TaskMotion Planning with Reinforcement Learning for Adaptable Mobile Service Robots Taskmotion planning (TMP) addresses the problem of efficiently generating executable and lowcost task plans in a discrete space such that the (initially unknown) action costs are determined by motion plans in a corresponding continuous space. A taskmotion plan for a mobile service robot that behaves in a highly dynamic domain can be sensitive to domain uncertainty and changes, leading to suboptimal behaviors or execution failures. In this paper, we propose a novel framework, TMPRL, which is an integration of TMP and reinforcement learning (RL), to solve the problem of robust TMP in dynamic and uncertain domains. The robot first generates a lowcost, feasible taskmotion plan by iteratively planning in the discrete space and updating relevant action costs evaluated by the motion planner in continuous space. During execution, the robot learns via modelfree RL to further improve its taskmotion plans. RL enables adaptability to the current domain, but can be costly with regards to experience; using TMP, which does not rely on experience, can jumpstart the learning process before executing in the real world. TMPRL is evaluated in a mobile service robot domain where the robot navigates in an office area, showing significantly improved adaptability to unseen domain dynamics over TMP and task planning (TP)RL methods.

Learning to Explore in Motion and Interaction Tasks Model free reinforcement learning suffers from the high sampling complexity inherent to robotic manipulation or locomotion tasks. Most successful approaches typically use random sampling strategies which leads to slow policy convergence. In this paper we present a novel approach for efficient exploration that leverages previously learned tasks. We exploit the fact that the same system is used across many tasks and build a generative model for exploration based on data from previously solved tasks to improve learning new tasks. The approach also enables continuous learning of improved exploration strategies as novel tasks are learned. Extensive simulations on a robot manipulator performing a variety of motion and contact interaction tasks demonstrate the capabilities of the approach. In particular, our experiments suggest that the exploration strategy can more than double learning speed, especially when rewards are sparse. Moreover, the algorithm is robust to task variations and parameter tuning, making it beneficial for complex robotic problems.