[Submitted on 24 Oct 2026]
Title:MOTIO: Agentic Skill Engineering for Reality-Grounded Humanoid Loco-Manipulation
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Abstract:We present MOTIO, an agentic skill-engineering system that turns ordinary human-action videos, reality-grounded 3D assets, motion references, and reusable robot skills into executable humanoid loco-manipulation programs. Through in-context task and contact reasoning, an agent constructs staged robot-native interactions and improves them using physics execution, multi-frame feedback, and human validation---without task-specific teleoperation or robot demonstration collection. Agentic 3D supplies metric product and environment assets, while existing whole-body, inverse-kinematics, and dexterous-hand interfaces realize the programs. Across 33 timestamped first approvals, the median recorded agent-turn time is 97.0 minutes and request-to-approval duration is 2.80 hours. Simulation-side layout and object variation prepare a long-horizon bottle-disposal skill for a continuous 101.75-second physical G1 execution in a scanned office; physical refinement reduces screw-tightening thread residual by 76.0%. Agent-generated skills additionally supervise BC/DAgger distillation into one shared reference policy, completing 18 simulated tasks under the same checkpoint without inference-time expert intervention. Project page: this https URL
Comments: 24 pages, including 11 pages of supplementary material
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2610.33446 [cs.RO]
(or arXiv:2610.33446v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.33446
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Zhe Li [view email] [v1] Sat, 24 Oct 2026 04:55:00 UTC (21,783 KB)
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