than 30s.
46
+ * The full traceback if an exception is raised.
47
+ * Attach any other additional information, like screenshots, you think may help.
48
+
49
+ ### Do you want a new feature?
50
+
51
+ A good feature request addresses the following points:
52
+
53
+ 1. Motivation first:
54
+ * Is it related to a problem/frustration with the library? If so, please explain
55
+ why. Providing a code snippet that demonstrates the problem is best.
56
+ * Is it related to something you would need for a project? We'd love to hear
57
+ about it!
58
+ * Is it something you worked on and think could benefit the community?
59
+ Awesome! Tell us what problem it solved for you.
60
+ 2. Write a *paragraph* describing the feature.
61
+ 3. Provide a **code snippet** that demonstrates its future use.
62
+ 4. In case this is related to a paper, please attach a link.
63
+ 5. Attach any additional information (drawings, screenshots, etc.) you think may help.
64
+
65
+ If your issue is well written we're already 80% of the way there by the time you
66
+ post it.
67
+
68
+ ## Adding new policies, datasets or environments
69
+
70
+ Look at our implementations for [datasets](./src/lerobot/datasets/), [policies](./src/lerobot/policies/),
71
+ environments ([aloha](https://github.com/huggingface/gym-aloha),
72
+ [xarm](https://github.com/huggingface/gym-xarm),
73
+ [pusht](https://github.com/huggingface/gym-pusht))
74
+ and follow the same api design.
75
+
76
+ When implementing a new dataset loadable with LeRobotDataset follow these steps:
77
+ - Update `available_datasets_per_env` in `lerobot/__init__.py`
78
+
79
+ When implementing a new environment (e.g. `gym_aloha`), follow these steps:
80
+ - Update `available_tasks_per_env` and `available_datasets_per_env` in `lerobot/__init__.py`
81
+
82
+ When implementing a new policy class (e.g. `DiffusionPolicy`) follow these steps:
83
+ - Update `available_policies` and `available_policies_per_env`, in `lerobot/__init__.py`
84
+ - Set the required `name` class attribute.
85
+ - Update variables in `tests/test_available.py` by importing your new Policy class
86
+
87
+ ## Submitting a pull request (PR)
88
+
89
+ Before writing code, we strongly advise you to search through the existing PRs or
90
+ issues to make sure that nobody is already working on the same thing. If you are
91
+ unsure, it is always a good idea to open an issue to get some feedback.
92
+
93
+ You will need basic `git` proficiency to be able to contribute to
94
+ 🤗 LeRobot. `git` is not the easiest tool to use but it has the greatest
95
+ manual. Type `git --help` in a shell and enjoy. If you prefer books, [Pro
96
+ Git](https://git-scm.com/book/en/v2) is a very good reference.
97
+
98
+ Follow these steps to start contributing:
99
+
100
+ 1. Fork the [repository](https://github.com/huggingface/lerobot) by
101
+ clicking on the 'Fork' button on the repository's page. This creates a copy of the code
102
+ under your GitHub user account.
103
+
104
+ 2. Clone your fork to your local disk, and add the base repository as a remote. The following command
105
+ assumes you have your public SSH key uploaded to GitHub. See the following guide for more
106
+ [information](https://docs.github.com/en/repositories/creating-and-managing-repositories/cloning-a-repository).
107
+
108
+ ```bash
109
+ git clone git@github.com:<your Github handle>/lerobot.git
110
+ cd lerobot
111
+ git remote add upstream https://github.com/huggingface/lerobot.git
112
+ ```
113
+
114
+ 3. Create a new branch to hold your development changes, and do this for every new PR you work on.
115
+
116
+ Start by synchronizing your `main` branch with the `upstream/main` branch (more details in the [GitHub Docs](https://docs.github.com/en/github/collaborating-with-issues-and-pull-requests/syncing-a-fork)):
117
+
118
+ ```bash
119
+ git checkout main
120
+ git fetch upstream
121
+ git rebase upstream/main
122
+ ```
123
+
124
+ Once your `main` branch is synchronized, create a new branch from it:
125
+
126
+ ```bash
127
+ git checkout -b a-descriptive-name-for-my-changes
128
+ ```
129
+
130
+ 🚨 **Do not** work on the `main` branch.
131
+
132
+ 4. for development, we advise to use a tool like `poetry` or `uv` instead of just `pip` to easily track our dependencies.
133
+ Follow the instructions to [install poetry](https://python-poetry.org/docs/#installation) (use a version >=2.1.0) or to [install uv](https://docs.astral.sh/uv/getting-started/installation/#installation-methods) if you don't have one of them already.
