[2605.08571] BEACON: Cross-Domain Co-Training of Generative Robot Policies via Best-Effort Adaptation
[Submitted on 9 May 2026]
Title:BEACON: Cross-Domain Co-Training of Generative Robot Policies via Best-Effort Adaptation
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Abstract:We introduce BEACON--Best-Effort Adaptation for Cross-Domain Co-Training--a theory-driven framework for training generative robot policies with abundant source demonstrations and limited target demonstrations. BEACON casts cross-domain co-training as a discrepancy-aware importance-reweighting problem, jointly learning a diffusion-based visuomotor policy and per-sample source weights that minimize an objective informed by target-domain generalization guarantees. To make best-effort adaptation practical for high-dimensional sequence policies, we develop scalable instance-level discrepancy estimators, stochastic alternating updates for policy and weights, and a multi-source extension that balances heterogeneous source domains. Across sim-to-sim, sim-to-real, and multi-source manipulation settings, BEACON improves robustness and data efficiency over target-only, fixed-ratio co-training, and feature-alignment baselines. Importantly, even without an explicit alignment objective, BEACON achieves feature alignment as an implicit result of discrepancy-aware cross-domain co-training.
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2605.08571 [cs.RO]
(or arXiv:2605.08571v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2605.08571
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Antong Zhang [view email] [v1] Sat, 9 May 2026 00:15:28 UTC (6,680 KB)
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