Total runs:
80.2K
Run Growth:
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Growth Rate:
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Sponsored by
APIMaster
lerobot / act_aloha_sim_insertion_human
Model's Last Updated: March 07 2025
robotics
Introduction of act_aloha_sim_insertion_human
Model Details of act_aloha_sim_insertion_human
Model Card for ACT/AlohaInsertion
Action Chunking Transformer Policy (as per
Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware
) trained for the
AlohaInsertion
environment from
gym-aloha
.
How to Get Started with the Model
See the LeRobot library (particularly the evaluation script ) for instructions on how to load and evaluate this model.
Training Details
Trained with LeRobot@TODO .
The model was trained using LeRobot's training script and with the aloha_sim_insertion_human dataset, using this command:
python lerobot/scripts/train.py \
--output_dir=outputs/train/act_aloha_insertion \
--policy.type=act \
--dataset.repo_id=lerobot/aloha_sim_insertion_human \
--env.type=aloha \
--env.task=AlohaInsertion-v0 \
--wandb.enable=true
The training curves may be found at https://wandb.ai/aliberts/lerobot/runs/qxcub3cg . The current model corresponds to the checkpoint at 100k steps.
This took about 1h50 to train on an Nvida A100.
Evaluation
The model was evaluated on the
AlohaInsertion
task from
gym-aloha
and compared to a similar model trained with the original
ACT repository
. Each episode marks a success if the peg is successfully picked by one robot arm and inserted into the socket held by the other robot arm.
Here are the success rate results for 500 episodes worth of evaluation. The "Theirs" column is the metric as reported in the original paper. The results of each of the individual rollouts may be found in eval_info.json .
| Ours | Theirs | |
|---|---|---|
| Success rate for 500 episodes (%) | 20.6 | 20 |
It was produced after training with this command:
python lerobot/scripts/eval.py \
--policy.path=outputs/train/act_aloha_insertion/checkpoints/last/pretrained_model \
--output_dir=outputs/eval/act_aloha_insertion/last \
--env.type=aloha \
--env.task=AlohaInsertion-v0 \
--eval.n_episodes=500 \
--eval.batch_size=50 \
--device=cuda \
--use_amp=false
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?).
Runs of lerobot act_aloha_sim_insertion_human on huggingface.co
225
Total runs
0
24-hour runs
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3-day runs
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7-day runs
-111
30-day runs
More Information About act_aloha_sim_insertion_human huggingface.co Model
Url of act_aloha_sim_insertion_human
act_aloha_sim_insertion_human huggingface.co Url
Provider of act_aloha_sim_insertion_human huggingface.co
Other API from lerobot
Updated:September 17 2026
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