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Browse files- README.md +11 -32
- config.yaml +7 -7
- eval_info.json +819 -819
- model.safetensors +1 -1
README.md
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See the [LeRobot library](https://github.com/huggingface/lerobot) (particularly the [evaluation script](https://github.com/huggingface/lerobot/blob/main/lerobot/scripts/eval.py)) for instructions on how to load and evaluate this model.
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## Training Details
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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/d747195c5733c4f68d4bfbe62632d6fc1b605712/lerobot/scripts/train.py) and with the [aloha_sim_transfer_cube_human](https://huggingface.co/datasets/lerobot/aloha_sim_transfer_cube_human/tree/v1.3) dataset.
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Here are the [loss](./train_loss.csv) and [evaluation success rate](./eval_pc_success.csv) (with 50 rollouts) during training.
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This took about 2.5 hours to train on an Nvida RTX 3090.
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## Evaluation
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The model was evaluated on the `AlohaTransferCube` environment from [gym-aloha](https://github.com/huggingface/gym-aloha) and compared to a similar model trained with the original [ACT repository](https://github.com/tonyzhaozh/act). Each episode marks a success if the cube is successfully picked by one robot arm and transferred to the other robot arm.
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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).
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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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This branch contains the model weights obtained from training on the original ACT repository.
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This is the command that was used for training:
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```bash
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python3 imitate_episodes.py \
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--task_name sim_transfer_cube_human \
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--ckpt_dir outputs/sim_transfer_cube_human_vae \
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--policy_class ACT --kl_weight 10 --chunk_size 100 --hidden_dim 512 --batch_size 8 --dim_feedforward 3200 \
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--num_epochs 2000 --lr 1e-5 \
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--seed 0 \
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```
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Also, the code was patched to train with all the data rather than reserving a validation split.
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config.yaml
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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:
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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:
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save_freq:
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log_freq:
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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:
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override_dataset_stats:
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observation.images.top:
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mean:
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eval:
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n_episodes:
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batch_size:
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use_async_envs: false
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wandb:
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enable: true
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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: 10000
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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: 10
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save_freq: 10
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log_freq: 50
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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: 100
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override_dataset_stats:
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observation.images.top:
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mean:
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eval:
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n_episodes: 10
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batch_size: 10
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use_async_envs: false
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wandb:
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enable: true
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eval_info.json
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"per_episode": [
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@@ -1780,784 +1780,784 @@
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@@ -3362,162 +3362,162 @@
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