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SLoMo: A General System for Legged Robot Motion Imitation from Casual Videos
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SLoMo: A General System for Legged Robot Motion Imitation from Casual Videos Abstract We present SLoMo: a first-of-its-kind framework for transferring skilled motions from casually captured video footage of humans and animals to legged robots. SLoMo works in three stages: 1) synthesize a physically plausible reconstructed key-point trajectory from monocular videos; 2) optimize a dynamically feasible reference trajectory for the robot offline that includes body and foot motion, as well as contact sequences that closely tracks the key points; 3) track the reference trajectory online using a general-purpose model-predictive controller on robot hardware. Traditional motion imitation for legged motor skills often requires expert animators, collaborative demonstrations, and/or expensive motion capture equipment, all of which limits scalability. Instead, SLoMo only relies on easy-to-obtain monocular video footage, readily available in online repositories such as YouTube. It converts videos into motion primitives that can be executed reliably by real-world robots. We demonstrate our approach by transferring the motions of cats, dogs, and humans to example robots including a quadruped (on hardware) and a humanoid (in simulation). To the best knowledge of the authors, this is the first attempt at a general-purpose motion transfer framework that imitates animal and human motions on legged robots directly from casual videos without artificial markers or labels. paper link: https://ieeexplore.ieee.org/abstract/document/10246373 code : https://github.com/johnzhang3/SLoMo?tab=readme-ov-file 假开源？ video: https://www.youtube.com/watch?v=bvoM-nBd7lM
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