134
+
135
+ Set up a development environment with conda or miniconda:
136
+ ```bash
137
+ conda create -y -n lerobot-dev python=3.10 && conda activate lerobot-dev
138
+ ```
139
+
140
+ If you're using `uv`, it can manage python versions so you can instead do:
141
+ ```bash
142
+ uv venv --python 3.10 && source .venv/bin/activate
143
+ ```
144
+
145
+ To develop on 🤗 LeRobot, you will at least need to install the `dev` and `test` extras dependencies along with the core library:
146
+
147
+ using `poetry`
148
+ ```bash
149
+ poetry sync --extras "dev test"
150
+ ```
151
+
152
+ using `uv`
153
+ ```bash
154
+ uv sync --extra dev --extra test
155
+ ```
156
+
157
+ You can also install the project with all its dependencies (including environments):
158
+
159
+ using `poetry`
160
+ ```bash
161
+ poetry sync --all-extras
162
+ ```
163
+
164
+ using `uv`
165
+ ```bash
166
+ uv sync --all-extras
167
+ ```
168
+
169
+ > **Note:** If you don't install simulation environments with `--all-extras`, the tests that require them will be skipped when running the pytest suite locally. However, they *will* be tested in the CI. In general, we advise you to install everything and test locally before pushing.
170
+
171
+ Whichever command you chose to install the project (e.g. `poetry sync --all-extras`), you should run it again when pulling code with an updated version of `pyproject.toml` and `poetry.lock` in order to synchronize your virtual environment with the new dependencies.
172
+
173
+ The equivalent of `pip install some-package`, would just be:
174
+
175
+ using `poetry`
176
+ ```bash
177
+ poetry add some-package
178
+ ```
179
+
180
+ using `uv`
181
+ ```bash
182
+ uv add some-package
183
+ ```
184
+
185
+ When making changes to the poetry sections of the `pyproject.toml`, you should run the following command to lock dependencies.
186
+ using `poetry`
187
+ ```bash
188
+ poetry lock
189
+ ```
190
+
191
+ using `uv`
192
+ ```bash
193
+ uv lock
194
+ ```
195
+
196
+
197
+ 5. Develop the features on your branch.
198
+
199
+ As you work on the features, you should make sure that the test suite
200
+ passes. You should run the tests impacted by your changes like this (see
201
+ below an explanation regarding the environment variable):
202
+
203
+ ```bash
204
+ pytest tests/<TEST_TO_RUN>.py
205
+ ```
206
+
207
+ 6. Follow our style.
208
+
209
+ `lerobot` relies on `ruff` to format its source code
210
+ consistently. Set up [`pre-commit`](https://pre-commit.com/) to run these checks
211
+ automatically as Git commit hooks.
212
+
213
+ Install `pre-commit` hooks:
214
+ ```bash
215
+ pre-commit install
216
+ ```
217
+
218
+ You can run these hooks whenever you need on staged files with:
219
+ ```bash
220
+ pre-commit
221
+ ```
222
+
223
+ Once you're happy with your changes, add changed files using `git add` and
224
+ make a commit with `git commit` to record your changes locally:
225
+
226
+ ```bash
227
+ git add modified_file.py
228
+ git commit
229
+ ```
230
+
231
+ Note, if you already committed some changes that have a wrong formatting, you can use:
232
+ ```bash
233
+ pre-commit run --all-files
234
+ ```
235
+
236
+ Please write [good commit messages](https://chris.beams.io/posts/git-commit/).
237
+
238
+ It is a good idea to sync your copy of the code with the original
239
+ repository regularly. This way you can quickly account for changes:
240
+
241
+ ```bash
242
+ git fetch upstream
243
+ git rebase upstream/main
244
+ ```
245
+
246
+ Push the changes to your account using:
247
+
248
+ ```bash
249
+ git push -u origin a-descriptive-name-for-my-changes
250
+ ```
251
+
252
+ 6. Once you are satisfied (**and the checklist below is happy too**), go to the
253
+ webpage of your fork on GitHub. Click on 'Pull request' to send your changes
254
+ to the project maintainers for review.
255
+
256
+ 7. It's ok if maintainers ask you for changes. It happens to core contributors
257
+ too! So everyone can see the changes in the Pull request, work in your local
258
+ branch and push the changes to your fork. They will automatically appear in
259
+ the pull request.
260
+
261
+
262
+ ### Checklist
263
+
264
+ 1. The title of your pull request should be a summary of its contribution;
265
+ 2. If your pull request addresses an issue, please mention the issue number in
266
+ the pull request description to make sure they are linked (and people
267
+ consulting the issue know you are working on it);
268
+ 3. To indicate a work in progress please prefix the title with `[WIP]`, or preferably mark
269
+ the PR as a draft PR. These are useful to avoid duplicated work, and to differentiate
270
+ it from PRs ready to be merged;
271
+ 4. Make sure existing tests pass;
272
+
273
+ ### Tests
274
+
275
+ An extensive test suite is included to test the library behavior and several examples. Library tests can be found in the [tests folder](https://github.com/huggingface/lerobot/tree/main/tests).
276
+
277
+ Install [git lfs](https://git-lfs.com/) to retrieve test artifacts (if you don't have it already).
