Instructions to use maskjp/piper_pick_f15 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LeRobot
How to use maskjp/piper_pick_f15 with LeRobot:
- Notebooks
- Google Colab
- Kaggle
Upload policy weights, train config and readme
Browse files- README.md +12 -4
- config.json +55 -26
- model.safetensors +2 -2
- train_config.json +70 -35
README.md
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datasets: maskjp/piper_pick_f15
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library_name: lerobot
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license: apache-2.0
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model_name:
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pipeline_tag: robotics
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tags:
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- robotics
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- lerobot
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---
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# Model Card for
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<!-- Provide a quick summary of what the model is/does. -->
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This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
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datasets: maskjp/piper_pick_f15
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library_name: lerobot
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license: apache-2.0
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model_name: pi05
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pipeline_tag: robotics
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tags:
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- robotics
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- lerobot
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- pi05
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---
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# Model Card for pi05
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<!-- Provide a quick summary of what the model is/does. -->
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**π₀.₅ (Pi05) Policy**
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π₀.₅ is a Vision-Language-Action model with open-world generalization, from Physical Intelligence. The LeRobot implementation is adapted from their open source OpenPI repository.
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**Model Overview**
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π₀.₅ represents a significant evolution from π₀, developed by Physical Intelligence to address a big challenge in robotics: open-world generalization. While robots can perform impressive tasks in controlled environments, π₀.₅ is designed to generalize to entirely new environments and situations that were never seen during training.
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For more details, see the [Physical Intelligence π₀.₅ blog post](https://www.physicalintelligence.company/blog/pi05).
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This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
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config.json
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"n_obs_steps": 1,
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"input_features": {
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"normalization_mapping": {
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"optimizer_lr": 1e-05,
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"optimizer_weight_decay": 0.0001,
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"optimizer_lr_backbone": 1e-05
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{
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"type": "pi05",
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"pretrained_path": "lerobot/pi05_base",
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"time_sampling_beta_alpha": 1.5,
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"time_sampling_scale": 0.999,
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"time_sampling_offset": 0.001,
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"min_period": 0.004,
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"max_period": 4.0,
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"use_relative_actions": false,
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"gripper"
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],
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"action_feature_names": [
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"joint1.pos",
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"joint2.pos",
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"joint3.pos",
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"joint4.pos",
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"joint5.pos",
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"joint6.pos",
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"gripper.pos"
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],
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"rtc_config": null,
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"image_resolution": [
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224,
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],
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"empty_cameras": 0,
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"tokenizer_max_length": 200,
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"normalization_mapping": {
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"VISUAL": "IDENTITY",
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"STATE": "QUANTILES",
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"ACTION": "QUANTILES"
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"gradient_checkpointing": true,
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"compile_model": true,
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"compile_mode": "max-autotune",
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"freeze_vision_encoder": false,
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"train_expert_only": false,
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"optimizer_lr": 2.5e-05,
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"optimizer_betas": [
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],
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"optimizer_eps": 1e-08,
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"optimizer_weight_decay": 0.01,
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"optimizer_grad_clip_norm": 1.0,
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"scheduler_warmup_steps": 1000,
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"scheduler_decay_steps": 30000,
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"scheduler_decay_lr": 2.5e-06
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}
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model.safetensors
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size
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size 9354050752
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train_config.json
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},
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"env": null,
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"policy": {
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"observation.state": {
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"use_policy_training_preset": true,
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"optimizer": {
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"eval": {
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"pretrained_path": "lerobot/pi05_base",
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"action_expert_variant": "gemma_300m",
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"action_feature_names": [
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"joint2.pos",
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"joint3.pos",
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"joint4.pos",
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"joint5.pos",
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"joint6.pos",
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],
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"output_dir": "outputs/piper_pick_f15",
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"scheduler": {
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"type": "cosine_decay_with_warmup",
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"num_warmup_steps": 1000,
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"num_decay_steps": 30000,
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"peak_lr": 2.5e-05,
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},
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