Multi-task online RL can improve generalist robot policies

Michal Korniak and co-authors propose and systematically evaluate a method for improving generalist robot policies via multi-task online reinforcement learning in an arXiv paper. The authors claim that a suitable algorithmic recipe can not only improve multi-task performance online but also generalize to tasks not covered in online training, and they describe this as one of the early pieces of evidence for online general improvement of robot foundation models. The authors built a structured evaluation suite to test general improvement capability and systematically compared algorithmic and engineering choices such as value estimation, policy extraction, and resource allocation. The authors claim that the training task distribution and value-based reinforcement learning are key factors determining whether such generalization emerges. These results are the authors' own claims and have not yet been independently verified.

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