Update model & configs
Browse files- README.md +28 -20
- config.json +52 -41
- config.yaml +0 -187
- eval_info.json +691 -691
- eval_pc_success.csv +0 -11
- model.safetensors +1 -1
- train_config.json +207 -0
- train_loss.csv +0 -401
- training_curves.png +0 -0
README.md
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license: apache-2.0
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datasets:
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- lerobot/aloha_sim_transfer_cube_human
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---
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# Model Card for ACT/AlohaTransferCube
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Trained with [LeRobot@d747195](https://github.com/huggingface/lerobot/tree/d747195c5733c4f68d4bfbe62632d6fc1b605712).
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The model was trained using [LeRobot's training script](https://github.com/huggingface/lerobot/blob/
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```bash
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python lerobot/scripts/train.py \
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dataset_repo_id=lerobot/aloha_sim_transfer_cube_human \
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training.eval_freq=10000 \
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training.log_freq=250 \
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training.offline_steps=100000 \
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training.save_model=true \
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training.save_freq=25000 \
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eval.n_episodes=50 \
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eval.batch_size=50 \
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wandb.enable=true \
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device=cuda
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```
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The training curves may be found at https://wandb.ai/
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This took about
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## Evaluation
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Here are the success rate results for 500 episodes worth of evaluation. The first row is the naive mean. The second row assumes a uniform prior and computes the beta posterior, then calculates the mean and lower/upper confidence bounds (with a 68.2% confidence interval centered on the mean). The "Theirs" column is for an equivalent model trained on the original ACT repository and evaluated on LeRobot (the model weights may be found in the [`original_act_repo`](https://huggingface.co/lerobot/act_aloha_sim_transfer_cube_human/tree/original_act_repo) branch of this respository). The results of each of the individual rollouts may be found in [eval_info.json](eval_info.json).
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<blank>|Ours|Theirs
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Success rate for 500 episodes (%) | 87.6 | 68.0
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Beta distribution lower/mean/upper (%) | 86.0 / 87.5 / 88.9 | 65.8 / 67.9 / 70.0
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The original code was heavily refactored, and some bugs were spotted along the way. The differences in code may account for the difference in success rate. Another possibility is that our simulation environment may use slightly different heuristics to evaluate success (we've observed that success is registered as soon as the second arm's gripper makes antipodal contact with the cube). Finally, one should observe that the in-training evaluation jumps up towards the end of training. This may need further investigation (Is it statistically significant? If so, what is the cause?).
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license: apache-2.0
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datasets:
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- lerobot/aloha_sim_transfer_cube_human
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tags:
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- act-policy
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- model_hub_mixin
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- pytorch_model_hub_mixin
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- robotics
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pipeline_tag: robotics
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---
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# Model Card for ACT/AlohaTransferCube
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Trained with [LeRobot@d747195](https://github.com/huggingface/lerobot/tree/d747195c5733c4f68d4bfbe62632d6fc1b605712).
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The model was trained using [LeRobot's training script](https://github.com/huggingface/lerobot/blob/main/lerobot/scripts/train.py) and with the [aloha_sim_transfer_cube_human](https://huggingface.co/datasets/lerobot/aloha_sim_transfer_cube_human) dataset, using this command:
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```bash
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python lerobot/scripts/train.py \
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--output_dir=outputs/train/act_aloha_transfer \
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--policy.type=act \
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--dataset.repo_id=lerobot/aloha_sim_transfer_cube_human \
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--env.type=aloha \
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--env.task=AlohaTransferCube-v0 \
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--wandb.enable=true
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```
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The training curves may be found at https://wandb.ai/aliberts/lerobot/runs/720l37xb.
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The current model corresponds to the checkpoint at 80k steps.
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This took about 1h45 to train on an Nvida A100.
