metadata
datasets: arclabmit/xarm7_beavrsim_shellgame_dataset
library_name: lerobot
license: apache-2.0
pipeline_tag: robotics
tags:
- robotics
- lerobot
- act
model-index:
- name: xarm7_act_beavrsim_shellgame_model
results:
- task:
type: robotics
name: Robotic Manipulation
dataset:
name: beavr_sim
type: simulation
metrics:
- type: success_rate
value: 10.299999999999999
name: Success Rate
- type: reward
value: -0.05
name: Avg Reward
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
lerobot-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
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
Evaluation Results
Evaluated on 2026-02-05 09:45
| Metric | Value |
|---|---|
| Success Rate | 10.3% |
| Average Reward | -0.050 |
| Max Reward (Avg) | 1.030 |
| Episodes | 1000 |
| Eval Speed | 2.47 s/ep |
| Seed | 26 |
Detailed per-episode results can be found in eval/eval_info.json.