Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on Ro botics - Androids. According to news reporting from Edinburgh, United Kingdom, b y NewsRx journalists, research stated, "Planning multicontact motions in a reced ing horizon fashion requires a value function to guide the planning with respect to the future, e.g., building momentum to traverse large obstacles. Traditional ly, the value function is approximated by computing trajectories in a prediction horizon (never executed) that foresees the future beyond the execution horizon. " Financial support for this research came from EU H2020 project Enhancing Healthc are with Assistive Robotic Mobile Manipulation HARMONY. The news correspondents obtained a quote from the research from the University o f Edinburgh, "However, given the nonconvex dynamics of multicontact motions, thi s approach is computationally expensive. To enable online receding horizon plann ing (RHP) of multicontact motions, we find efficient approximations of the value function. Specifically, we propose a trajectory-based and a learning-based appr oach. In the former, namely RHP with multiple levels of model fidelity, we appro ximate the value function by computing the prediction horizon with a convex rela xed model. In the latter, namely locally guided RHP, we learn an oracle to predi ct local objectives for locomotion tasks, and we use these local objectives to c onstruct local value functions for guiding a short-horizon RHP. We evaluate both approaches in simulation by planning centroidal trajectories of a humanoid robo t walking on moderate slopes, and on large slopes where the robot cannot maintai n static balance. Our results show that locally guided RHP achieves the best com putation efficiency (95%-98.6% cycles converge online )."
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