  Task-motion planning (TMP) addresses the problem of efficiently generating
executable and low-cost task plans in a discrete space such that the (initially
unknown) action costs are determined by motion plans in a corresponding
continuous space. However, a task-motion plan can be sensitive to unexpected
domain uncertainty and changes, leading to suboptimal behaviors or execution
failures. In this paper, we propose a novel framework, TMP-RL, which is an
integration of TMP and reinforcement learning (RL) from the execution
experience, to solve the problem of robust task-motion planning in dynamic and
uncertain domains. TMP-RL features two nested planning-learning loops. In the
inner TMP loop, the robot generates a low-cost, feasible task-motion plan by
iteratively planning in the discrete space and updating relevant action costs
evaluated by the motion planner in continuous space. In the outer loop, the
plan is executed, and the robot learns from the execution experience via
model-free RL, to further improve its task-motion plans. RL in the outer loop
is more accurate to the current domain but also more expensive, and using less
costly task and motion planning leads to a jump-start for learning in the real
world. Our approach is evaluated on a mobile service robot conducting
navigation tasks in an office area. Results show that TMP-RL approach
significantly improves adaptability and robustness (in comparison to TMP
methods) and leads to rapid convergence (in comparison to task planning (TP)-RL
methods). We also show that TMP-RL can reuse learned values to smoothly adapt
to new scenarios during long-term deployments.
