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FastMimic: Model-Based Motion Imitation for Agile, Diverse and Generalizable Quadrupedal Locomotion

Cited 2 time in Web of Science Cited 2 time in Scopus
Authors

Li, Tianyu; Won, Jungdam; Cho, Jeongwoo; Ha, Sehoon; Rai, Akshara

Issue Date
2023-06
Publisher
MDPI AG
Citation
Robotics, Vol.12 No.3, p. 90
Abstract
Robots operating in human environments require a diverse set of skills, including slow and fast walking, turning, side-stepping, and more. However, developing robot controllers capable of exhibiting such a broad range of behaviors is a challenging problem that necessitates meticulous investigation for each task. To address this challenge, we introduce a trajectory optimization method that resolves the kinematic infeasibility of reference animal motions. This method, combined with a model-based controller, results in a unified data-driven model-based control framework capable of imitating various animal gaits without the need for expensive simulation training or real-world fine-tuning. Our framework is capable of imitating a variety of motor skills such as trotting, pacing, turning, and side-stepping with ease. It shows superior tracking capabilities in both simulations and the real world compared to other imitation controllers, including a model-based one and a learning-based motion imitation technique.
ISSN
2218-6581
URI
https://hdl.handle.net/10371/201160
DOI
https://doi.org/10.3390/robotics12030090
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