Fleet Learning via Policy Merging

arXiv:2310.01362v2 Announce Type: replace-cross
Abstract: Fleets of robots ingest massive amounts of heterogeneous streaming data silos generated by interacting with their environments, far more than what can be stored or transmitted with ease. At the same time, teams of robots should co-acquire diverse skills through their heterogeneous experiences in varied settings. How can we enable such fleet-level learning without having to transmit or centralize fleet-scale data? In this paper, we investigate policy merging (PoMe) from such distributed heterogeneous datasets as …

abstract arxiv cs.lg cs.ro data data silos diverse environments generated massive merging policy robots skills streaming streaming data teams through type via