X-Morph: Human Motion Priors for Scalable Robot Learning Across Morphologies

• URL: http://arxiv.org/abs/2606.30290v1
• Date: Mon, 29 Jun 2026 13:31:27 GMT
• Title: X-Morph: Human Motion Priors for Scalable Robot Learning Across Morphologies
• Abstract summary: We present X-Morph, a human-motion-to-robot-behavior that converts human motion into loco-manipulation policies.<n>A cross-morphology stage converts human motions into kinematically plausible, intent-preserving robot references.<n>The resulting policies track diverse retargeted motions, repurpose to unseen human motions, and support use cases including video-based teleoperation.
• Score: 6.9484599086239465
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent progress in humanoid behavior models has been driven in large part by abundant human motion data, but comparable motion data is scarce for non-humanoid legged robots such as quadrupeds, hexapods, and quadruped manipulators. A promising alternative is to repurpose human motion across embodiments; however, direct retargeting often produces motions that are visually plausible yet physically inconsistent or difficult to track under robot dynamics. We present X-Morph, a human-motion-to-robot-behavior pipeline that converts human motion into deployable locomotion and loco-manipulation policies for diverse non-humanoid legged morphologies. A cross-morphology retargeting stage converts human motions into kinematically plausible, intent-preserving robot references, which are then tracked by a privileged RL policy and distilled into a causal student policy. We evaluate X-Morph on three morphologically distinct platforms: a quadruped, a hexapod, and a quadruped equipped with a manipulator. The resulting policies track diverse retargeted motions, generalize to unseen human motions, and support downstream use cases including video-based teleoperation, behavior-prior control, and text-conditioned motion generation. These results suggest that large-scale human motion can serve as a substrate for learning broad, reusable behavior priors beyond humanoid robots. Project page: https://maker-rat.github.io/morph/

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