Zero2Skill: Bootstrapping Robot Skills through Autonomous Data Collection, Training, and Deployment

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 Zero2Skill, a human-robot symbiotic agentic system in which corrections are retained and reu

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ethics.ai (15 July 2026), “Zero2Skill: Bootstrapping Robot Skills through Autonomous Data Collection, Training, and Deployment,” evidence record 11306, https://ethics.ai/record/11306 (originally published by arXiv cs.HC).

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