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EmbodiedRSI: Active Continual Robot Learning Through Hypothesis-Guided Co-Evolution

Paper recorded by Signals 4 on 2026-10-07 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-07 on arXiv · recorded by Signals 4 on 2026-10-08

Category: cs.AI · 人工智能 · first seen 2026-10-08

Abstract

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 substantial robot data, which can be costly to collect through methods such as teleoperation. Agentic harnesses can adapt around the model, but current self-evolving harnesses use robot trials inefficient

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#388 most recent of 960 cs.AI papers we have recorded · ↑ newer: Validity Without Ground Truth: What Stated-Preference Economics Offers · ↓ older: Before They Can Solve: Predicting Post-Training Coding-Agent Performan
Cite this page: EmbodiedRSI: Active Continual Robot Learning Through Hypothesis-Guided Co-Evolution: the #388 most recent of 960 cs.AI papers we have recorded (as of 2026-10-07). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/embodiedrsi-active-continual-robot-learning-through-hypothesis-guided-co-evoluti.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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