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cs.RO2026
Residual Denoising Enables Sample-Efficient Multi-Agent Coordination on Demand
Dayi Dong, Maulik Bhatt, Aayushi Shrivastava +2
Pretrained robot policies offer strong manipulation skills but are typically limited to single-agent settings, where a robot acts in isolation. In this work, we study how to adapt…
cs.LG2026
Matching Multiple Experts: On the Exploitability of Multi-Agent Imitation Learning
Antoine Bergerault, Volkan Cevher, Negar Mehr
Multi-agent imitation learning (MA-IL) aims to learn optimal policies from expert demonstrations of interactions in multi-agent interactive domains. Despite existing guarantees on…