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

• URL: http://arxiv.org/abs/2607.14047v3
• Date: Wed, 22 Jul 2026 04:19:08 GMT
• Title: Zero2Skill: Bootstrapping Robot Skills through Autonomous Data Collection, Training, and Deployment
• Abstract summary: Existing pipelines reduce human effort via self-resetting, VLM verification, or language-guided correction.<n>We present Zero2Skill, a human-robot symbiotic agentic system in which corrections are retained and reused across rounds.
• Score: 53.913175773174636
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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 reused across rounds. The collection loop collects, verifies, and resets autonomously, pausing for a remote operator only when a phase exhausts an explicit retry budget. An LLM parser maps each natural-language utterance to a structured adjustment stored in Corrective Memory, so addressed failure modes typically need not be corrected again under the same conditions. On a real-robot desktop-clearing testbed, Zero2Skill matches teleoperation episode success while reducing human working time to 16%. Language corrections improve verifier-human agreement in all four evaluated settings and raise average single-attempt success from 12.5% to 47.5% (arm-selection: 20.0% to 50.0%). Policies fine-tuned on Zero2Skill data match teleoperation-trained policy success at a fraction of collection human cost.

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