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.

通过将基于物理的因果推理嵌入机器人的决策过程中，我们可以使机器人任务执行更安全、更可靠，对各种类型的不确定性更具鲁棒性。

机器人操纵任务中基于物理的因果推理，用于安全和稳健的下一最佳行动选择

Physics-Based Causal Reasoning for Safe & Robust Next-Best Action  Selection in Robot Manipulation Tasks

Explainable artificial intelligence is a research field that tries to provide
more transparency for autonomous intelligent systems. Explainability has been
used, particularly in reinforcement learning and robotic scenarios, to better
understand the robot decision-making process. Previous work, however, has been
widely focused on providing technical explanations that can be better
understood by AI practitioners than non-expert end-users. In this work, we make
use of human-like explanations built from the probability of success to
complete the goal that an autonomous robot shows after performing an action.
These explanations are intended to be understood by people who have no or very
little experience with artificial intelligence methods. This paper presents a
user trial to study whether these explanations that focus on the probability an
action has of succeeding in its goal constitute a suitable explanation for
non-expert end-users. The results obtained show that non-expert participants
rate robot explanations that focus on the probability of success higher and
with less variance than technical explanations generated from Q-values, and
also favor counterfactual explanations over standalone explanations.

本文探讨了透明度逐渐成为自主智能系统的必要品质，提供了人性化的基于成功概率的解释方法，并通过用户调查证明该种解释对于非专业人士更易理解和接受。