278
+
279
+ On Mac:
280
+ ```bash
281
+ brew install git-lfs
282
+ git lfs install
283
+ ```
284
+
285
+ On Ubuntu:
286
+ ```bash
287
+ sudo apt-get install git-lfs
288
+ git lfs install
289
+ ```
290
+
291
+ Pull artifacts if they're not in [tests/artifacts](tests/artifacts)
292
+ ```bash
293
+ git lfs pull
294
+ ```
295
+
296
+ We use `pytest` in order to run the tests. From the root of the
297
+ repository, here's how to run tests with `pytest` for the library:
298
+
299
+ ```bash
300
+ python -m pytest -sv ./tests
301
+ ```
302
+
303
+
304
+ You can specify a smaller set of tests in order to test only the feature
305
+ you're working on.
lerobot/docs/source/hilserl.mdx ADDED
@@ -0,0 +1,548 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # HIL-SERL Real Robot Training Workflow Guide
2
+
3
+ In this tutorial you will go through the full Human-in-the-Loop Sample-Efficient Reinforcement Learning (HIL-SERL) workflow using LeRobot. You will master training a policy with RL on a real robot in just a few hours.
4
+
5
+ HIL-SERL is a sample-efficient reinforcement learning algorithm that combines human demonstrations with online learning and human interventions. The approach starts from a small set of human demonstrations, uses them to train a reward classifier, and then employs an actor-learner architecture where humans can intervene during policy execution to guide exploration and correct unsafe behaviors. In this tutorial, you'll use a gamepad to provide interventions and control the robot during the learning process.
6
+
7
+ It combines three key ingredients:
8
+ 1. **Offline demonstrations & reward classifier:** a handful of human-teleop episodes plus a vision-based success detector give the policy a shaped starting point.
9
+ 2. **On-robot actor / learner loop with human interventions:** a distributed Soft Actor Critic (SAC) learner updates the policy while an actor explores on the physical robot; the human can jump in at any time to correct dangerous or unproductive behaviour.
10
+ 3. **Safety & efficiency tools:** joint/end-effector (EE) bounds, crop region of interest (ROI) preprocessing and WandB monitoring keep the data useful and the hardware safe.
11
+
12
+ Together these elements let HIL-SERL reach near-perfect task success and faster cycle times than imitation-only baselines.
13
+
14
+ <p align="center">
15
+ <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/hilserl-main-figure.png" alt="HIL-SERL workflow" title="HIL-SERL workflow" width="100%"></img>
16
+ </p>
17
+
18
+ <p align="center"><i>HIL-SERL workflow, Luo et al. 2024</i></p>
19
+
20
+ This guide provides step-by-step instructions for training a robot policy using LeRobot's HilSerl implementation to train on a real robot.
21
+
22
+ ## What do I need?
23
+
24
+ - A gamepad (recommended) or keyboard to control the robot
25
+ - A Nvidia GPU
26
+ - A real robot with a follower and leader arm (optional if you use the keyboard or the gamepad)
27
+ - A URDF file for the robot for the kinematics package (check `lerobot/common/model/kinematics.py`)
28
+
29
+ ## What kind of tasks can I train?
30
+
31
+ One can use HIL-SERL to train on a variety of manipulation tasks. Some recommendations:
32
+ - Start with a simple task to understand how the system works.
33
+ - Push cube to a goal region
34
+ - Pick and lift cube with the gripper
35
+ - Avoid extremely long horizon tasks. Focus on tasks that can be completed in 5-10 seconds.
36
+ - Once you have a good idea of how the system works, you can try more complex tasks and longer horizons.
37
+ - Pick and place cube
38
+ - Bimanual tasks to pick objects with two arms
39
+ - Hand-over tasks to transfer objects from one arm to another
40
+ - Go crazy!
41
+
42
+ ## Install LeRobot with HIL-SERL
43
+
44
+ To install LeRobot with HIL-SERL, you need to install the `hilserl` extra.
