# Multimodal Coherent Explanation Generation of Robot Failures

arXiv: https://arxiv.org/abs/2410.00659 · HTML: https://mlsanity.com/paper/2410.00659/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 40 references that are in the map.
- First posted on arXiv 1 Oct 2024.

## Builds on (most cited first, 24 of 24)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://mlsanity.com/paper/1704.05426/) (2017)
- [DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing](https://mlsanity.com/paper/2111.09543/) (2021)
- [Concept Bottleneck Models](https://mlsanity.com/paper/2007.04612/) (2020)
- [AI2-THOR: An Interactive 3D Environment for Visual AI](https://mlsanity.com/paper/1712.05474/) (2017)
- [Multimodal Explanations: Justifying Decisions and Pointing to the Evidence](https://mlsanity.com/paper/1802.08129/) (2018)
- [REFLECT: Summarizing Robot Experiences for Failure Explanation and Correction](https://mlsanity.com/paper/2306.15724/) (2023)
- [Simple BERT Models for Relation Extraction and Semantic Role Labeling](https://mlsanity.com/paper/1904.05255/) (2019)
- [A Token-level Reference-free Hallucination Detection Benchmark for Free-form Text Generation](https://mlsanity.com/paper/2104.08704/) (2021)
- [End-to-End Multimodal Fact-Checking and Explanation Generation: A Challenging Dataset and Models](https://mlsanity.com/paper/2205.12487/) (2022)
- [Automated Rationale Generation: A Technique for Explainable AI and its Effects on Human Perceptions](https://mlsanity.com/paper/1901.03729/) (2019)
- [GLocalX -- From Local to Global Explanations of Black Box AI Models](https://mlsanity.com/paper/2101.07685/) (2021)
- [Explainable AI for Robot Failures: Generating Explanations that Improve User Assistance in Fault Recovery](https://mlsanity.com/paper/2101.01625/) (2021)
- [Clue: Cross-modal Coherence Modeling for Caption Generation](https://mlsanity.com/paper/2005.00908/) (2020)
- [Support or Refute: Analyzing the Stance of Evidence to Detect Out-of-Context Mis- and Disinformation](https://mlsanity.com/paper/2311.01766/) (2023)
- [A Study on Multimodal and Interactive Explanations for Visual Question Answering](https://mlsanity.com/paper/2003.00431/) (2020)
- [FINO-Net: A Deep Multimodal Sensor Fusion Framework for Manipulation Failure Detection](https://mlsanity.com/paper/2011.05817/) (2020)
- [Semantic-Based Explainable AI: Leveraging Semantic Scene Graphs and Pairwise Ranking to Explain Robot Failures](https://mlsanity.com/paper/2108.03554/) (2021)
- [Why did I fail? A Causal-based Method to Find Explanations for Robot Failures](https://mlsanity.com/paper/2204.04483/) (2022)
- [REX: Reasoning-aware and Grounded Explanation](https://mlsanity.com/paper/2203.06107/) (2022)
- [Understanding, Categorizing and Predicting Semantic Image-Text Relations](https://mlsanity.com/paper/1906.08595/) (2019)
- [Feature Guided Search for Creative Problem Solving Through Tool Construction](https://mlsanity.com/paper/2008.10685/) (2020)
- [A Surrogate Model Framework for Explainable Autonomous Behaviour](https://mlsanity.com/paper/2305.19724/) (2023)
- [What went wrong?: Identification of Everyday Object Manipulation Anomalies](https://mlsanity.com/paper/2001.09084/) (2020)

## Built on by (most cited first, 1 of 1)

- [Multi-Scenario Reasoning: Unlocking Cognitive Autonomy in Humanoid Robots for Multimodal Understanding](https://mlsanity.com/paper/2412.20429/) (2024)

## Abstract (arXiv)

The explainability of a robot's actions is crucial to its acceptance in social spaces. Explaining why a robot fails to complete a given task is particularly important for non-expert users to be aware of the robot's capabilities and limitations. So far, research on explaining robot failures has only considered generating textual explanations, even though several studies have shown the benefits of multimodal ones. However, a simple combination of multiple modalities may lead to semantic incoherence between the information across different modalities - a problem that is not well-studied. An incoherent multimodal explanation can be difficult to understand, and it may even become inconsistent with what the robot and the human observe and how they perform reasoning with the observations. Such inconsistencies may lead to wrong conclusions about the robot's capabilities. In this paper, we introduce an approach to generate coherent multimodal explanations by checking the logical coherence of explanations from different modalities, followed by refinements as required. We propose a classification approach for coherence assessment, where we evaluate if an explanation logically follows another. Our experiments suggest that fine-tuning a neural network that was pre-trained to recognize textual entailment, performs well for coherence assessment of multimodal explanations. Code & data: https://pradippramanick.github.io/coherent-explain/.