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## Evaluation
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Here are the success rate results for 500 episodes worth of evaluation. The first row is the naive mean. The second row assumes a uniform prior and computes the beta posterior, then calculates the mean and lower/upper confidence bounds (with a 68.2% confidence interval centered on the mean). The "Theirs" column is for an equivalent model trained on the original ACT repository and evaluated on LeRobot (the model weights may be found in the [`original_act_repo`](https://huggingface.co/lerobot/act_aloha_sim_transfer_cube_human/tree/original_act_repo) branch of this respository). The results of each of the individual rollouts may be found in [eval_info.json](eval_info.json).
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<blank>|Ours|Theirs
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-|-|-
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Success rate for 500 episodes (%) | 87.6 | 68.0
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Beta distribution lower/mean/upper (%) | 86.0 / 87.5 / 88.9 | 65.8 / 67.9 / 70.0
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It was produced after training with this command:
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```bash
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python lerobot/scripts/eval.py \
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--policy.path=outputs/train/act_aloha_transfer/checkpoints/080000/pretrained_model \
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--output_dir=outputs/eval/act_aloha_transfer/080000 \
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--env.type=aloha \
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--env.task=AlohaTransferCube-v0 \
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--eval.n_episodes=500 \
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--eval.batch_size=50 \
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--device=cuda \
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--use_amp=false
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```
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The original code was heavily refactored, and some bugs were spotted along the way. The differences in code may account for the difference in success rate. Another possibility is that our simulation environment may use slightly different heuristics to evaluate success (we've observed that success is registered as soon as the second arm's gripper makes antipodal contact with the cube). Finally, one should observe that the in-training evaluation jumps up towards the end of training. This may need further investigation (Is it statistically significant? If so, what is the cause?).
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config.json
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{
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}
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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.images.top": {
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"type": "VISUAL",
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"shape": [
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3,
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480,
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640
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]
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},
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"observation.state": {
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"type": "STATE",
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"shape": [
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14