45
+
46
+ ```bash
47
+ pip install -e ".[hilserl]"
48
+ ```
49
+
50
+ ## Real Robot Training Workflow
51
+
52
+ ### Understanding Configuration
53
+
54
+ The training process begins with proper configuration for the HILSerl environment. The configuration class of interest is `HILSerlRobotEnvConfig` in `lerobot/envs/configs.py`. Which is defined as:
55
+
56
+ ```python
57
+ class HILSerlRobotEnvConfig(EnvConfig):
58
+ robot: RobotConfig | None = None # Main robot agent (defined in `lerobot/robots`)
59
+ teleop: TeleoperatorConfig | None = None # Teleoperator agent, e.g., gamepad or leader arm, (defined in `lerobot/teleoperators`)
60
+ wrapper: EnvTransformConfig | None = None # Environment wrapper settings; check `lerobot/scripts/server/gym_manipulator.py`
61
+ fps: int = 10 # Control frequency
62
+ name: str = "real_robot" # Environment name
63
+ mode: str = None # "record", "replay", or None (for training)
64
+ repo_id: str | None = None # LeRobot dataset repository ID
65
+ dataset_root: str | None = None # Local dataset root (optional)
66
+ task: str = "" # Task identifier
67
+ num_episodes: int = 10 # Number of episodes for recording
68
+ episode: int = 0 # episode index for replay
69
+ device: str = "cuda" # Compute device
70
+ push_to_hub: bool = True # Whether to push the recorded datasets to Hub
71
+ pretrained_policy_name_or_path: str | None = None # For policy loading
72
+ reward_classifier_pretrained_path: str | None = None # For reward model
73
+ number_of_steps_after_success: int = 0 # For reward classifier, collect more positive examples after a success to train a classifier
74
+ ```
75
+
76
+
77
+ ### Finding Robot Workspace Bounds
78
+
79
+ Before collecting demonstrations, you need to determine the appropriate operational bounds for your robot.
80
+
81
+ This helps simplify the problem of learning on the real robot in two ways: 1) by limiting the robot's operational space to a specific region that solves the task and avoids unnecessary or unsafe exploration, and 2) by allowing training in end-effector space rather than joint space. Empirically, learning in joint space for reinforcement learning in manipulation is often a harder problem - some tasks are nearly impossible to learn in joint space but become learnable when the action space is transformed to end-effector coordinates.
82
+
83
+ **Using find_joint_limits.py**
84
+
85
+ This script helps you find the safe operational bounds for your robot's end-effector. Given that you have a follower and leader arm, you can use the script to find the bounds for the follower arm that will be applied during training.
86
+ Bounding the action space will reduce the redundant exploration of the agent and guarantees safety.
87
+
88
+ ```bash
89
+ python -m lerobot.scripts.find_joint_limits \
90
+ --robot.type=so100_follower \
91
+ --robot.port=/dev/tty.usbmodem58760431541 \
92
+ --robot.id=black \
93
+ --teleop.type=so100_leader \
94
+ --teleop.port=/dev/tty.usbmodem58760431551 \
95
+ --teleop.id=blue
96
+ ```
97
+
98
+ **Workflow**
99
+
100
+ 1. Run the script and move the robot through the space that solves the task
101
+ 2. The script will record the minimum and maximum end-effector positions and the joint angles and prints them to the console, for example:
102
+ ```
103
+ Max ee position [0.2417 0.2012 0.1027]
104
+ Min ee position [0.1663 -0.0823 0.0336]
105
+ Max joint positions [-20.0, -20.0, -20.0, -20.0, -20.0, -20.0]
106
+ Min joint positions [50.0, 50.0, 50.0, 50.0, 50.0, 50.0]
107
+ ```
108
+ 3. Use these values in the configuration of your teleoperation device (TeleoperatorConfig) under the `end_effector_bounds` field
109
+
110
+ **Example Configuration**
111
+
112
+ ```json
113
+ "end_effector_bounds": {
114
+ "max": [0.24, 0.20, 0.10],
115
+ "min": [0.16, -0.08, 0.03]
116
+ }
117
+ ```
118
+
119
+ ### Collecting Demonstrations
120
+
121
+ With the bounds defined, you can safely collect demonstrations for training. Training RL with off-policy algorithm allows us to use offline datasets collected in order to improve the efficiency of the learning process.
122
+
123
+ **Setting Up Record Mode**
124
+
125
+ Create a configuration file for recording demonstrations (or edit an existing one like [env_config_so100.json](https://huggingface.co/datasets/aractingi/lerobot-example-config-files/blob/main/env_config_so100.json)):
126
+
127
+ 1. Set `mode` to `"record"`
128
+ 2. Specify a unique `repo_id` for your dataset (e.g., "username/task_name")
129
+ 3. Set `num_episodes` to the number of demonstrations you want to collect
130
+ 4. Set `crop_params_dict` to `null` initially (we'll determine crops later)
131
+ 5. Configure `robot`, `cameras`, and other hardware settings
132
+
133
+ Example configuration section:
134
+ ```json
135
+ "mode": "record",
136
+ "repo_id": "username/pick_lift_cube",
137
+ "dataset_root": null,
138
+ "task": "pick_and_lift",
139
+ "num_episodes": 15,
140
+ "episode": 0,
141
+ "push_to_hub": true
142
+ ```
143
+
144
+ ### Using a Teleoperation Device
145
+
146
+ Along with your robot, you will need a teleoperation device to control it in order to collect datasets of your task and perform interventions during the online training.
147
+ We support using a gamepad or a keyboard or the leader arm of the robot.
148
+
149
+ HIL-Serl learns actions in the end-effector space of the robot. Therefore, the teleoperation will control the end-effector's x,y,z displacements.
150
+
151
+ For that we need to define a version of the robot that takes actions in the end-effector space. Check the robot class `SO100FollowerEndEffector` and its configuration `SO100FollowerEndEffectorConfig` for the default parameters related to the end-effector space.
