Recursia: Recursive Self-Improvement for Zero-Shot Long-Horizon Robot Manipution
Overview
This article introduces the Recursia framework, which achieves embodied recursive self-improvement (RSI) by organizing the system as an internal and external dual-loop structure, enabling generalization in robot zero-shot long-sequence tasks. Recursia achieved success rates of 28.4% and 53.4% in LIBERO-Pro Long tasks, respectively 5.8 and 15.1 percentage points higher than NVIDIA ASPIRE. The study emphasizes the importance of integrating execution feedback into high-level decision-making and explores the challenges of maintaining task success across different execution contexts.
From Machine Heart — Robotics on WeChat
Event evidence and corrections
1 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
Reported quantity · Reported success rate: 43.4 percent · Basis not reported · Differing source assertions
“另一组迭代分析中，Task 方向的成功率从第 0 轮的 0% 提高到第 18 轮的 43.4%，中间存在波动。”
Exact source · revision 1jiqizhixin
Reported quantity · Reported success rate: 66.7 percent · Basis not reported · Differing source assertions
“按表 1 的真机结果，Recursia 的三任务平均成功率比 SD-only 高出 40 个百分点（66.7% vs 26.7%） ；其中 “先把碗放上盘子、再把杯子放进移动后的碗” 这一强组合依赖任务，基线为 20%，Recursia 为 100%。”
Exact source · revision 1jiqizhixin
Evidence dependency changes (2)
- 2026-10-30T22:59:45.333Z · evidence updated · source revision 1. Evidence extraction was updated; current source attributions are shown above.
- 2026-10-30T22:59:28.820Z · evidence updated · source revision 1. Evidence extraction was updated; current source attributions are shown above.
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- Machine Heart — Robotics on WeChatSignalEmbodied RSI Paradigm Breaks Through Zero-Shot Generalization Bottleneck for Robots: Recursia Outperforms NVIDIA ASPIRE by 10 Points in Long-Term Tasks
This article introduces the Recursia framework, which achieves embodied recursive self-improvement (RSI) by organizing the system as an internal and external dual-loop structure, enabling generalization in robot zero-shot long-sequence tasks. Recursia achieved success rates of 28.4% and 53.4% in LIBERO-Pro Long tasks, respectively 5.8 and 15.1 percentage points higher than NVIDIA ASPIRE. The study emphasizes the importance of integrating execution feedback into high-level decision-making and explores the challenges of maintaining task success across different execution contexts.
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