Embodied 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.