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]
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}
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},
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"output_features": {
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"action": {
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"type": "ACTION",
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"shape": [
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14
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]
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}
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},
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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": false,
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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": 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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config.yaml
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device: cuda
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use_amp: false
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seed: 1000
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dataset_repo_id: lerobot/aloha_sim_transfer_cube_human
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training:
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offline_steps: 100000
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online_steps: 0
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online_steps_between_rollouts: 1
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online_sampling_ratio: 0.5
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online_env_seed: ???
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eval_freq: 10000
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save_freq: 25000
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log_freq: 250
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save_model: true
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batch_size: 8
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lr: 1.0e-05
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lr_backbone: 1.0e-05
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weight_decay: 0.0001
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grad_clip_norm: 10
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override_dataset_stats:
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observation.images.top:
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mean:
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std:
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delta_timestamps:
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action:
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eval:
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n_episodes: 50
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batch_size: 50
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use_async_envs: false
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wandb:
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enable: true
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disable_artifact: false
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project: lerobot
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notes: ''
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fps: 50
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env:
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name: aloha
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task: AlohaTransferCube-v0
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state_dim: 14
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action_dim: 14
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fps: ${fps}
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episode_length: 400
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gym:
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obs_type: pixels_agent_pos
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render_mode: rgb_array
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policy:
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name: act
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n_obs_steps: 1
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chunk_size: 100
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n_action_steps: 100
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input_shapes:
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observation.images.top:
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observation.state:
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output_shapes:
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action:
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input_normalization_modes:
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observation.images.top: mean_std
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observation.state: mean_std
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output_normalization_modes:
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action: mean_std
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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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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: 10.0
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|
eval_info.json
CHANGED
|
@@ -2,364 +2,364 @@
|
|
| 2 |
"per_episode": [
|
| 3 |
{
|
| 4 |
"episode_ix": 0,
|
| 5 |
-
"sum_reward":
|
| 6 |
-
"max_reward":
|
| 7 |
-
"success":
|
| 8 |
"seed": 1000
|
| 9 |
},
|
| 10 |
{
|
| 11 |
"episode_ix": 1,
|
| 12 |
-
"sum_reward":
|
| 13 |
"max_reward": 4.0,
|
| 14 |
"success": true,
|
| 15 |
"seed": 1001
|
| 16 |
},
|
| 17 |
{
|
| 18 |
"episode_ix": 2,
|
| 19 |
-
"sum_reward":
|
| 20 |
"max_reward": 4.0,
|
| 21 |
"success": true,
|
| 22 |
"seed": 1002
|
| 23 |
},
|
| 24 |
{
|
| 25 |
"episode_ix": 3,
|
| 26 |
-
"sum_reward":
|
| 27 |
-
"max_reward":
|
| 28 |
-
"success":
|
| 29 |
"seed": 1003
|
| 30 |
},
|
| 31 |
{
|
| 32 |
"episode_ix": 4,
|
| 33 |
-
"sum_reward":
|
| 34 |
"max_reward": 4.0,
|
| 35 |
"success": true,
|
| 36 |
"seed": 1004
|
| 37 |
},
|
| 38 |
{
|
| 39 |
"episode_ix": 5,
|
| 40 |
-
"sum_reward":
|
| 41 |
-
"max_reward":
|
| 42 |
-
"success":
|
| 43 |
"seed": 1005
|
| 44 |
},
|
| 45 |
{
|
| 46 |
"episode_ix": 6,
|
| 47 |
-
"sum_reward":
|
| 48 |
"max_reward": 4.0,
|
| 49 |
"success": true,
|
| 50 |
"seed": 1006
|
| 51 |
},
|
| 52 |
{
|
| 53 |
"episode_ix": 7,
|
| 54 |
-
"sum_reward":
|
| 55 |
"max_reward": 4.0,
|
| 56 |
"success": true,
|
| 57 |
"seed": 1007
|
| 58 |
},
|
| 59 |
{
|
| 60 |
"episode_ix": 8,
|
| 61 |
-
"sum_reward":
|
| 62 |
"max_reward": 4.0,
|
| 63 |
"success": true,
|
| 64 |
"seed": 1008
|
| 65 |
},
|
| 66 |
{
|
| 67 |
"episode_ix": 9,
|
| 68 |
-
"sum_reward":
|
| 69 |
-
"max_reward":
|
| 70 |
-
"success":
|
| 71 |
"seed": 1009
|
| 72 |
},
|
| 73 |
{
|
| 74 |
"episode_ix": 10,
|
| 75 |
-
"sum_reward":
|
| 76 |
"max_reward": 1.0,
|
| 77 |
"success": false,
|
| 78 |
"seed": 1010
|
| 79 |
},
|
| 80 |
{
|
| 81 |
"episode_ix": 11,
|
| 82 |
-
"sum_reward":
|
| 83 |
"max_reward": 4.0,
|
| 84 |
"success": true,
|
| 85 |
"seed": 1011
|
| 86 |
},
|
| 87 |
{
|
| 88 |
"episode_ix": 12,
|
| 89 |
-
"sum_reward":
|
| 90 |
"max_reward": 4.0,
|
| 91 |
"success": true,
|
| 92 |
"seed": 1012
|
| 93 |
},
|
| 94 |
{
|
| 95 |
"episode_ix": 13,
|
| 96 |
-
"sum_reward":
|
| 97 |
"max_reward": 1.0,
|