152
+
153
+ ```python
154
+ class SO100FollowerEndEffectorConfig(SO100FollowerConfig):
155
+ """Configuration for the SO100FollowerEndEffector robot."""
156
+
157
+ # Default bounds for the end-effector position (in meters)
158
+ end_effector_bounds: dict[str, list[float]] = field( # bounds for the end-effector in x,y,z direction
159
+ default_factory=lambda: {
160
+ "min": [-1.0, -1.0, -1.0], # min x, y, z
161
+ "max": [1.0, 1.0, 1.0], # max x, y, z
162
+ }
163
+ )
164
+
165
+ max_gripper_pos: float = 50 # maximum gripper position that the gripper will be open at
166
+
167
+ end_effector_step_sizes: dict[str, float] = field( # maximum step size for the end-effector in x,y,z direction
168
+ default_factory=lambda: {
169
+ "x": 0.02,
170
+ "y": 0.02,
171
+ "z": 0.02,
172
+ }
173
+ )
174
+ ```
175
+
176
+ The `Teleoperator` defines the teleoperation device. You can check the list of available teleoperators in `lerobot/teleoperators`.
177
+
178
+ **Setting up the Gamepad**
179
+
180
+ The gamepad provides a very convenient way to control the robot and the episode state.
181
+
182
+ To setup the gamepad, you need to set the `control_mode` to `"gamepad"` and define the `teleop` section in the configuration file.
183
+
184
+ ```json
185
+ "teleop": {
186
+ "type": "gamepad",
187
+ "use_gripper": true
188
+ },
189
+ ```
190
+
191
+ <p align="center">
192
+ <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/gamepad_guide.jpg?raw=true" alt="Figure shows the control mappings on a Logitech gamepad." title="Gamepad Control Mapping" width="100%"></img>
193
+ </p>
194
+ <p align="center"><i>Gamepad button mapping for robot control and episode management</i></p>
195
+
196
+ **Setting up the SO101 leader**
197
+
198
+ The SO101 leader arm has reduced gears that allows it to move and track the follower arm during exploration. Therefore, taking over is much smoother than the gearless SO100.
199
+
200
+ To setup the SO101 leader, you need to set the `control_mode` to `"leader"` and define the `teleop` section in the configuration file.
201
+
202
+ ```json
203
+ "teleop": {
204
+ "type": "so101_leader",
205
+ "port": "/dev/tty.usbmodem585A0077921", # check your port number
206
+ "use_degrees": true
207
+ },
208
+ ```
209
+
210
+ In order to annotate the success/failure of the episode, **you will need** to use a keyboard to press `s` for success, `esc` for failure.
211
+ During the online training, press `space` to take over the policy and `space` again to give the control back to the policy.
212
+
213
+ <details>
214
+ <summary><strong>Video: SO101 leader teleoperation</strong></summary>
215
+
216
+ <div class="video-container">
217
+ <video controls width="600">
218
+ <source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so101_leader_tutorial.mp4" type="video/mp4" />
219
+ </video>
220
+ </div>
221
+
222
+ <p align="center"><i>SO101 leader teleoperation example, the leader tracks the follower, press `space` to intervene</i></p>
223
+ </details>
224
+
225
+ **Recording Demonstrations**
226
+
227
+ Start the recording process, an example of the config file can be found [here](https://huggingface.co/datasets/aractingi/lerobot-example-config-files/blob/main/env_config_so100.json):
228
+
229
+ ```bash
230
+ python -m lerobot.scripts.rl.gym_manipulator --config_path src/lerobot/configs/env_config_so100.json
231
+ ```
232
+
233
+ During recording:
234
+ 1. The robot will reset to the initial position defined in the configuration file `fixed_reset_joint_positions`
235
+ 2. Complete the task successfully
236
+ 3. The episode ends with a reward of 1 when you press the "success" button
237
+ 4. If the time limit is reached, or the fail button is pressed, the episode ends with a reward of 0
238
+ 5. You can rerecord an episode by pressing the "rerecord" button
239
+ 6. The process automatically continues to the next episode
240
+ 7. After recording all episodes, the dataset is pushed to the Hugging Face Hub (optional) and saved locally
241
+
242
+
243
+ ### Processing the Dataset
244
+
245
+ After collecting demonstrations, process them to determine optimal camera crops.
246
+ Reinforcement learning is sensitive to background distractions, so it is important to crop the images to the relevant workspace area.
247
+
248
+ Visual RL algorithms learn directly from pixel inputs, making them vulnerable to irrelevant visual information. Background elements like changing lighting, shadows, people moving, or objects outside the workspace can confuse the learning process. Good ROI selection should:
249
+ - Include only the essential workspace where the task happens
250
+ - Capture the robot's end-effector and all objects involved in the task
251
+ - Exclude unnecessary background elements and distractions
252
+
253
+ Note: If you already know the crop parameters, you can skip this step and just set the `crop_params_dict` in the configuration file during recording.
