Model Card for ACT/BananaPick (Distilled)
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:
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
@software{ib_robot,
title = {IB-Robot: Intelligence Boom Robot},
url = {https://gitcode.com/openeuler/IB_Robot},
license = {Apache-2.0}
}