SO-101 ACT — Orange Rectangular Block to Blue Cup
ACT policy trained for the task:
Pick up the orange rectangular block and place it inside the blue cup.
Training
- Robot: SO-101 follower arm
- Policy: ACT with a ResNet-18 visual backbone
- Demonstrations: 50 successful real-robot episodes, 32,481 frames
- Recording rate: 30 FPS
- Cameras: two RGB streams at 640×480
- Training steps: 40,000
- Action chunk size: 50
- Executed action steps per chunk: 25
- Inputs: two RGB camera streams plus six-dimensional joint state
- Output: six-dimensional joint action
The final 1,000-step window had a mean training loss of approximately 0.0729 ± 0.0015.
Training loss is an optimization metric and is not a physical success-rate measurement.
Load with LeRobot
from lerobot.policies.act.modeling_act import ACTPolicy
policy = ACTPolicy.from_pretrained(
"DylanSh/so101_orange_rectangular_block_blue_cup_act_40k"
)
Camera-key warning
The recording setup used these historical feature names:
observation.images.front: close arm-mounted viewobservation.images.wrist: fixed workspace overview
The names are semantically reversed but must remain unchanged at deployment because they are part of the learned feature mapping.
Intended use and limitations
This checkpoint is intended for the same SO-101 calibration, camera geometry, task wording, table layout, and object appearance used during recording. Run a no-motion calibration/port check before physical evaluation and keep an emergency stop available. Performance may degrade after camera, lighting, calibration, gripper, object, or workspace changes.
The training dataset is currently stored locally and is not included in this model repository.