  Given the task of positioning a ball-like object to a goal region beyond
direct reach, humans can often throw, slide, or rebound objects against the
wall to attain the goal. However, enabling robots to reason similarly is
non-trivial. Existing methods for physical reasoning are data-hungry and
struggle with complexity and uncertainty inherent in the real world. This paper
presents PhyPlan, a novel physics-informed planning framework that combines
physics-informed neural networks (PINNs) with modified Monte Carlo Tree Search
(MCTS) to enable embodied agents to perform dynamic physical tasks. PhyPlan
leverages PINNs to simulate and predict outcomes of actions in a fast and
accurate manner and uses MCTS for planning. It dynamically determines whether
to consult a PINN-based simulator (coarse but fast) or engage directly with the
actual environment (fine but slow) to determine optimal policy. Evaluation with
robots in simulated 3D environments demonstrates the ability of our approach to
solve 3D-physical reasoning tasks involving the composition of dynamic skills.
Quantitatively, PhyPlan excels in several aspects: (i) it achieves lower regret
when learning novel tasks compared to state-of-the-art, (ii) it expedites skill
learning and enhances the speed of physical reasoning, (iii) it demonstrates
higher data efficiency compared to a physics un-informed approach.
