Recursia's inner-outer loop pushes embodied RSI, crushing π₀ on real-robot compositional tasks
机器之心 · wechat · 2026-10-14
A team from Beijing Jiaotong University, Peking University, NYU, and USC proposes Recursia, framing compositional generalization within embodied recursive self-improvement: an inner loop handles observation, decisions, and code execution, while an outer loop proposes, verifies, and retains edits to a shared executable stack so improvements persist across tasks.
Key results
- LIBERO-ProLong zero-shot transfer: 28%/53% success under position/task perturbation, beating NVIDIA ASPIRE by 5.8 and 15.1 points; OpenVLA and π₀ score 0%, π₀.₅ just 8%/1%
- Real-robot (Franka FR3) three-task average: 66.7% vs 26.7% for the SD-only baseline; the strongly compositional "move bowl, then place cup in the moved bowl" task hits 100% vs 20%
Architecture
- Shared stack spans four layers: tools, skills, handover (state continuity between steps), and verification, catching interface failures hidden behind single-step successes
- Candidate patches pass generality checks, mechanism tests, and parent-child comparisons; patches that lose previously-successful states beyond a per-task cap are rejected
- The outer harness's gate strictness and debugging data selection are themselves treated as tunable objects
The article also surveys RSI efforts at Anthropic (Claude writes over 80% of internal merged code), OpenAI (RSI Index in GPT-5.6 notes), and NVIDIA's ASPIRE/ENPIRE, and notes that multimodal-LLM-driven arms still bottleneck on 3D geometry and contact-state perception in fine manipulation.
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