The article introduces FootstepNet, an efficient method for bipedal footstep planning and forecasting. It addresses the challenges of designing humanoid locomotion controllers and presents a heuristic-free approach based on Deep Reinforcement Learning. The method is validated through simulation results and real-world deployment on a kid-size humanoid robot during the RoboCup 2023 competition. The content covers the introduction, problem statement, background on RL and DRL, method description, experiments conducted, deployment in RoboCup, conclusions, and references.
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by Clém... às arxiv.org 03-20-2024
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