Safe and efficient object manipulation is a key enabler of many real-world robot applications. However, this is challenging because robot operation must be robust to a range of sensor and actuator uncertainties. In this paper, we present a physics-informed causal-inference-based framework for a robot to probabilistically reason about candidate actions in a block stacking task in a partially observable setting. We integrate a physics-based simulation of the rigid-body system dynamics with a causal Bayesian network (CBN) formulation to define a causal generative probabilistic model of the robot decision-making process. Using simulation-based Monte Carlo experiments, we demonstrate our framework's ability to successfully: (1) predict block tower stability with high accuracy (Pred Acc: 88.6%); and, (2) select an approximate next-best action for the block stacking task, for execution by an integrated robot system, achieving 94.2% task success rate. We also demonstrate our framework's suitability for real-world robot systems by demonstrating successful task executions with a domestic support robot, with perception and manipulation sub-system integration. Hence, we show that by embedding physics-based causal reasoning into robots' decision-making processes, we can make robot task execution safer, more reliable, and more robust to various types of uncertainty.

翻译：安全高效的对象操作是许多真实机器人应用的关键使能技术。然而，由于机器人操作必须对传感器与执行器的多种不确定性具有鲁棒性，这构成了重大挑战。本文提出一种基于物理信息的因果推断框架，使机器人在部分可观测条件下，能够对积木堆叠任务中的候选动作进行概率化推理。我们将刚体系统动力学的物理仿真与因果贝叶斯网络（CBN）建模相结合，构建了机器人决策过程的因果生成概率模型。通过基于仿真的蒙特卡洛实验，我们验证了该框架具备以下能力：（1）以88.6%的预测准确率精准预测积木塔稳定性；（2）以94.2%的任务成功率实现集成机器人系统对积木堆叠任务的近似下一步最优动作选择。我们还通过家庭辅助机器人的感知与操作子系统集成实验，成功演示了该框架在真实机器人系统中的适用性。结果表明，将基于物理的因果推理嵌入机器人决策过程，可使任务执行更加安全、可靠，并对多种不确定性具有更强的鲁棒性。