Instructions to use aakashv100/act_so101_pick_cube_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LeRobot
How to use aakashv100/act_so101_pick_cube_v2 with LeRobot:
- Notebooks
- Google Colab
- Kaggle
metadata
library_name: lerobot
license: apache-2.0
pipeline_tag: robotics
tags:
- robotics
- lerobot
- act
- so101
datasets:
- aakashv100/so101-pick-cube-v2
ACT — SO-101 pick cube v2
ACT (Action Chunking Transformer) policy for the SO-101 follower arm. Task: "Pick up the cube and place it in the bowl".
Training
- Dataset: aakashv100/so101-pick-cube-v2 — 50 teleoperated episodes, 30 fps
- Cameras:
gripper_cam(wrist, 640x480) +top_cam(overhead wide-angle USB, 640x480) - State/action: 6-DoF joint positions (degrees) + gripper
- Steps: 60k, chunk_size = n_action_steps = 100
- Checkpoint: 060000
Usage (lerobot)
lerobot-record \
--robot.type=so101_follower \
--policy.path=aakashv100/act_so101_pick_cube_v2 \
--dataset.single_task="Pick up the cube and place it in the bowl" \
...
Evaluation
Evaluated with a 50-trial fixed-position + 50-trial random-position protocol (same checkpoint, cube start positions within a 10x10 cm zone). Note: all training episodes start the cube at a single fixed position, so random-position performance measures out-of-distribution generalization.