crislmfroes / act-panda-open-base-cabinet-sim-v17

huggingface.co
Total runs: 11
24-hour runs: 0
7-day runs: 0
30-day runs: 5
Model's Last Updated: August 23 2025
robotics

Introduction of act-panda-open-base-cabinet-sim-v17

Model Details of act-panda-open-base-cabinet-sim-v17

Model Card for act

Action Chunking with Transformers (ACT) 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.

This policy has been trained and pushed to the Hub using LeRobot . See the full documentation at LeRobot Docs .


How to Get Started with the Model

For a complete walkthrough, see the training guide . Below is the short version on how to train and run inference/eval:

Train from scratch
python -m lerobot.scripts.train \
  --dataset.repo_id=${HF_USER}/<dataset> \
  --policy.type=act \
  --output_dir=outputs/train/<desired_policy_repo_id> \
  --job_name=lerobot_training \
  --policy.device=cuda \
  --policy.repo_id=${HF_USER}/<desired_policy_repo_id>
  --wandb.enable=true

Writes checkpoints to outputs/train/<desired_policy_repo_id>/checkpoints/ .

Evaluate the policy/run inference
python -m lerobot.record \
  --robot.type=so100_follower \
  --dataset.repo_id=<hf_user>/eval_<dataset> \
  --policy.path=<hf_user>/<desired_policy_repo_id> \
  --episodes=10

Prefix the dataset repo with eval_ and supply --policy.path pointing to a local or hub checkpoint.


Model Details
  • License: apache-2.0

Runs of crislmfroes act-panda-open-base-cabinet-sim-v17 on huggingface.co

11
Total runs
0
24-hour runs
-3
3-day runs
0
7-day runs
5
30-day runs

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