Reinforcement Learning Enables Scaling Generalist Robot Policy Improvement
Reinforcement Learning Enables Scaling Generalist Robot Policy Improvement
This research paper introduces a method for iteratively improving generalist robot policies via reinforcement learning. The authors demonstrate that such policies can be adapted online across many diverse tasks simultaneously, achieving performance improvements and zero-shot generalization to tasks not seen during the adaptation phase. The study systematically evaluates algorithmic choices regarding value estimation, policy extraction, and resource allocation, identifying training task distribution and value-based RL as critical factors for enabling this multi-task generalist improvement.
This arXiv preprint presents the first evidence, to the authors' knowledge, that generalist robot foundation models can undergo online improvement across diverse tasks using reinforcement learning, with performance gains extending to tasks not explicitly included in the training distribution.
Source: arXiv Robotics — research abstracts · Read original article ↗
Loading article text…
What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
- paper: not_reported
Source excerpts and review record
Automatically extracted; no manual editorial approval recorded.
Source：arXiv Robotics — research abstracts · arxiv.org