Titlebook: ROBOT 2017: Third Iberian Robotics Conference; Volume 2 Anibal Ollero,Alberto Sanfeliu,Carlos Cardeira Conference proceedings 2018 Springer

闡釋

Mixed-Policy Asynchronous Deep Q-Learning such as deep neural networks, have been successfully used in both single- and multi-agent environments with high dimensional state-spaces. The multi-agent learning paradigm faces even more problems, due to the effect of several agents learning simultaneously in the environment. One of its main conc

袋鼠

Reward-Weighted GMM and Its Application to?Action-Selection in Robotized Shoe Dressingask and must select the action that maximizes success probability among a repertoire of pre-trained actions. We investigate the case in which sensory data is only available before making the decision, but not while the action is being performed. In this paper we propose to use a Gaussian Mixture Mod

吼叫

樸素

Tactile Sensing and Machine Learning for?Human and Object Recognition in?Disaster?Scenariosrios where haptic feedback provides a valuable information for the search of potential victims. To extract haptic information from the environment, a tactile sensor attached to a lightweight robotic arm is used. Then, methods based on the SURF descriptor, support vector machines (SVM), Deep Convolut

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全等

祝賀

condone

hedonic

Mercantile

Reward-Weighted GMM and Its Application to?Action-Selection in Robotized Shoe Dressingorithm to use the result of each execution to update the model, thus adapting the robot behavior to the user and evaluating the effectiveness of each pre-trained action. The proposed algorithm is applied to a robotic shoe-dressing task. Simulated and real experiments show the validity of our approach.