We have a new paper accepted at The Multi-disciplinary Conference on Reinforcement Learning and Decision Making. Trevor will present our new work on learning to hand over objects at the UMICH Ann Arbor. Abstract:
While significant advancements have been made recently in the field of reinforcement learning, relatively little work has been devoted to reinforcement learning in a human context. Learning in the context of a human adds a variety of additional constraints that make the problem more difficult including an increased importance on sample efficiency and the inherent unpredictability of the human counterpart. In this work we used the Sparse Latent Space Policy Search algorithm and a linear-Gaussian trajectory approximator with the objective of learning optimized, understandable trajectories for object handovers between a robot and a human with very high sample-efficiency.