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.

翻译：将球形物体直接放置到目标区域的任务对人类而言可以通过投掷、滑动或利用墙壁反弹物体来实现，但让机器人具备类似的物理推理能力并非易事。现有物理推理方法对数据需求量大，且难以应对真实世界固有的复杂性和不确定性。本文提出PhyPlan——一种新型物理信息规划框架，它结合物理信息神经网络（PINNs）与改进的蒙特卡洛树搜索（MCTS），使具身智能体能够执行动态物理任务。PhyPlan利用PINNs快速准确地模拟和预测动作结果，并通过MCTS进行规划，动态决策是使用基于PINN的模拟器（粗略但快速）还是直接与环境交互（精确但缓慢）以确定最优策略。在模拟三维环境中对机器人进行的评估表明，该方法能解决涉及动态技能组合的3D物理推理任务。定量分析显示，PhyPlan在以下方面表现优异：（i）与现有最优方法相比，学习新任务时的遗憾值更低；（ii）加速技能学习并提升物理推理速度；（iii）数据效率显著高于未引入物理信息的方法。