Robotics
LeRobot
Safetensors
diffusion
anl139 commited on
Commit
929e54e
·
verified ·
1 Parent(s): 6b0cc1d

Upload policy weights, train config and readme

Browse files
Files changed (4) hide show
  1. README.md +5 -5
  2. config.json +45 -25
  3. model.safetensors +2 -2
  4. train_config.json +59 -35
README.md CHANGED
@@ -1,21 +1,21 @@
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  ---
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- datasets: anl139/record-Pastery60
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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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  - lerobot
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  - robotics
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- - act
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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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  ---
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+ datasets: anl139/record-Pastery50
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  library_name: lerobot
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  license: apache-2.0
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+ model_name: diffusion
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  pipeline_tag: robotics
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  tags:
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  - lerobot
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  - robotics
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+ - diffusion
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  ---
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+ # Model Card for diffusion
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  <!-- Provide a quick summary of what the model is/does. -->
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+ [Diffusion Policy](https://huggingface.co/papers/2303.04137) treats visuomotor control as a generative diffusion process, producing smooth, multi-step action trajectories that excel at contact-rich manipulation.
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  This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
config.json CHANGED
@@ -1,10 +1,10 @@
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  {
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- "type": "act",
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- "n_obs_steps": 1,
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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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  "input_features": {
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  "observation.state": {
@@ -31,31 +31,51 @@
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  }
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  },
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  "device": "cuda",
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- "use_amp": true,
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  "push_to_hub": true,
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  "repo_id": "anl139/my_policy",
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  "private": null,
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  "tags": null,
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  "license": null,
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- "chunk_size": 100,
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- "n_action_steps": 100,
 
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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": 0,
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- "pre_norm": false,
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- "dim_model": 512,
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- "n_heads": 8,
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- "dim_feedforward": 3200,
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- "feedforward_activation": "relu",
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- "n_encoder_layers": 4,
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- "n_decoder_layers": 1,
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- "use_vae": true,
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- "latent_dim": 32,
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- "n_vae_encoder_layers": 4,
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- "temporal_ensemble_coeff": null,
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- "dropout": 0.1,
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- "kl_weight": 1.0,
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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": "diffusion",
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  "normalization_mapping": {
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  "VISUAL": "MEAN_STD",
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+ "STATE": "MIN_MAX",
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+ "ACTION": "MIN_MAX"
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  },
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  "input_features": {
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  "observation.state": {
 
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  }
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  "device": "cuda",
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  "push_to_hub": true,
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  "repo_id": "anl139/my_policy",
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  "private": null,
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  "tags": null,
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  "vision_backbone": "resnet18",
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+ "diffusion_step_embed_dim": 128,
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+ "use_film_scale_modulation": true,
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+ "noise_scheduler_type": "DDPM",
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+ "num_train_timesteps": 100,
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+ "optimizer_eps": 1e-08,
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+ "optimizer_weight_decay": 1e-06,
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+ "scheduler_name": "cosine",
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+ "scheduler_warmup_steps": 500
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  }
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train_config.json CHANGED
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  {
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  "root": null,
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  "image_transforms": {
@@ -66,12 +66,12 @@
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  "policy": {
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- "type": "act",
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- "n_obs_steps": 1,
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  "normalization_mapping": {
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  "VISUAL": "MEAN_STD",
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- "STATE": "MEAN_STD",
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  "input_features": {
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  "observation.state": {
@@ -98,58 +98,82 @@
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  }
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  "device": "cuda",
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  "vision_backbone": "resnet18",
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- "optimizer_lr_backbone": 1e-05
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  },
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- "output_dir": "outputs/train/act_so10t",
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- "job_name": "act_so100_test",
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  "resume": true,
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  "seed": 1000,
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  "num_workers": 4,
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  "eval_freq": 2000,
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  "save_checkpoint": true,
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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,
@@ -161,7 +185,7 @@
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  "observation.state": {
 
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