新闻
EmbodiedRSI: Active Continual Robot Learning Through Hypothesis-Guided Co-Evolution
arXiv cs.AI · 发布于 · 阅读约3分钟
30秒读懂
- 发生了什么
- EmbodiedRSI enables robots to autonomously improve their skills by selecting which physical experiments to run, then evolving code and behaviors based on results.
- 为何重要
- Roboticists building systems that need to adapt beyond foundation models without expensive manual data collection or teleoperation.
- 注意
- Results are on simulation benchmarks and limited real-world tasks; scalability to diverse real-world environments and long-term deployment remains undemonstrated.
- agent
- agentic
- foundation model
- post-train
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