# Context-Continuous Preference Learning for Exoskeleton Personalization

arXiv: https://arxiv.org/abs/2609.28427 · HTML: https://mlsanity.com/paper/2609.28427/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 54 references that are in the map.
- First posted on arXiv 23 Sep 2026.

## Builds on (most cited first, 12 of 12)

- [Characterizing and Avoiding Negative Transfer](https://mlsanity.com/paper/1811.09751/) (2018)
- [Contextual Dueling Bandits](https://mlsanity.com/paper/1502.06362/) (2015)
- [Preferential Bayesian Optimization](https://mlsanity.com/paper/1704.03651/) (2017)
- [Time-Varying Gaussian Process Bandit Optimization](https://mlsanity.com/paper/1601.06650/) (2016)
- [Preference-Based Learning for Exoskeleton Gait Optimization](https://mlsanity.com/paper/1909.12316/) (2019)
- [ROIAL: Region of Interest Active Learning for Characterizing Exoskeleton Gait Preference Landscapes](https://mlsanity.com/paper/2011.04812/) (2020)
- [Human Preference-Based Learning for High-dimensional Optimization of Exoskeleton Walking Gaits](https://mlsanity.com/paper/2003.06495/) (2020)
- [A tutorial on learning from preferences and choices with Gaussian Processes](https://mlsanity.com/paper/2403.11782/) (2024)
- [Rapid Online Learning of Hip Exoskeleton Assistance Preferences](https://mlsanity.com/paper/2502.15366/) (2025)
- [Toward Context-Aware Exoskeleton Assistance: Integrating Computer Vision Payload Estimation with a Multi-Metric Optimization Space](https://mlsanity.com/paper/2508.06207/) (2025)
- [Continual Online Personalization of Exoskeleton Control via Manifold-Aware Experience Replay](https://mlsanity.com/paper/2606.17455/) (2026)
- [Personalized Lower-limb Exoskeleton Assistance via Preference-based Bayesian Optimization](https://mlsanity.com/paper/2608.09015/) (2026)

## Abstract (arXiv)

Personalizing exoskeleton assistance across operating conditions is constrained by the time and physical effort required to collect user feedback. We examined whether a user's preference landscape varies smoothly across operating conditions and when this continuity supports learning from limited feedback. We propose Context-Continuous Preference Learning (CCPL), a Gaussian-process preference model that shares observations across nearby contexts while retaining context-specific utility estimates. We evaluated CCPL through simulations and retrospective analyses of ankle and elbow exoskeleton preference data from nine healthy adults. In simulations, CCPL improved reconstruction and preference-based Bayesian optimization relative to independent learning when preferences varied smoothly, but showed negative transfer when continuity was weak. In both human studies, full-data reference landscapes estimated separately for each participant and context tended to be more similar between nearby operating conditions. With five exposures per context, CCPL increased mean reconstruction correlation with these references from 0.644 to 0.720 for ankle assistance and from 0.476 to 0.526 for elbow assistance relative to independent learning. The five-exposure budget was approximately 37% lower for ankle and 17% lower for elbow than the estimated independent-learning budgets needed to match these correlations. CCPL also improved held-out response prediction relative to independent learning, while benefits over pooled learning varied. These findings support context continuity as a basis for sharing preference observations under limited feedback, although benefits for online personalization in humans remain to be established.