Model-based Motion Imitation for Agile, Diverse and Generalizable Quadupedal Locomotion
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Robots operating in human environments need a variety of skills, like slow and fast walking, turning, and side-stepping. However, building robot controllers that can exhibit such a large range of behaviors is challenging, and unsolved. We present an approach that uses a model-based controller for imitating different animal gaits without requiring any real-world fine-tuning. Unlike previous works that learn one policy per motion, we present a unified controller which is capable of generating four different animal gaits on the A1 robot. Our framework includes a trajectory optimization procedure that improves the quality of real-world imitation. We demonstrate our results in simulation and on a real 12-DoF A1 quadruped robot. Our result shows that our approach can mimic four animal motions, and outperform baselines learned per motion.
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