254
+
255
+ **Determining Crop Parameters**
256
+
257
+ Use the `crop_dataset_roi.py` script to interactively select regions of interest in your camera images:
258
+
259
+ ```bash
260
+ python -m lerobot.scripts.rl.crop_dataset_roi --repo-id username/pick_lift_cube
261
+ ```
262
+
263
+ 1. For each camera view, the script will display the first frame
264
+ 2. Draw a rectangle around the relevant workspace area
265
+ 3. Press 'c' to confirm the selection
266
+ 4. Repeat for all camera views
267
+ 5. The script outputs cropping parameters and creates a new cropped dataset
268
+
269
+ Example output:
270
+ ```
271
+ Selected Rectangular Regions of Interest (top, left, height, width):
272
+ observation.images.side: [180, 207, 180, 200]
273
+ observation.images.front: [180, 250, 120, 150]
274
+ ```
275
+
276
+ <p align="center">
277
+ <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/crop_dataset.gif" width="600"/>
278
+ </p>
279
+
280
+ <p align="center"><i>Interactive cropping tool for selecting regions of interest</i></p>
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+
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+
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+ **Updating Configuration**
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+
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+ Add these crop parameters to your training configuration:
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+
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+ ```json
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+ "crop_params_dict": {
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+ "observation.images.side": [180, 207, 180, 200],
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+ "observation.images.front": [180, 250, 120, 150]
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+ },
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+ "resize_size": [128, 128]
293
+ ```
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+
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+ **Recommended image resolution**
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+
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+ Most vision-based policies have been validated on square inputs of either **128×128** (default) or **64×64** pixels. We therefore advise setting the resize_size parameter to [128, 128] – or [64, 64] if you need to save GPU memory and bandwidth. Other resolutions are possible but have not been extensively tested.
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+
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+
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+ ### Training a Reward Classifier
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+
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+ The reward classifier plays an important role in the HIL-SERL workflow by automating reward assignment and automatically detecting episode success. Instead of manually defining reward functions or relying on human feedback for every timestep, the reward classifier learns to predict success/failure from visual observations. This enables the RL algorithm to learn efficiently by providing consistent and automated reward signals based on the robot's camera inputs.
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+
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+ This guide explains how to train a reward classifier for human-in-the-loop reinforcement learning implementation of LeRobot. Reward classifiers learn to predict the reward value given a state which can be used in an RL setup to train a policy.
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+
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+ **Note**: Training a reward classifier is optional. You can start the first round of RL experiments by annotating the success manually with your gamepad or keyboard device.
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+
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+ The reward classifier implementation in `modeling_classifier.py` uses a pretrained vision model to process the images. It can output either a single value for binary rewards to predict success/fail cases or multiple values for multi-class settings.
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+
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+ **Collecting a Dataset for the reward classifier**
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+
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+ Before training, you need to collect a dataset with labeled examples. The `record_dataset` function in `gym_manipulator.py` enables the process of collecting a dataset of observations, actions, and rewards.
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+
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+ To collect a dataset, you need to modify some parameters in the environment configuration based on HILSerlRobotEnvConfig.
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+
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+ ```bash
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+ python -m lerobot.scripts.rl.gym_manipulator --config_path src/lerobot/configs/reward_classifier_train_config.json
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+ ```
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+
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+ **Key Parameters for Data Collection**
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+
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+ - **mode**: set it to `"record"` to collect a dataset
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+ - **repo_id**: `"hf_username/dataset_name"`, name of the dataset and repo on the hub
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+ - **num_episodes**: Number of episodes to record
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+ - **number_of_steps_after_success**: Number of additional frames to record after a success (reward=1) is detected
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+ - **fps**: Number of frames per second to record
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+ - **push_to_hub**: Whether to push the dataset to the hub
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+
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+ The `number_of_steps_after_success` parameter is crucial as it allows you to collect more positive examples. When a success is detected, the system will continue recording for the specified number of steps while maintaining the reward=1 label. Otherwise, there won't be enough states in the dataset labeled to 1 to train a good classifier.
