Porto / Shanghai / Taipei / Spline / Nashville / Nagoya / Genova / /
Company
Neural Information Processing Systems / Neural Networks / Autonomous Systems / Industrial Electronics / IEEE Intl / /
Country
Taiwan / Italy / Japan / United States / Portugal / China / / /
Facility
Toyota Technological Institute / /
IndustryTerm
average electric energy / average energy consumption / random search / energy minimization / proposed reinforcement learning algorithm / energy saving motion / consumed energy / accumulated consumed electric energy values / lowest energy consumption / proportional-derivative systems / good enough solution / parti-game algorithm / elastic potential energy / sub-optimal solution / via-points acquisition algorithm / energy consumption measure / electric energy / energy reduction / electric energy consumption / energy / mechanical energy / release energy / reinforcement learning algorithm / energy consumption / bipedal walking energy minimization task / average energy / good candidate solution / consumed electric energy / elastic energy usage / random search approach / electrical energy / elastic potential energy into/from muscles / energy minimization problem / real-time bipedal walking patterns / learning algorithm / /
Organization
Center of Mass / Toyota Technological Institute / /
Person
J. Peters / E. Theodorou / M. Kalakrishnan / Petar Kormushev / M. Ishikawa / V / S. Schaal / Feedback / F. Stulp / Thomas Algorithm / S. Chitta / A. Van Emmerik / J. Buchli / C. G. Atkeson / P. Pastor / Sylvain Calinon / M. J. Grey / V / J. Jun Morimoto / /
Position
ARCHER / actor / Walkk execution Inverse Kinematics Joint trajectories PID joint position controller / /
Bipedal Walking Energy Minimization by Reinforcement Learning with Evolving Policy Parameterization Petar Kormushev, Barkan Ugurlu, Sylvain Calinon, Nikolaos G. Tsagarakis, Darwin G. Caldwell Abstract— We present a lea