Robotics
LeRobot
Safetensors
pi05
maskjp commited on
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Upload policy weights, train config and readme

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Files changed (4) hide show
  1. README.md +12 -4
  2. config.json +55 -26
  3. model.safetensors +2 -2
  4. train_config.json +70 -35
README.md CHANGED
@@ -2,20 +2,28 @@
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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: act
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  pipeline_tag: robotics
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  tags:
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  - robotics
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- - act
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  - lerobot
 
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  ---
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- # Model Card for act
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  <!-- Provide a quick summary of what the model is/does. -->
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- [Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates.
 
 
 
 
 
 
 
 
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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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+
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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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+
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+ **Model Overview**
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+
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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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+
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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).
config.json CHANGED
@@ -1,5 +1,5 @@
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  {
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- "type": "act",
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  "n_obs_steps": 1,
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  "input_features": {
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  "observation.state": {
@@ -49,31 +49,60 @@
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  "private": null,
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  "tags": null,
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  "license": null,
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- "pretrained_path": null,
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- "chunk_size": 100,
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- "n_action_steps": 100,
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  "normalization_mapping": {
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- "VISUAL": "MEAN_STD",
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- "STATE": "MEAN_STD",
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- "ACTION": "MEAN_STD"
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  },
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- "vision_backbone": "resnet18",
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- "pretrained_backbone_weights": "ResNet18_Weights.IMAGENET1K_V1",
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- "replace_final_stride_with_dilation": false,
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- "use_vae": true,
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- "latent_dim": 32,
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- "dropout": 0.1,
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- "kl_weight": 10.0,
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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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  {
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+ "type": "pi05",
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  "n_obs_steps": 1,
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  "input_features": {
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  "observation.state": {
 
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  "private": null,
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  "tags": null,
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  "license": null,
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+ "pretrained_path": "lerobot/pi05_base",
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+ "paligemma_variant": "gemma_2b",
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+ "action_expert_variant": "gemma_300m",
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+ "dtype": "bfloat16",
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+ "relative_exclude_joints": [
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+ "gripper"
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+ "joint1.pos",
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+ "joint2.pos",
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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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  },
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+ "gradient_checkpointing": true,
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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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train_config.json CHANGED
@@ -81,7 +81,7 @@
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  },
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  "env": null,
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  "policy": {
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- "type": "act",
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  "n_obs_steps": 1,
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  "input_features": {
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  "observation.state": {
@@ -131,42 +131,71 @@
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  "private": null,
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  "normalization_mapping": {
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- "VISUAL": "MEAN_STD",
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- "STATE": "MEAN_STD",
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- "ACTION": "MEAN_STD"
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- "vision_backbone": "resnet18",
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  },
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- "output_dir": "outputs/train/piper_pick_f15",
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  "tolerance_s": 0.0001,
@@ -175,16 +204,22 @@
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  "use_policy_training_preset": true,
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  "optimizer": {
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  "eval": {
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  "n_episodes": 50,
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  "batch_size": 50,
@@ -196,7 +231,7 @@
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  "project": "lerobot",
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  "mode": null,
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  "add_tags": true
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  },
 
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  },
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  "env": null,
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  "policy": {
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+ "type": "pi05",
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  "n_obs_steps": 1,
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  "input_features": {
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  "observation.state": {
 
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  "private": null,
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