| 98 |
"success": false,
|
| 99 |
"seed": 1013
|
| 100 |
},
|
| 101 |
{
|
| 102 |
"episode_ix": 14,
|
| 103 |
-
"sum_reward":
|
| 104 |
"max_reward": 4.0,
|
| 105 |
"success": true,
|
| 106 |
"seed": 1014
|
| 107 |
},
|
| 108 |
{
|
| 109 |
"episode_ix": 15,
|
| 110 |
-
"sum_reward":
|
| 111 |
-
"max_reward":
|
| 112 |
-
"success":
|
| 113 |
"seed": 1015
|
| 114 |
},
|
| 115 |
{
|
| 116 |
"episode_ix": 16,
|
| 117 |
-
"sum_reward":
|
| 118 |
"max_reward": 4.0,
|
| 119 |
"success": true,
|
| 120 |
"seed": 1016
|
| 121 |
},
|
| 122 |
{
|
| 123 |
"episode_ix": 17,
|
| 124 |
-
"sum_reward":
|
| 125 |
"max_reward": 4.0,
|
| 126 |
"success": true,
|
| 127 |
"seed": 1017
|
| 128 |
},
|
| 129 |
{
|
| 130 |
"episode_ix": 18,
|
| 131 |
-
"sum_reward":
|
| 132 |
"max_reward": 4.0,
|
| 133 |
"success": true,
|
| 134 |
"seed": 1018
|
| 135 |
},
|
| 136 |
{
|
| 137 |
"episode_ix": 19,
|
| 138 |
-
"sum_reward":
|
| 139 |
-
"max_reward":
|
| 140 |
-
"success":
|
| 141 |
"seed": 1019
|
| 142 |
},
|
| 143 |
{
|
| 144 |
"episode_ix": 20,
|
| 145 |
-
"sum_reward":
|
| 146 |
-
"max_reward":
|
| 147 |
-
"success":
|
| 148 |
"seed": 1020
|
| 149 |
},
|
| 150 |
{
|
| 151 |
"episode_ix": 21,
|
| 152 |
-
"sum_reward":
|
| 153 |
"max_reward": 4.0,
|
| 154 |
"success": true,
|
| 155 |
"seed": 1021
|
| 156 |
},
|
| 157 |
{
|
| 158 |
"episode_ix": 22,
|
| 159 |
-
"sum_reward":
|
| 160 |
"max_reward": 4.0,
|
| 161 |
"success": true,
|
| 162 |
"seed": 1022
|
| 163 |
},
|
| 164 |
{
|
| 165 |
"episode_ix": 23,
|
| 166 |
-
"sum_reward":
|
| 167 |
"max_reward": 4.0,
|
| 168 |
"success": true,
|
| 169 |
"seed": 1023
|
| 170 |
},
|
| 171 |
{
|
| 172 |
"episode_ix": 24,
|
| 173 |
-
"sum_reward":
|
| 174 |
-
"max_reward":
|
| 175 |
-
"success":
|
| 176 |
"seed": 1024
|
| 177 |
},
|
| 178 |
{
|
| 179 |
"episode_ix": 25,
|
| 180 |
-
"sum_reward":
|
| 181 |
"max_reward": 4.0,
|
| 182 |
"success": true,
|
| 183 |
"seed": 1025
|
| 184 |
},
|
| 185 |
{
|
| 186 |
"episode_ix": 26,
|
| 187 |
-
"sum_reward":
|
| 188 |
-
"max_reward":
|
| 189 |
-
"success":
|
| 190 |
"seed": 1026
|
| 191 |
},
|
| 192 |
{
|
| 193 |
"episode_ix": 27,
|
| 194 |
-
"sum_reward":
|
| 195 |
"max_reward": 4.0,
|
| 196 |
"success": true,
|
| 197 |
"seed": 1027
|
| 198 |
},
|
| 199 |
{
|
| 200 |
"episode_ix": 28,
|
| 201 |
-
"sum_reward":
|
| 202 |
-
"max_reward":
|
| 203 |
-
"success":
|
| 204 |
"seed": 1028
|
| 205 |
},
|
| 206 |
{
|
| 207 |
"episode_ix": 29,
|
| 208 |
-
"sum_reward":
|
| 209 |
"max_reward": 4.0,
|
| 210 |
"success": true,
|
| 211 |
"seed": 1029
|
| 212 |
},
|
| 213 |
{
|
| 214 |
"episode_ix": 30,
|
| 215 |
-
"sum_reward":
|
| 216 |
-
"max_reward":
|
| 217 |
-
"success":
|
| 218 |
"seed": 1030
|
| 219 |
},
|
| 220 |
{
|
| 221 |
"episode_ix": 31,
|
| 222 |
-
"sum_reward":
|
| 223 |
-
"max_reward":
|
| 224 |
-
"success":
|
| 225 |
"seed": 1031
|
| 226 |
},
|
| 227 |
{
|
| 228 |
"episode_ix": 32,
|
| 229 |
-
"sum_reward":
|
| 230 |
"max_reward": 4.0,
|
| 231 |
"success": true,
|
| 232 |
"seed": 1032
|
| 233 |
},
|
| 234 |
{
|
| 235 |
"episode_ix": 33,
|
| 236 |
-
"sum_reward":
|
| 237 |
"max_reward": 4.0,
|
| 238 |
"success": true,
|
| 239 |
"seed": 1033
|
| 240 |
},
|
| 241 |
{
|
| 242 |
"episode_ix": 34,
|
| 243 |
-
"sum_reward":
|
| 244 |
"max_reward": 4.0,
|
| 245 |
"success": true,
|
| 246 |
"seed": 1034
|
| 247 |
},
|
| 248 |
{
|
| 249 |
"episode_ix": 35,
|
| 250 |
-
"sum_reward":
|
| 251 |
"max_reward": 4.0,
|
| 252 |
"success": true,
|
| 253 |
"seed": 1035
|
| 254 |
},
|
| 255 |
{
|
| 256 |
"episode_ix": 36,
|
| 257 |
-
"sum_reward":
|
| 258 |
-
"max_reward":
|
| 259 |
-
"success":
|
| 260 |
"seed": 1036
|
| 261 |
},
|
| 262 |
{
|
| 263 |
"episode_ix": 37,
|
| 264 |
-
"sum_reward":
|
| 265 |
"max_reward": 4.0,
|
| 266 |
"success": true,
|
| 267 |
"seed": 1037
|
| 268 |
},
|
