Reconstruct, Practice, Go Real: Guided Self-Improvement for Embodied Agents
- Published
- Source
- arXiv
- Paper number
- 1151
- Field
- Robotics
- arXiv ID
- 2610.02204
Key points
- They proposed the RPG framework, which automatically creates simulation practice tasks from offline data and revises the skill library and prompts through failure diagnosis.
- Without changing model weights, improving only skills and prompts raised success on 22 tasks from 28.6% to 95.0%.
- It outperformed the prior best baselines ASPIRE (75.5%) and a GPT-6-based agent (60.0%) by a wide margin.
- Skill revisions are only retained after cross-task validation showing they also help other tasks sharing the library.
- After calibration, the frozen system succeeded in all 30 physical robot trials across three tasks.
Paper links
External research summaries. These are not HDATF publications or measured product results.