Action Chunking Transformer Policy (as per Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware) trained for banana pick-and-place on a 1-arm SO-101 robot. This checkpoint is a distilled model obtained via knowledge distillation.

How to Get Started with the Model

See the IB-Robot project (particularly the inference_service) for instructions on how to load and deploy this model with ROS 2.

To load the model directly in Python:

python

from lerobot.common.policies.act.modeling_act import ACTPolicy

policy = ACTPolicy.from_pretrained("RightAiknow/act_1arm_2cam_banana_pick_v1_distill")

Training Details

This model was trained via knowledge distillation (kd: true) within the IB-Robot framework.

Policy: ACT (Action Chunking with Transformers)
Training method: Knowledge distillation
Robot: 1-arm SO-101
Cameras: top, wrist (480x640)
Action dim: 6
Training steps: 500,000 (configured)
Checkpoint: step_160000
Chunk size: 100
Batch size: 32
Vision backbone: ResNet18
Optimizer: AdamW (lr=1e-5, weight_decay=1e-4)
Latent dim (VAE): 32
Dim model: 1024

Citation

bibtex

@software{ib_robot,
  title = {IB-Robot: Intelligence Boom Robot},
  url = {https://gitcode.com/openeuler/IB_Robot},
  license = {Apache-2.0}
}