| 269 |
{
|
| 270 |
"episode_ix": 38,
|
| 271 |
-
"sum_reward":
|
| 272 |
"max_reward": 4.0,
|
| 273 |
"success": true,
|
| 274 |
"seed": 1038
|
| 275 |
},
|
| 276 |
{
|
| 277 |
"episode_ix": 39,
|
| 278 |
-
"sum_reward":
|
| 279 |
"max_reward": 4.0,
|
| 280 |
"success": true,
|
| 281 |
"seed": 1039
|
| 282 |
},
|
| 283 |
{
|
| 284 |
"episode_ix": 40,
|
| 285 |
-
"sum_reward":
|
| 286 |
"max_reward": 4.0,
|
| 287 |
"success": true,
|
| 288 |
"seed": 1040
|
| 289 |
},
|
| 290 |
{
|
| 291 |
"episode_ix": 41,
|
| 292 |
-
"sum_reward":
|
| 293 |
"max_reward": 4.0,
|
| 294 |
"success": true,
|
| 295 |
"seed": 1041
|
| 296 |
},
|
| 297 |
{
|
| 298 |
"episode_ix": 42,
|
| 299 |
-
"sum_reward":
|
| 300 |
"max_reward": 4.0,
|
| 301 |
"success": true,
|
| 302 |
"seed": 1042
|
| 303 |
},
|
| 304 |
{
|
| 305 |
"episode_ix": 43,
|
| 306 |
-
"sum_reward":
|
| 307 |
"max_reward": 4.0,
|
| 308 |
"success": true,
|
| 309 |
"seed": 1043
|
| 310 |
},
|
| 311 |
{
|
| 312 |
"episode_ix": 44,
|
| 313 |
-
"sum_reward":
|
| 314 |
"max_reward": 4.0,
|
| 315 |
"success": true,
|
| 316 |
"seed": 1044
|
| 317 |
},
|
| 318 |
{
|
| 319 |
"episode_ix": 45,
|
| 320 |
-
"sum_reward":
|
| 321 |
"max_reward": 4.0,
|
| 322 |
"success": true,
|
| 323 |
"seed": 1045
|
| 324 |
},
|
| 325 |
{
|
| 326 |
"episode_ix": 46,
|
| 327 |
-
"sum_reward":
|
| 328 |
-
"max_reward":
|
| 329 |
"success": false,
|
| 330 |
"seed": 1046
|
| 331 |
},
|
| 332 |
{
|
| 333 |
"episode_ix": 47,
|
| 334 |
-
"sum_reward":
|
| 335 |
"max_reward": 4.0,
|
| 336 |
"success": true,
|
| 337 |
"seed": 1047
|
| 338 |
},
|
| 339 |
{
|
| 340 |
"episode_ix": 48,
|
| 341 |
-
"sum_reward":
|
| 342 |
"max_reward": 4.0,
|
| 343 |
"success": true,
|
| 344 |
"seed": 1048
|
| 345 |
},
|
| 346 |
{
|
| 347 |
"episode_ix": 49,
|
| 348 |
-
"sum_reward":
|
| 349 |
"max_reward": 4.0,
|
| 350 |
"success": true,
|
| 351 |
"seed": 1049
|
| 352 |
},
|
| 353 |
{
|
| 354 |
"episode_ix": 50,
|
| 355 |
-
"sum_reward":
|
| 356 |
"max_reward": 4.0,
|
| 357 |
"success": true,
|
| 358 |
"seed": 1050
|
| 359 |
},
|
| 360 |
{
|
| 361 |
"episode_ix": 51,
|
| 362 |
-
"sum_reward":
|
| 363 |
"max_reward": 4.0,
|
| 364 |
"success": true,
|
| 365 |
"seed": 1051
|
|
@@ -373,77 +373,77 @@
|
|
| 373 |
},
|
| 374 |
{
|
| 375 |
"episode_ix": 53,
|
| 376 |
-
"sum_reward":
|
| 377 |
-
"max_reward":
|
| 378 |
-
"success":
|
| 379 |
"seed": 1053
|
| 380 |
},
|
| 381 |
{
|
| 382 |
"episode_ix": 54,
|
| 383 |
-
"sum_reward":
|
| 384 |
"max_reward": 4.0,
|
| 385 |
"success": true,
|
| 386 |
"seed": 1054
|
| 387 |
},
|
| 388 |
{
|
| 389 |
"episode_ix": 55,
|
| 390 |
-
"sum_reward":
|
| 391 |
-
"max_reward":
|
| 392 |
-
"success":
|
| 393 |
"seed": 1055
|
| 394 |
},
|
| 395 |
{
|
| 396 |
"episode_ix": 56,
|
| 397 |
-
"sum_reward":
|
| 398 |
"max_reward": 1.0,
|
| 399 |
"success": false,
|
| 400 |
"seed": 1056
|
| 401 |
},
|
| 402 |
{
|
| 403 |
"episode_ix": 57,
|
| 404 |
-
"sum_reward":
|
| 405 |
"max_reward": 4.0,
|
| 406 |
"success": true,
|
| 407 |
"seed": 1057
|
| 408 |
},
|
| 409 |
{
|
| 410 |
"episode_ix": 58,
|
| 411 |
-
"sum_reward":
|
| 412 |
"max_reward": 4.0,
|
| 413 |
"success": true,
|
| 414 |
"seed": 1058
|
| 415 |
},
|
| 416 |
{
|
| 417 |
"episode_ix": 59,
|
| 418 |
-
"sum_reward":
|
| 419 |
"max_reward": 4.0,
|
| 420 |
"success": true,
|
| 421 |
"seed": 1059
|
| 422 |
},
|
| 423 |
{
|
| 424 |
"episode_ix": 60,
|
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{
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|
@@ -1913,84 +1913,84 @@
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99500,0.042739674448967
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99750,0.043802548199892
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training_curves.png
CHANGED
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