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---
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---
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title: Project-IRA
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emoji: 🦾
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short_description: Vision-Language-Action policies for LeRobot SO101
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---
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# Project-IRA — Interactive Robotic Arm
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Vision-Language-Action policies and multi-task manipulation datasets for the
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**SO-101** low-cost robot arm, built on [LeRobot](https://github.com/huggingface/lerobot).
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**Code:** https://github.com/Project-IRA/interactive-robotic-arm — the repositories here and that repo depend on each other; the code
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repo contains the recording, validation, merging, training and inference pipeline that
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produced everything in this organization.
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**Authors:** Cleo Baten, Bela Keppler, Jonas Sapper
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---
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## Start here
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If you just want a policy that works on an SO-101:
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> **[`Project-IRA/TPSoSe2026_Pi05_LeRobot_SO101_Finetuning_V7_Full_V2`](https://huggingface.co/Project-IRA/TPSoSe2026_Pi05_LeRobot_SO101_Finetuning_V7_Full_V2)** — checkpoint **008000**
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This is the strongest model in the org: Pi0.5 fine-tuned on all four tasks, with image
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augmentation. Note its unusual internal directory layout, documented on its card.
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## Demo
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<video controls src="https://huggingface.co/Project-IRA/TPSoSe2026_Pi05_LeRobot_SO101_Finetuning_V7_Full_V2/resolve/main/assets/pi05_lego_demo.mp4"></video>
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Pi0.5 sorting lego bricks onto colour-matched plates on the physical SO-101.
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## Tasks
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| Task | What the arm does |
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|---|---|
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| **Sort Lego Color** | Sort lego bricks onto colour-matched plates |
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| **Desk Cleanup** | Clear objects from the desk into a bowl |
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| **Dice Throw** | Pick up a dice cup and tip the dice onto the table ("true random") |
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| **Fetch Ball** | Pick up a ball and place it into a waiting human hand |
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## Models
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Quality ratings are qualitative operator assessments from rollouts on the physical arm.
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There are no formal success-rate numbers.
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| Model | Base | Trained on | Best checkpoint | Quality |
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|---|---|---|---|---|
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| [Pi05 V7 Full V2](https://huggingface.co/Project-IRA/TPSoSe2026_Pi05_LeRobot_SO101_Finetuning_V7_Full_V2) | Pi0.5 | all 4 tasks | **008000** | **Best overall** — works really well |
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| [Pi05 V7 Full](https://huggingface.co/Project-IRA/TPSoSe2026_Pi05_LeRobot_SO101_Finetuning_V7_Full) | Pi0.5 | all 4 tasks | **010000** | Works really well |
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| [Pi05 V4 Lego](https://huggingface.co/Project-IRA/TPSoSe2026_Pi05_LeRobot_SO101_Finetuning_V4_Lego) | Pi0.5 | Lego only | **030000** | Works really well (Lego only) |
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| [SmolVLA V6 Full](https://huggingface.co/Project-IRA/TPSoSe2026_SmolVLA_LeRobot_SO101_Finetuning_V6_Full) | SmolVLA | all 4 tasks | ~150000 (untested) | OK-ish, across most tasks |
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| [SmolVLA V5 Full](https://huggingface.co/Project-IRA/TPSoSe2026_SmolVLA_LeRobot_SO101_Finetuning_V5_Full) | SmolVLA | all 4 tasks | **100000** | Works, but worse than V6 |
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| [SmolVLA V2 Lego](https://huggingface.co/Project-IRA/TPSoSe2026_SmolVLA_LeRobot_SO101_Finetuning_V2_Lego) | SmolVLA | Lego only | **030000** | OK-ish (Lego only) |
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| [SmolVLA V3 Desk Cleanup](https://huggingface.co/Project-IRA/TPSoSe2026_SmolVLA_LeRobot_SO101_Finetuning_V3_Desk_Cleanup) | SmolVLA | Desk Cleanup only | — | Does not work |
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| [SmolVLA V1 Misc](https://huggingface.co/Project-IRA/TPSoSe2026_SmolVLA_LeRobot_SO101_Finetuning_V1_Misc_Dataset) | SmolVLA | misc early data | — | Does not work |
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**The headline result:** Pi0.5 substantially outperforms SmolVLA on this setup. The
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~450M SmolVLA appears capacity-limited across four tasks, while Pi0.5 handles the full
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multi-task set well — and needs far fewer steps to get there (8k–10k vs 100k–200k).
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## Datasets
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| Dataset | Episodes | Content |
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| [Full Merged Final V1](https://huggingface.co/datasets/Project-IRA/TPSoSe2026_Dataset_Full_Merged_Final_LeRobot_SO101_V1) | **930** | The training set. All 4 tasks, 93 prompts, 844,208 frames |
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| [Lego](https://huggingface.co/datasets/Project-IRA/TPSoSe2026_Dataset_Lego_LeRobot_SO101) | 460 | Colour sorting |
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| [Desk Cleanup](https://huggingface.co/datasets/Project-IRA/TPSoSe2026_Dataset_Desk_Cleanup_LeRobot_SO101) | 350 | Desk clearing (250 of these went into the merge) |
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| [Dice Throw](https://huggingface.co/datasets/Project-IRA/TPSoSe2026_Dataset_Dice_Throw_LeRobot_SO101) | 120 | Dice cup |
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| [Fetch Ball](https://huggingface.co/datasets/Project-IRA/TPSoSe2026_Dataset_Fetch_Ball_LeRobot_SO101) | 100 | Ball handover |
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| [Collection](https://huggingface.co/datasets/Project-IRA/TPSoSe2026_Dataset_Collection_LeRobot_SO101) | — | Early miscellaneous data; superseded |
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## Robot setup
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| Robot | SO-101 follower arm (6-DOF), `robot_type: so_follower` |
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| Teleoperation | SO-101 leader arm |
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| Control frequency | 30 fps |
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| State / action space | 6-dim: `shoulder_pan.pos`, `shoulder_lift.pos`, `elbow_flex.pos`, `wrist_flex.pos`, `wrist_roll.pos`, `gripper.pos` |
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| Camera `observation.images.desk_view` | 800x600, h264 (recording) |
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| Camera `observation.images.wrist_left` | 640x480, h264 (recording) |
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> **Inference note:** both cameras are run at **640x480 during inference**, not at their
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> recording resolutions, to reduce the payload sent to the inference server.
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## Prompt design
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Language conditioning was varied deliberately: the prompt phrasing was changed roughly
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every 10 episodes during recording. The merged dataset therefore contains **93 distinct
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prompts across 10 episodes each**, all in English. Full prompt lists are on each dataset card.
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## Licensing
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All models and datasets here are **CC BY-SA 4.0** — share-alike, so derivatives must stay
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open. Upstream LeRobot, SmolVLA and Pi0.5/openpi components are Apache-2.0; their
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copyright notices and licence text are retained as Apache-2.0 Section 4 requires.
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## Citation
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```bibtex
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@misc{project_ira_2026,
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title = {Project-IRA: Interactive Robotic Arm},
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author = {Baten, Cleo and Keppler, Bela and Sapper, Jonas},
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year = {2026},
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howpublished = {\url{https://huggingface.co/Project-IRA}},
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note = {Code: \url{https://github.com/Project-IRA/interactive-robotic-arm}}
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}
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```
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