用反事实推理提升机器人感知故障诊断能力，无需额外传感器。

Perception systems provide a rich understanding of the environment for autonomous systems, shaping decisions in all downstream modules. Hence, accurate detection and isolation of faults in perception systems is important. Faults in perception systems pose particular challenges: faults are often tied to the perceptual context of the environment, and errors in their multi-stage pipelines can propagate across modules. To address this, we adopt a counterfactual reasoning approach to propose a framework for fault detection and isolation (FDI) in perception systems. As opposed to relying on physical redundancy (i.e., having extra sensors), our approach utilizes analytical redundancy with counterfactual reasoning to construct perception reliability tests as causal outcomes influenced by system states and fault scenarios. Counterfactual reasoning generates reliability test results under hypothesized faults to update the belief over fault hypotheses. We derive both passive and active FDI methods. While the passive FDI can be achieved by belief updates, the active FDI approach is defined as a causal bandit problem, where we utilize Monte Carlo Tree Search (MCTS) with upper confidence bound (UCB) to find control inputs that maximize a detection and isolation metric, designated as Effective Information (EI). The mentioned metric quantifies the informativeness of control inputs for FDI. We demonstrate the approach in a robot exploration scenario, where a space robot performing vision-based navigation actively adjusts its attitude to increase EI and correctly isolate faults caused by sensor damage, dynamic scenes, and perceptual degradation.