arXiv — cs.AI preprintsInternational2 October 2026
Reconstruct, Practice, Go Real: Guided Self-Improvement for Embodied Agents
This is an official announcement record
Firsthand records what arXiv — cs.AI preprints announced and links to the original. The wording below is theirs, not ours.
arXiv:2610.02204v1 Announce Type: cross Abstract: Building reliable robot capabilities across diverse tasks requires substantial human effort to develop and maintain skills, design rewards, and integrate perception with control. We present Reconstruct, Practice, Go Real (RPG), a framework for autonomous improvement of robot execution systems without updating model weights. RPG identifies manipulation capabilities in an offline dataset and constructs related practice tasks in simulation. During practice, RPG uses execution feedback, privileged simulator state, and available dataset videos to di
Read the official announcement
Opens arxiv.org
More from arXiv — cs.AI preprints
- Heavy-Tailed Memory Traces in Long-Horizon Language Agents2 October 2026
- When Do Causal World Models Help Modular LLM Agents2 October 2026
- From Proposal to Verified Effect: Praxa, an Evidence-Bound Harness for Governed AI Agent Execution2 October 2026
- What Do Rationales Communicate? A Message-Intervention Study in Role-Specialized QA2 October 2026
- Measuring the Microtask Eligibility Gap: When Is an Off-the-Shelf SLM Enough for an Agent Harness?2 October 2026
This content is for informational purposes only and is not professional advice. Specifications, prices, plan tiers, and features change frequently and may differ from what is shown here; verify current details on the manufacturer's or company's official page before purchasing. Ratings are based on analysis of published documentation, not independent lab testing.