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This work introduces CoRE, which learns Collaboration-Role Experts from pooled multi-task, multi-robot demonstrations, and achieves the highest average performance among evaluated decentralized methods.
NestDex, a nested policy-learning framework that reduces this burden by using learned hand skills to assist demonstration collection, improves demonstration reliability and efficiency, and the resulting empirical evaluations support effective autonomous policy learning.
TriManPolicy is presented, a tri-manual imitation learning system that allows one operator to demonstrate behaviours for three arms while reconsidering when they occur, and policies trained on demonstrations retimed by DATS exhibit more efficient coordination while maintaining comparable observed task success.
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