EmbodiedRSI: Active Continual Robot Learning Through Hypothesis-Guided Co-Evolution
- Published
- Source
- arXiv
- Paper number
- 1179
- Field
- Robotics
- arXiv ID
- 2610.10498
Key points
- Value-of-Information-based experiment selection reduced the number of robot trials and improved exploration efficiency.
- They built a co-evolution structure that evaluates code and skill hypotheses separately and then measures the gain of running them jointly.
- It reached 77.0% overall and 71.3% on Composite-Unseen in RoboCasa365, far ahead of the best baseline at 40.1%.
- It recorded 86.8% success on LIBERO-Pro and achieved 71.3% when transferring zero-shot from simulation to a real robot.
- They proposed a three-level hierarchical memory of execution records, hypotheses, and distilled knowledge that is reused for selecting the next experiment.
Paper links
External research summaries. These are not HDATF publications or measured product results.