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知并明确设计机器人可能遇到的所有场景。因此，以这种方式设计的机器人是脆弱的，在高度受控环境之外容易失败。因果模型提供了一个严谨的框架，用于编码支配机器人与其环境交互的因果关系的正式知识，以及真实世界机器人通常遇到的噪声和不确定性的概率表示。结合因果推断，这些模型使自主智能体能够理解、推理和解释其环境。本研究聚焦于机器人堆块任务问题，因为该任务展示了基础的感知和操作能力，这些能力是许多应用（包括仓储物流和家庭辅助机器人）所必需的。我们提出了一种新颖的因果概率框架，将物理模拟能力嵌入结构因果模型中，使机器人能够感知和评估堆块任务的当前状态，根据放置候选推理下一个最佳动作，并生成事后反事实解释。我们提供了下一个最佳动作选择的示例结果，并概述了在模拟和真实世界机器人堆块任务中计划的实验。