Using Motion Primitives in Probabilistic Sample-Based Planning for Humanoid Robots
نویسندگان
چکیده
This paper presents a method of computing efficient and naturallooking motions for humanoid robots walking on varied terrain. It uses a small set of high-quality motion primitives (such as a fixed gait on flat ground) that have been generated offline. But rather than restrict motion to these primitives, it uses them to derive a sampling strategy for a probabilistic, sample-based planner. Results in simulation on several different terrains demonstrate a reduction in planning time and a marked increase in motion quality.
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