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+
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+ Example configuration section for data collection:
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+
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+ ```json
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+ {
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+ "mode": "record",
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+ "repo_id": "hf_username/dataset_name",
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+ "dataset_root": "data/your_dataset",
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+ "num_episodes": 20,
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+ "push_to_hub": true,
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+ "fps": 10,
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+ "number_of_steps_after_success": 15
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+ }
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+ ```
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+
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+ **Reward Classifier Configuration**
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+
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+ The reward classifier is configured using `configuration_classifier.py`. Here are the key parameters:
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+
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+ - **model_name**: Base model architecture (e.g., we mainly use `"helper2424/resnet10"`)
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+ - **model_type**: `"cnn"` or `"transformer"`
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+ - **num_cameras**: Number of camera inputs
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+ - **num_classes**: Number of output classes (typically 2 for binary success/failure)
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+ - **hidden_dim**: Size of hidden representation
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+ - **dropout_rate**: Regularization parameter
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+ - **learning_rate**: Learning rate for optimizer
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+
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+ Example configuration for training the [reward classifier](https://huggingface.co/datasets/aractingi/lerobot-example-config-files/blob/main/reward_classifier_train_config.json):
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+
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+ ```json
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+ {
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+ "policy": {
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+ "type": "reward_classifier",
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+ "model_name": "helper2424/resnet10",
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+ "model_type": "cnn",
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+ "num_cameras": 2,
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+ "num_classes": 2,
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+ "hidden_dim": 256,
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+ "dropout_rate": 0.1,
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+ "learning_rate": 1e-4,
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+ "device": "cuda",
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+ "use_amp": true,
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+ "input_features": {
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+ "observation.images.front": {
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+ "type": "VISUAL",
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+ "shape": [3, 128, 128]
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+ },
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+ "observation.images.side": {
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+ "type": "VISUAL",
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+ "shape": [3, 128, 128]
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+ }
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+ }
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+ }
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+ }
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+ ```
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+
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+ **Training the Classifier**
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+
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+ To train the classifier, use the `train.py` script with your configuration:
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+
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+ ```bash
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+ python -m lerobot.scripts.train --config_path path/to/reward_classifier_train_config.json
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+ ```
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+
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+ **Deploying and Testing the Model**
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+
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+ To use your trained reward classifier, configure the `HILSerlRobotEnvConfig` to use your model:
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+
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+ ```python
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+ env_config = HILSerlRobotEnvConfig(
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+ reward_classifier_pretrained_path="path_to_your_pretrained_trained_model",
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+ # Other environment parameters
402
+ )
403
+ ```
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+ or set the argument in the json config file.
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+
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+ ```json
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+ {
408
+ "reward_classifier_pretrained_path": "path_to_your_pretrained_model"
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+ }
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+ ```
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+
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+ Run `gym_manipulator.py` to test the model.
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+ ```bash
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+ python -m lerobot.scripts.rl.gym_manipulator --config_path path/to/env_config.json
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+ ```
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+
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+ The reward classifier will automatically provide rewards based on the visual input from the robot's cameras.
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+
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+ **Example Workflow for training the reward classifier**
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+
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+ 1. **Create the configuration files**:
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+ Create the necessary json configuration files for the reward classifier and the environment. Check the examples [here](https://huggingface.co/datasets/aractingi/lerobot-example-config-files/tree/main).
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+
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+ 2. **Collect a dataset**:
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+ ```bash
426
+ python -m lerobot.scripts.rl.gym_manipulator --config_path src/lerobot/configs/env_config.json
427
+ ```
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+
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+ 3. **Train the classifier**:
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+ ```bash
431
+ python -m lerobot.scripts.train --config_path src/lerobot/configs/reward_classifier_train_config.json
432
+ ```
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+
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+ 4. **Test the classifier**:
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+ ```bash
436
+ python -m lerobot.scripts.rl.gym_manipulator --config_path src/lerobot/configs/env_config.json
437
+ ```
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+
439
+ ### Training with Actor-Learner
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+
441
+ The LeRobot system uses a distributed actor-learner architecture for training. This architecture decouples robot interactions from the learning process, allowing them to run concurrently without blocking each other. The actor server handles robot observations and actions, sending interaction data to the learner server. The learner server performs gradient descent and periodically updates the actor's policy weights. You will need to start two processes: a learner and an actor.
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+
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+ **Configuration Setup**
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+
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+ Create a training configuration file (example available [here](https://huggingface.co/datasets/aractingi/lerobot-example-config-files/blob/main/train_config_hilserl_so100.json)). The training config is based on the main `TrainRLServerPipelineConfig` class in `lerobot/configs/train.py`.
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+
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+ 1. Configure the policy settings (`type="sac"`, `device`, etc.)
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+ 2. Set `dataset` to your cropped dataset
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+ 3. Configure environment settings with crop parameters
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+ 4. Check the other parameters related to SAC in [configuration_sac.py](https://github.com/huggingface/lerobot/blob/19bb621a7d0a31c20cd3cc08b1dbab68d3031454/lerobot/policies/sac/configuration_sac.py#L79).
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+ 5. Verify that the `policy` config is correct with the right `input_features` and `output_features` for your task.
