2 citations · 2 across the 3 of their papers we have counts for
3 papers
Robot Policy Learning from Demonstration Using Advantage Weighting and Early Termination
Abdalkarim Mohtasib, Gerhard Neumann, Heriberto Cuayahuitl
Learning robotic tasks in the real world is still highly challenging and effective practical solutions remain to be found. Traditional methods used in this area are imitation learn…
Reward-Based Environment States for Robot Manipulation Policy Learning
Cédérick Mouliets, Isabelle Ferrané, Heriberto Cuayáhuitl
Training robot manipulation policies is a challenging and open problem in robotics and artificial intelligence. In this paper we propose a novel and compact state representation ba…
Training an Interactive Humanoid Robot Using Multimodal Deep Reinforcement Learning
Heriberto Cuayáhuitl, Guillaume Couly, Clément Olalainty
Training robots to perceive, act and communicate using multiple modalities still represents a challenging problem, particularly if robots are expected to learn efficiently from sma…