The paper discusses recursive self‑improvement (RSI) in physical AI, where a system not only enhances its behavior but also the methods it uses to improve. It reviews how robot learning can evolve both capabilities and improvement processes, highlighting that gains persist only when feedback addresses remaining learning needs, the component can apply the change, the value survives task or hardware shifts, and practice remains safe and affordable. Current physical studies mainly show behavioral and procedural gains, but have not yet isolated recursive benefits, leading the authors to propose evaluation principles and future research directions for safely trial‑by‑trial self‑improvement.
arXiv:2610.18842v1 Announce Type: new Abstract: Robots are increasingly autonomous in task execution, yet their improvement remains largely human-driven. This study investigates Physical Recursive Se...
arXiv:2610.35604v1 Announce Type: cross Abstract: Generalist robot policies and language-model agents provide complementary capabilities, but completing a task also requires adapting their use to the...
arXiv:2606. 19980v1 Announce Type: new Abstract: Achieving dexterous robotic manipulation in the real world heavily relies on human supervision and algorithm engineering, which becomes a central bottleneck in the pursuit of general physical intelligence.
The paper discusses recursive self‑improvement (RSI) for AI, describing how systems can use experience and feedback to make lasting enhancements to both their abilities and their future improvement processes. It introduces the Headroom‑Closed Index (HCI) to expose limitations in current large language models and outlines a development roadmap for RSI, progressing from autonomy in execution to full recursive meta‑improvement. The authors analyze RSI in various contexts such as scientific discovery, embodied intelligence, and software engineering, noting differing requirements and development speeds, and they connect RSI research to practical industry applications while highlighting key challenges to achieving genuine RSI.
Teach-and-Grow Learning (TGL) is an agent-centered architecture that transforms a few successful demonstrations into reusable Skill Blocks, enabling a robot to compose, execute, and revise behaviors in new scenes without task-specific policy retraining. The system maintains a Skill Library and structured Experience Memory to capture successes, failures, and repairs, allowing persistent reuse and agent-directed adaptation. Evaluation on the LIBERO benchmark shows state-of-the-art performance, and the authors propose a scaling-law hypothesis suggesting that accumulated reusable experience reduces future-task error and teaching demand following a power-law trend.
arXiv:2609.38178v1 Announce Type: cross Abstract: Robots deployed in the physical world must be able to improve beyond their initial training as they encounter new situations and failures. For this i...
arXiv:2610.20474v1 Announce Type: new Abstract: Pretrained vision-language-action models (VLAs) often fail midway through unseen long-horizon tasks. Recursive self-improvement (RSI) offers a remedy,...
Reconstruct, Practice, Go Real (RPG) is a framework that enables autonomous improvement of robot execution systems without updating model weights. RPG identifies manipulation capabilities from offline data, creates simulation practice tasks, and uses feedback to diagnose failures, develop new symbolic skills, refine existing ones, and revise the system prompt. Across 22 manipulation tasks, RPG raises task success from 28.6% after the first practice round to 95.0% after 15 rounds, outperforming baselines and achieving perfect success on 30 physical trials after calibration.
RoboRSI is a robot self‑improvement system that uses Top‑Down Skill Refinement to decompose tasks into compound, atomic, and base skills with clear responsibilities and input‑output contracts. The system coordinates a Manager, Planner, Engineer, and Reviewer to plan, execute, diagnose, and validate skill revisions, allowing humans to guide objectives and corrections. On a mobile manipulator, RoboRSI successfully learns multi‑object household cleanup over 104 rounds and outperforms the strongest baseline by 2.7 to 11.0 percentage points on several simulation benchmarks.
arXiv:2607. 00272v1 Announce Type: cross Abstract: Traditional robot programming is challenging: it requires orchestrating multimodal perception, managing physical contact dynamics, and handling diverse configurations and execution failures.
arXiv:2610.14625v1 Announce Type: new Abstract: Recursive self-improvement (RSI) offers a route to AI systems that participate in their own continued development. Recent methods allow language agents...
Robot foundation models provide strong visuomotor control, yet their performance can degrade when object positions or task instructions change. Further improvements often require post-training on subs...