# ROIAL: Region of Interest Active Learning for Characterizing Exoskeleton Gait Preference Landscapes

arXiv: https://arxiv.org/abs/2011.04812 · HTML: https://mlsanity.com/paper/2011.04812/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Nov 2020.

## Built on by (most cited first, 20 of 20)

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- [Principled Preferential Bayesian Optimization](https://mlsanity.com/paper/2402.05367/) (2024)
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- [SIRL: Similarity-based Implicit Representation Learning](https://mlsanity.com/paper/2301.00810/) (2023)
- [Active Reward Learning from Online Preferences](https://mlsanity.com/paper/2302.13507/) (2023)
- [Towards Modeling and Influencing the Dynamics of Human Learning](https://mlsanity.com/paper/2301.00901/) (2023)
- [Batch Active Learning of Reward Functions from Human Preferences](https://mlsanity.com/paper/2402.15757/) (2024)
- [PABBO: Preferential Amortized Black-Box Optimization](https://mlsanity.com/paper/2503.00924/) (2025)
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- [A Generalized Acquisition Function for Preference-based Reward Learning](https://mlsanity.com/paper/2403.06003/) (2024)
- [DP-Dueling: Learning from Preference Feedback without Compromising User Privacy](https://mlsanity.com/paper/2403.15045/) (2024)
- [Knowledge Gradient for Preference Learning](https://mlsanity.com/paper/2601.22335/) (2026)
- [An Approach to Investigate Public Opinion, Views, and Perspectives Towards Exoskeleton Technology](https://mlsanity.com/paper/2205.09151/) (2022)
- [Identifying Copeland Winners in Dueling Bandits with Indifferences](https://mlsanity.com/paper/2310.00750/) (2023)
- [Human-in-the-Loop Pareto Optimization: Trade-off Characterization for Assist-as-Needed Training and Performance Evaluation](https://mlsanity.com/paper/2603.23777/) (2026)
- [Active Query Synthesis for Preference Learning](https://mlsanity.com/paper/2605.26072/) (2026)
- [Context-Continuous Preference Learning for Exoskeleton Personalization](https://mlsanity.com/paper/2609.28427/) (2026)

## Abstract (arXiv)

Characterizing what types of exoskeleton gaits are comfortable for users, and understanding the science of walking more generally, require recovering a user's utility landscape. Learning these landscapes is challenging, as walking trajectories are defined by numerous gait parameters, data collection from human trials is expensive, and user safety and comfort must be ensured. This work proposes the Region of Interest Active Learning (ROIAL) framework, which actively learns each user's underlying utility function over a region of interest that ensures safety and comfort. ROIAL learns from ordinal and preference feedback, which are more reliable feedback mechanisms than absolute numerical scores. The algorithm's performance is evaluated both in simulation and experimentally for three non-disabled subjects walking inside of a lower-body exoskeleton. ROIAL learns Bayesian posteriors that predict each exoskeleton user's utility landscape across four exoskeleton gait parameters. The algorithm discovers both commonalities and discrepancies across users' gait preferences and identifies the gait parameters that most influenced user feedback. These results demonstrate the feasibility of recovering gait utility landscapes from limited human trials.