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Evidence record 10923 · automatically gathered

PhysClaw-0: A Symbiotic Agentic System for Robot Autonomy via Language Corrections

Autonomous data collection governs the volume and quality of real-world trajectories for manipulation policy learning. Existing pipelines reduce human effort via self-resetting, VLM verification, or language-guided correction, yet episode-scoped fixes must be reissued whenever the same failure recurs, so oversight cost grows with session length rather than with the number of distinct problems. We present PhysClaw-0, a human-robot symbiotic agentic system in which corrections are retained and reu

Record details

Published: 15 July 2026
Source: arXiv cs.HC
Category: Research
Topics: Regulation · Agents & autonomy
Retrieved: 16 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

Same publisher byline · Shared topic: Regulation

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How to cite this record

ethics.ai (15 July 2026), “PhysClaw-0: A Symbiotic Agentic System for Robot Autonomy via Language Corrections,” evidence record 10923, https://ethics.ai/record/10923 (originally published by arXiv cs.HC).

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