mlsanityData to 14 Oct 2026

Explainable Autonomous Robots: A Survey and Perspective

arXiv 2105.02658 ↗First posted 6 May 2021cs.AIcited by 15

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Abstract (arXiv)

Advanced communication protocols are critical to enable the coexistence of autonomous robots with humans. Thus, the development of explanatory capabilities is an urgent first step toward autonomous robots. This survey provides an overview of the various types of "explainability" discussed in machine learning research. Then, we discuss the definition of "explainability" in the context of autonomous robots (i.e., explainable autonomous robots) by exploring the question "what is an explanation?" We further conduct a research survey based on this definition and present some relevant topics for future research.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Builds on 30 of its 94 references that are in the map. First posted on arXiv 6 May 2021.

Builds on

30 of 94 references.

References · 30

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,442
  4. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
  5. 2017Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)cited by 1,319
  6. 2017Explanation in Artificial Intelligence: Insights from the Social Sciencescited by 1,206
  7. 2018A Survey Of Methods For Explaining Black Box Modelscited by 973
  8. 2018Recurrent World Models Facilitate Policy Evolutioncited by 785
  9. 2017Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Modelscited by 206
  10. 2018Programmatically Interpretable Reinforcement Learningcited by 193
  11. 2019Explainable Reinforcement Learning Through a Causal Lenscited by 177
  12. 2020Explainable Reinforcement Learning: A Surveycited by 141
18 more
  1. 2018State Representation Learning for Control: An Overviewcited by 127
  2. 2019Automated Rationale Generation: A Technique for Explainable AI and its Effects on Human Perceptionscited by 81
  3. 2020World Model as a Graph: Learning Latent Landmarks for Planningcited by 69
  4. 2019Interestingness Elements for Explainable Reinforcement Learning: Understanding Agents' Capabilities and Limitationscited by 50
  5. 2017Grader variability and the importance of reference standards for evaluating machine learning models for diabetic retinopathycited by 44
  6. 2018Contrastive Explanations for Reinforcement Learning in terms of Expected Consequencescited by 44
  7. 2020The Emerging Landscape of Explainable AI Planning and Decision Makingcited by 44
  8. 2018Learning Finite State Representations of Recurrent Policy Networkscited by 37
  9. 2017Enabling Robots to Communicate their Objectivescited by 35
  10. 2019Explainability in Human-Agent Systemscited by 34
  11. 2021Explainable AI for Robot Failures: Generating Explanations that Improve User Assistance in Fault Recoverycited by 32
  12. 2015Symbol Emergence in Robotics: A Surveycited by 31
  13. 2018Explicability? Legibility? Predictability? Transparency? Privacy? Security? The Emerging Landscape of Interpretable Agent Behaviorcited by 30
  14. 2020Joint Mind Modeling for Explanation Generation in Complex Human-Robot Collaborative Taskscited by 15
  15. 2020Distal Explanations for Model-free Explainable Reinforcement Learningcited by 12
  16. 2018Autonomous Self-Explanation of Behavior for Interactive Reinforcement Learning Agentscited by 10
  17. 2020Designing Environments Conducive to Interpretable Robot Behaviorcited by 8
  18. 2020Sequential Explanations with Mental Model-Based Policiescited by 8

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 2; 2023: 1; 2024: 6; 2025: 5; 2026: 1.

15 papers.

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  1. 2024Trust in AI: Progress, Challenges, and Future Directionscited by 35
  2. 2022DreamingV2: Reinforcement Learning with Discrete World Models without Reconstructioncited by 29
  3. 2025Episodic memory in AI agents poses risks that should be studied and mitigatedcited by 11
  4. 2024Distilling Reinforcement Learning Policies for Interpretable Robot Locomotion: Gradient Boosting Machines and Symbolic Regressioncited by 5
  5. 2022Explain yourself! Effects of Explanations in Human-Robot Interactioncited by 2
  6. 2024Enhancing Trust in Autonomous Agents: An Architecture for Accountability and Explainability through Blockchain and Large Language Modelscited by 2
  7. 2023Modifying RL Policies with Imagined Actions: How Predictable Policies Can Enable Users to Perform Novel Taskscited by 1
  8. 2024Vehicle Behavior Prediction by Episodic-Memory Implanted NDTcited by 0
  9. 2024Imagining In-distribution States: How Predictable Robot Behavior Can Enable User Control Over Learned Policiescited by 0
  10. 2024Enhancing Robot Assistive Behaviour with Reinforcement Learning and Theory of Mindcited by 0
  11. 2025Joint Action Language Modelling for Transparent Policy Executioncited by 0
  12. 2025Effective Explanations for Belief-Desire-Intention Robots: When and What to Explaincited by 0
3 more
  1. 2025From Facts to Foils: Designing and Evaluating Counterfactual Explanations for Smart Environmentscited by 0
  2. 2025Explaining Why Things Go Where They Go: Interpretable Constructs of Human Organizational Preferencescited by 0
  3. 2026PAANI : On Device Visual Evidence Fusion and Explainable Guidance for River Robot Simulationcited by 0