Learning a Value Function Based Heuristic for Physics Based Manipulation Planning in Clutter

In this work, we propose interleaving planning and execution, in a closed-loop setting, using a Receding Horizon Planner (RHP) for pushing manipulation in clutter. In this context, we address the problem of finding a suitable value function based heuristic for planning in near real-time, and for estimating the reward from the horizon to the goal. We estimate such a value function first by using plans generated by an existing sampling-based planner. Then, we further optimize the value function through reinforcement learning.