mlsanityData to 13 Oct 2026

Demonstration Based Explainable AI for Learning from Demonstration Methods

arXiv 2410.05715 ↗First posted 8 Oct 2024cs.ROcited by 1

On the map: Offline RL and imitation learning from data → Robot learning from demonstration

Abstract (arXiv)

Learning from Demonstration (LfD) is a powerful type of machine learning that can allow novices to teach and program robots to complete various tasks. However, the learning process for these systems may still be difficult for novices to interpret and understand, making effective teaching challenging. Explainable artificial intelligence (XAI) aims to address this challenge by explaining a system to the user. In this work, we investigate XAI within LfD by implementing an adaptive explanatory feedback system on an inverse reinforcement learning (IRL) algorithm. The feedback is implemented by demonstrating selected learnt trajectories to users. The system adapts to user teaching by categorizing and then selectively sampling trajectories shown to a user, to show a representative sample of both successful and unsuccessful trajectories. The system was evaluated through a user study with 26 participants teaching a robot a navigation task. The results of the user study demonstrated that the proposed explanatory feedback system can improve robot performance, teaching efficiency and user understanding of the robot.

Key facts. Cited by 1 paper in the map (data through 13 Oct 2026). Placed in Robot learning from demonstration, one of 13 directions in Offline RL and imitation learning from data. Builds on 14 of its 44 references that are in the map. First posted on arXiv 8 Oct 2024.

Builds on

14 of 44 references.

References · 14

  1. 2022Training language models to follow instructions with human feedbackcited by 15,856
  2. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,055
  3. 2019Fine-Tuning Language Models from Human Preferencescited by 2,139
  4. 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,547
  5. 2015Show, Attend and Tell: Neural Image Caption Generation with Visual Attentioncited by 1,421
  6. 2020GraphLIME: Local Interpretable Model Explanations for Graph Neural Networkscited by 209
  7. 2015Maximum Entropy Deep Inverse Reinforcement Learningcited by 175
  8. 2019Grasping in the Wild:Learning 6DoF Closed-Loop Grasping from Low-Cost Demonstrationscited by 104
  9. 2020Learning Reward Functions from Diverse Sources of Human Feedback: Optimally Integrating Demonstrations and Preferencescited by 71
  10. 2019Asking Easy Questions: A User-Friendly Approach to Active Reward Learningcited by 67
  11. 2021Explainable AI for Robot Failures: Generating Explanations that Improve User Assistance in Fault Recoverycited by 32
  12. 2022The Utility of Explainable AI in Ad Hoc Human-Machine Teamingcited by 30
2 more
  1. 2020Four Years in Review: Statistical Practices of Likert Scales in Human-Robot Interaction Studiescited by 12
  2. 2019Nonverbal Robot Feedback for Human Teacherscited by 4

Built on by

Cited by year (2026 to 13 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Robots That Know What to Ask: Recovering Misaligned Rewards through Targeted Explanationscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2023A Survey of Demonstration Learning
  2. 2018Machine Teaching for Inverse Reinforcement Learning: Algorithms and Applications
  3. 2022Fast Lifelong Adaptive Inverse Reinforcement Learning from Demonstrations
  4. 2022Autonomous Assessment of Demonstration Sufficiency via Bayesian Inverse Reinforcement Learning
  5. 2022Quantifying Demonstration Quality for Robot Learning and Generalization
  6. 2020Learning from Suboptimal Demonstration via Self-Supervised Reward Regression
  7. 2025Active Robot Curriculum Learning from Online Human Demonstrations
  8. 2019Interactive Teaching Algorithms for Inverse Reinforcement Learning