Scaling Online Exploration for Robot Agents
Scaling Online Exploration for Robot Agents
This research presents RAX, an open-source framework for robot agents that enables continuous self-improvement through online exploration. The framework allows agents to acquire experience, synthesize executable programs, and perform complex tasks without human demonstrations. RAX demonstrates significant performance improvements in both simulation and real-world settings, with results showing a +37.4% average improvement over top baseline models.
The paper introduces RAX, an open-source framework for robot agents that enables online exploration without human demonstrations, achieving improved generalization and task performance in both simulation and real-world settings.
Source: arXiv Robotics — research abstracts · Read original article ↗
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Source：arXiv Robotics — research abstracts · arxiv.org