Monte Carlo methods have been used extensively in the area of stochastic programming. As with other methods that involve a level of uncertainty, theoretical properties are required...
Abstract— We propose to improve the locomotive performance of humanoid robots by using approximated biped stepping and walking dynamics with reinforcement learning (RL). Although...
Jun Morimoto, Christopher G. Atkeson, Gen Endo, Go...
—Robotic systems need to be able to plan control actions that are robust to the inherent uncertainty in the real world. This uncertainty arises due to uncertain state estimation,...
Lars Blackmore, Masahiro Ono, Askar Bektassov, Bri...
This paper describes a program called Pret that automates system identification, the process of finding a dynamical model of a black-box system. Pret performs both structural iden...
Tiled architectures can provide a model for early estimation of global interconnect costs. A design exploration tool for reconfigurable architectures is currently under developmen...
Lilian Bossuet, Wayne Burleson, Guy Gogniat, Vikas...