Uncertainties in the real world mean that is impossible for system designers to anticipate and explicitly design for all scenarios that a robot might encounter. Thus, robots designed like this are fragile and fail outside of highly-controlled environments. Causal models provide a principled framework to encode formal knowledge of the causal relationships that govern the robot's interaction with its environment, in addition to probabilistic representations of noise and uncertainty typically encountered by real-world robots. Combined with causal inference, these models permit an autonomous agent to understand, reason about, and explain its environment. In this work, we focus on the problem of a robot block-stacking task due to the fundamental perception and manipulation capabilities it demonstrates, required by many applications including warehouse logistics and domestic human support robotics. We propose a novel causal probabilistic framework to embed a physics simulation capability into a structural causal model to permit robots to perceive and assess the current state of a block-stacking task, reason about the next-best action from placement candidates, and generate post-hoc counterfactual explanations. We provide exemplar next-best action selection results and outline planned experimentation in simulated and real-world robot block-stacking tasks.

翻译：现实世界中的不确定性意味着系统设计者无法预见并显式设计所有机器人可能遭遇的场景。因此，此类设计的机器人极为脆弱，仅在高度受控环境之外便会失效。因果模型提供了一个原则性框架，可编码支配机器人与环境交互的因果关系的正式知识，同时包含现实机器人通常遇到的噪声与不确定性的概率表征。结合因果推断，这些模型使自主体能够理解、推理并解释其所在环境。本研究聚焦于机器人积木堆叠任务问题，因其展现了基础感知与操作能力，这些能力是仓库物流及家庭人机协作机器人等诸多应用场景所必需的。我们提出了一种新颖的因果概率框架，将物理仿真能力嵌入结构因果模型，使机器人能够感知并评估积木堆叠任务的当前状态，从候选放置位置中推理出最优后续动作，并生成事后反事实解释。我们提供了示例性最优后续动作选择结果，并概述了在仿真与真实世界机器人积木堆叠任务中计划的实验方案。