Article

BEACON: Cross-Domain Co-Training of Generative Robot Policies via Best-Effort Adaptation

BEACON proposes a theory-guided cross-domain co-training framework that trains diffusion-based visuomotor policies using abundant source demonstrations and limited target demonstrations, and formulates co-training as a discrepancy-aware importance reweighting problem. The method introduces scalable instance-level discrepancy proxies, stochastic alternating updates of policy and weights, and a multi-source extension, improving robustness and data efficiency over target-only, fixed-ratio co-training, and feature-alignment baselines in sim-to-sim, sim-to-real, and multi-source manipulation settings.

Part of the event: BEACON: Cross-Domain Co-Training for Generative Robot Policies

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