ORIGINAL RESEARCH article
Front. Robot. AI
Sec. Computational Intelligence in Robotics
This article is part of the Research TopicAdvanced Sensing, Learning and Control for Effective Human-Robot InteractionView all 3 articles
Adaptive Querying for Reward Learning from Human Feedback
Provisionally accepted
- Oregon State University, Corvallis, United States
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Learning from human feedback is a popular approach to train robots to adapt to user preferences and improve safety. Existing approaches typically consider a single querying (interaction) format when seeking human feedback and do not leverage multiple modes of user interaction with a robot. We examine how to learn a penalty function associated with unsafe behaviors using multiple forms of human feedback, by optimizing both the query state and feedback format. Our proposed adaptive feedback selection is an iterative, two-phase approach which first selects critical states for querying, and then uses information gain to select a feedback format for querying across the sampled critical states. The feedback format selection also accounts for the cost and probability of receiving feedback in a certain format. Our experiments in simulation demonstrate the sample efficiency of our approach in learning to avoid undesirable behaviors. The results of our user study with a physical robot highlight the practicality and effectiveness of adaptive feedback selection in seeking informative, user-aligned feedback that accelerate learning. Experiment videos, code and appendices are found on our website: https://tinyurl.com/AFS-learning
Keywords: information gain, Interactive Imitation Learning, Learning from human feedback, Learning from multiple formats, Robotlearning
Received: 28 Oct 2025; Accepted: 15 Dec 2025.
Copyright: © 2025 Anand, Nwagwu, Sabbe, Fitter and Saisubramanian. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Yashwanthi Anand
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