# Why did I fail? A Causal-based Method to Find Explanations for Robot Failures

arXiv: https://arxiv.org/abs/2204.04483 · HTML: https://mlsanity.com/paper/2204.04483/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 31 references that are in the map.
- First posted on arXiv 9 Apr 2022.

## Builds on (most cited first, 13 of 13)

- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [DAGs with NO TEARS: Continuous Optimization for Structure Learning](https://mlsanity.com/paper/1803.01422/) (2018)
- [Neural Motifs: Scene Graph Parsing with Global Context](https://mlsanity.com/paper/1711.06640/) (2017)
- [D'ya like DAGs? A Survey on Structure Learning and Causal Discovery](https://mlsanity.com/paper/2103.02582/) (2021)
- [The Emerging Landscape of Explainable AI Planning and Decision Making](https://mlsanity.com/paper/2002.11697/) (2020)
- [Explainable AI for Robot Failures: Generating Explanations that Improve User Assistance in Fault Recovery](https://mlsanity.com/paper/2101.01625/) (2021)
- [Causal Reasoning in Simulation for Structure and Transfer Learning of Robot Manipulation Policies](https://mlsanity.com/paper/2103.16772/) (2021)
- [DoWhy: Addressing Challenges in Expressing and Validating Causal Assumptions](https://mlsanity.com/paper/2108.13518/) (2021)
- [Review of The Book of Why: The New Science of Cause and Effect](https://mlsanity.com/paper/2003.11635/) (2020)
- [Semantic-Based Explainable AI: Leveraging Semantic Scene Graphs and Pairwise Ranking to Explain Robot Failures](https://mlsanity.com/paper/2108.03554/) (2021)
- [Learning Discrete Bayesian Networks from Continuous Data](https://mlsanity.com/paper/1512.02406/) (2015)
- [Work in Progress -- Automated Generation of Robotic Planning Domains from Observations](https://mlsanity.com/paper/2107.04614/) (2021)
- [Ontology-Assisted Generalisation of Robot Action Execution Knowledge](https://mlsanity.com/paper/2107.09353/) (2021)

## Built on by (most cited first, 13 of 13)

- [REFLECT: Summarizing Robot Experiences for Failure Explanation and Correction](https://mlsanity.com/paper/2306.15724/) (2023)
- [Automating Robot Failure Recovery Using Vision-Language Models With Optimized Prompts](https://mlsanity.com/paper/2409.03966/) (2024)
- [CAR-DESPOT: Causally-Informed Online POMDP Planning for Robots in Confounded Environments](https://mlsanity.com/paper/2304.06848/) (2023)
- [CURE: Simulation-Augmented Auto-Tuning in Robotics](https://mlsanity.com/paper/2402.05399/) (2024)
- [STAR: A Foundation Model-driven Framework for Robust Task Planning and Failure Recovery in Robotic Systems](https://mlsanity.com/paper/2503.06060/) (2025)
- [Exploring Causality for HRI: A Case Study on Robotic Mental Well-being Coaching](https://mlsanity.com/paper/2503.11684/) (2025)
- [Hierarchical Vision Language Action Model Using Success and Failure Demonstrations](https://mlsanity.com/paper/2512.03913/) (2025)
- [Multimodal Coherent Explanation Generation of Robot Failures](https://mlsanity.com/paper/2410.00659/) (2024)
- [Learning Causal Structure Distributions for Robust Planning](https://mlsanity.com/paper/2508.06742/) (2025)
- [COBRA-PPM: A Causal Bayesian Reasoning Architecture Using Probabilistic Programming for Robot Manipulation Under Uncertainty](https://mlsanity.com/paper/2403.14488/) (2024)
- [Robot Pouring: Identifying Causes of Spillage and Selecting Alternative Action Parameters Using Probabilistic Actual Causation](https://mlsanity.com/paper/2502.09395/) (2025)
- [Generating Causal Explanations of Vehicular Agent Behavioural Interactions with Learnt Reward Profiles](https://mlsanity.com/paper/2503.14557/) (2025)
- [Adapting Robot's Explanation for Failures Based on Observed Human Behavior in Human-Robot Collaboration](https://mlsanity.com/paper/2504.09717/) (2025)

## Abstract (arXiv)

Robot failures in human-centered environments are inevitable. Therefore, the ability of robots to explain such failures is paramount for interacting with humans to increase trust and transparency. To achieve this skill, the main challenges addressed in this paper are I) acquiring enough data to learn a cause-effect model of the environment and II) generating causal explanations based on that model. We address I) by learning a causal Bayesian network from simulation data. Concerning II), we propose a novel method that enables robots to generate contrastive explanations upon task failures. The explanation is based on setting the failure state in contrast with the closest state that would have allowed for a successful execution. This state is found through breadth-first search and is based on success predictions from the learned causal model. We assessed our method in two different scenarios I) stacking cubes and II) dropping spheres into a container. The obtained causal models reach a sim2real accuracy of 70% and 72%, respectively. We finally show that our novel method scales over multiple tasks and allows real robots to give failure explanations like 'the upper cube was stacked too high and too far to the right of the lower cube.'