RLinf-USER: A Unified and Extensible System for Real-World Online Policy Learning in Embodied AI
Research
Source: arXiv cs.ROPublish time unverified
arXiv:2602.07837v4 Announce Type: replace Abstract: Online policy learning directly in the physical world is a promising yet challenging direction for embodied intelligence. Unlike simulation, real-world systems cannot be arbitrarily accelerated, cheaply reset, or massively replicated, suggesting that real-world policy learning is not merely an algorithmic problem, but inherently a systems problem.