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arXiv · 2412.07990

Adaptive Querying for Reward Learning from Human Feedback

Yashwanthi Anand·Nnamdi Nwagwu·Kevin Sabbe·Naomi T. Fitter·Sandhya Saisubramanian
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Abstract

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.

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Yashwanthi Anand, Nnamdi Nwagwu, Kevin Sabbe, Naomi T. Fitter, Sandhya Saisubramanian. 2024-12-11. Adaptive Querying for Reward Learning from Human Feedback. https://arxiv.org/abs/2412.07990

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