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+
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+ **Starting the Learner**
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+
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+ First, start the learner server process:
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+
457
+ ```bash
458
+ python -m lerobot.scripts.rl.learner --config_path src/lerobot/configs/train_config_hilserl_so100.json
459
+ ```
460
+
461
+ The learner:
462
+ - Initializes the policy network
463
+ - Prepares replay buffers
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+ - Opens a `gRPC` server to communicate with actors
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+ - Processes transitions and updates the policy
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+
467
+ **Starting the Actor**
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+
469
+ In a separate terminal, start the actor process with the same configuration:
470
+
471
+ ```bash
472
+ python -m lerobot.scripts.rl.actor --config_path src/lerobot/configs/train_config_hilserl_so100.json
473
+ ```
474
+
475
+ The actor:
476
+ - Connects to the learner via `gRPC`
477
+ - Initializes the environment
478
+ - Execute rollouts of the policy to collect experience
479
+ - Sends transitions to the learner
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+ - Receives updated policy parameters
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+
482
+ **Training Flow**
483
+
484
+ The training proceeds automatically:
485
+
486
+ 1. The actor executes the policy in the environment
487
+ 2. Transitions are collected and sent to the learner
488
+ 3. The learner updates the policy based on these transitions
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+ 4. Updated policy parameters are sent back to the actor
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+ 5. The process continues until the specified step limit is reached
491
+
492
+ **Human in the Loop**
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+
494
+ - The key to learning efficiently is to have human interventions to provide corrective feedback and completing the task to aide the policy learning and exploration.
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+ - To perform human interventions, you can press the upper right trigger button on the gamepad (or the `space` key on the keyboard). This will pause the policy actions and allow you to take over.
496
+ - A successful experiment is one where the human has to intervene at the start but then reduces the amount of interventions as the policy improves. You can monitor the intervention rate in the `wandb` dashboard.
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+
498
+ <p align="center">
499
+ <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/hil_effect.png?raw=true" alt="Figure shows the control mappings on a Logitech gamepad." title="Gamepad Control Mapping" width="100%"></img>
500
+ </p>
501
+
502
+ <p align="center"><i>Example showing how human interventions help guide policy learning over time</i></p>
503
+
504
+ - The figure shows the plot of the episodic reward over interaction step. The figure shows the effect of human interventions on the policy learning.
505
+ - The orange curve is an experiment without any human interventions. While the pink and blue curves are experiments with human interventions.
506
+ - We can observe that the number of steps where the policy starts achieving the maximum reward is cut by a quarter when human interventions are present.
507
+
508
+ **Monitoring and Debugging**
509
+
510
+ If you have `wandb.enable` set to `true` in your configuration, you can monitor training progress in real-time through the [Weights & Biases](https://wandb.ai/site/) dashboard.
511
+
512
+ ### Guide to Human Interventions
513
+ The learning process is very sensitive to the intervention strategy. It will takes a few runs to understand how to intervene effectively. Some tips and hints:
514
+ - Allow the policy to explore for a few episodes at the start of training.
515
+ - Avoid intervening for long periods of time. Try to intervene in situation to correct the robot's behaviour when it goes off track.
516
+ - Once the policy starts achieving the task, even if its not perfect, you can limit your interventions to simple quick actions like a simple grasping commands.
517
+
518
+ The ideal behaviour is that your intervention rate should drop gradually during training as shown in the figure below.
519
+
520
+ <p align="center">
521
+ <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/intervention_rate_tutorial_rl.png?raw=true" alt="Intervention rate" title="Intervention rate during training" width="100%"></img>
522
+ </p>
523
+
524
+ <p align="center"><i>Plot of the intervention rate during a training run on a pick and lift cube task</i></p>
525
+
526
+ ### Key hyperparameters to tune
527
+
528
+ Some configuration values have a disproportionate impact on training stability and speed:
529
+
530
+ - **`temperature_init`** (`policy.temperature_init`) – initial entropy temperature in SAC. Higher values encourage more exploration; lower values make the policy more deterministic early on. A good starting point is `1e-2`. We observed that setting it too high can make human interventions ineffective and slow down learning.
531
+ - **`policy_parameters_push_frequency`** (`policy.actor_learner_config.policy_parameters_push_frequency`) – interval in *seconds* between two weight pushes from the learner to the actor. The default is `4 s`. Decrease to **1-2 s** to provide fresher weights (at the cost of more network traffic); increase only if your connection is slow, as this will reduce sample efficiency.
532
+ - **`storage_device`** (`policy.storage_device`) – device on which the learner keeps the policy parameters. If you have spare GPU memory, set this to `"cuda"` (instead of the default `"cpu"`). Keeping the weights on-GPU removes CPU→GPU transfer overhead and can significantly increase the number of learner updates per second.
533
+
534
+
535
+ Congrats 🎉, you have finished this tutorial!
536
+
537
+ > [!TIP]
538
+ > If you have any questions or need help, please reach out on [Discord](https://discord.com/invite/s3KuuzsPFb).
539
+
540
+ Paper citation:
541
+ ```
542
+ @article{luo2024precise,
543
+ title={Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning},
544
+ author={Luo, Jianlan and Xu, Charles and Wu, Jeffrey and Levine, Sergey},
545
+ journal={arXiv preprint arXiv:2410.21845},
546
+ year={2024}
547
+ }
548
+ ```