# Preference-Based Learning for Exoskeleton Gait Optimization

arXiv: https://arxiv.org/abs/1909.12316 · HTML: https://mlsanity.com/paper/1909.12316/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2019.

## Built on by (most cited first, 24 of 24)

- [A Minimaximalist Approach to Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2401.04056/) (2024)
- [Active Preference-Based Gaussian Process Regression for Reward Learning](https://mlsanity.com/paper/2005.02575/) (2020)
- [Learning Multimodal Rewards from Rankings](https://mlsanity.com/paper/2109.12750/) (2021)
- [Optimal Design for Reward Modeling in RLHF](https://mlsanity.com/paper/2410.17055/) (2024)
- [qEUBO: A Decision-Theoretic Acquisition Function for Preferential Bayesian Optimization](https://mlsanity.com/paper/2303.15746/) (2023)
- [APReL: A Library for Active Preference-based Reward Learning Algorithms](https://mlsanity.com/paper/2108.07259/) (2021)
- [Active Reward Learning from Online Preferences](https://mlsanity.com/paper/2302.13507/) (2023)
- [Comparison-based Conversational Recommender System with Relative Bandit Feedback](https://mlsanity.com/paper/2208.09837/) (2022)
- [Preference-based Learning of Reward Function Features](https://mlsanity.com/paper/2103.02727/) (2021)
- [Batch Active Learning of Reward Functions from Human Preferences](https://mlsanity.com/paper/2402.15757/) (2024)
- [Bandits with Preference Feedback: A Stackelberg Game Perspective](https://mlsanity.com/paper/2406.16745/) (2024)
- [Assisted Robust Reward Design](https://mlsanity.com/paper/2111.09884/) (2021)
- [Bayesian Optimization Meets Hybrid Zero Dynamics: Safe Parameter Learning for Bipedal Locomotion Control](https://mlsanity.com/paper/2203.02570/) (2022)
- [Best Policy Learning from Trajectory Preference Feedback](https://mlsanity.com/paper/2501.18873/) (2025)
- [Preferential Multi-Objective Bayesian Optimization](https://mlsanity.com/paper/2406.14699/) (2024)
- [Enhancing Preference-based Linear Bandits via Human Response Time](https://mlsanity.com/paper/2409.05798/) (2024)
- [Mixed Likelihood Variational Gaussian Processes](https://mlsanity.com/paper/2503.04138/) (2025)
- [MO-Playground: Massively Parallelized Multi-Objective Reinforcement Learning for Robotics](https://mlsanity.com/paper/2603.09237/) (2026)
- [DP-Dueling: Learning from Preference Feedback without Compromising User Privacy](https://mlsanity.com/paper/2403.15045/) (2024)
- [Response Time Improves Choice Prediction and Function Estimation for Gaussian Process Models of Perception and Preferences](https://mlsanity.com/paper/2306.06296/) (2023)
- [A Finite Time Analysis of Thompson Sampling for Bayesian Optimization with Preferential Feedback](https://mlsanity.com/paper/2604.25025/) (2026)
- [Active Query Synthesis for Preference Learning](https://mlsanity.com/paper/2605.26072/) (2026)
- [Adaptive KappaSharp: Condition-Number Shaping for Preferential Bayesian Optimization](https://mlsanity.com/paper/2608.07859/) (2026)
- [Context-Continuous Preference Learning for Exoskeleton Personalization](https://mlsanity.com/paper/2609.28427/) (2026)

## Abstract (arXiv)

This paper presents a personalized gait optimization framework for lower-body exoskeletons. Rather than optimizing numerical objectives such as the mechanical cost of transport, our approach directly learns from user preferences, e.g., for comfort. Building upon work in preference-based interactive learning, we present the CoSpar algorithm. CoSpar prompts the user to give pairwise preferences between trials and suggest improvements; as exoskeleton walking is a non-intuitive behavior, users can provide preferences more easily and reliably than numerical feedback. We show that CoSpar performs competitively in simulation and demonstrate a prototype implementation of CoSpar on a lower-body exoskeleton to optimize human walking trajectory features. In the experiments, CoSpar consistently found user-preferred parameters of the exoskeleton's walking gait, which suggests that it is a promising starting point for adapting and personalizing exoskeletons (or other assistive devices) to individual users.