Stereo-CoRE

Stereo-CoRE

Official reproducibility package for Stereo-CoRE, a strictly decentralized shared policy for multi-task, multi-robot manipulation. Each robot receives only its own panda_hand wrist RGB-D observation and qpos. Deployment uses no task/agent ID, language, communication, global camera, peer observation, right camera, or FastFS.

Observation and policy contract

• wrist RGB-D: 640x480
• native metric depth decoded from millimetres
• frozen DINOv3-B/16 RGB and DeFM-S/14 depth encoders
• aligned 30x40 RGB/depth patch grids with learned 2-D relative-bias RGB-to-depth attention
• ACT: 4-layer latent encoder, 7-layer decoder, chunk length 100
• shared policy across LiftBarrier (2), CameraAlignment (3), ThreeRobotsStackCube (3), LongPipelineDelivery (4), and TakePhoto (4)

Main method

Stereo-CoRE couples the local action-query router to counterfactual expert capability. At a scheduled update, every expert predicts the same ground-truth action chunk; its true action error defines a soft capability target, and KL(q_capability || p_router) trains the router to select experts that are actually competent for the current local action role. The released main run uses capability_weight=0.05 and disables relation, specialization, and anchor auxiliaries.

Repositories

• Code: https://github.com/YananZHOU5555/Stereo-CoRE
• Dataset: https://huggingface.co/datasets/B111ue/RoboFactory-5Task-RGBD-Decentralized
• Models: https://huggingface.co/B111ue/Stereo-CoRE
• Upstream RoboFactory commit: 5868242322414a91454e22f1dd9641f613ba1bcf

See docs/REPRODUCE.md, docs/METHOD.md, and docs/RESULTS.md.

MODEL_REGISTRY.json binds every All-5 paper row to one public checkpoint, its SHA-256, embedded normalization statistics, exact config, frozen-seed results and evaluation protocol.