Investigating Adaptive Tuning of Assistive Exoskeletons Using Offline Reinforcement Learning: Challenges and Insights
On the map: Offline RL and imitation learning from data → Real-world robot offline RL
Abstract (arXiv)
Assistive exoskeletons have shown great potential in enhancing mobility for individuals with motor impairments, yet their effectiveness relies on precise parameter tuning for personalized assistance. In this study, we investigate the potential of offline reinforcement learning for optimizing effort thresholds in upper-limb assistive exoskeletons, aiming to reduce reliance on manual calibration. Specifically, we frame the problem as a multi-agent system where separate agents optimize biceps and triceps effort thresholds, enabling a more adaptive and data-driven approach to exoskeleton control. Mixed Q-Functionals (MQF) is employed to efficiently handle continuous action spaces while leveraging pre-collected data, thereby mitigating the risks associated with real-time exploration. Experiments were conducted using the MyoPro 2 exoskeleton across two distinct tasks involving horizontal and vertical arm movements. Our results indicate that the proposed approach can dynamically adjust threshold values based on learned patterns, potentially improving user interaction and control, though performance evaluation remains challenging due to dataset limitations.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Real-world robot offline RL, one of 13 directions in Offline RL and imitation learning from data. Builds on 9 of its 21 references that are in the map. First posted on arXiv 30 Apr 2025.
Builds on
9 of 21 references.
References · 9
- 2015Continuous control with deep reinforcement learningcited by 3,279
- 2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problemscited by 1,873
- 2018QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learningcited by 697
- 2017Value-Decomposition Networks For Cooperative Multi-Agent Learningcited by 490
- 2019QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learningcited by 372
- 2020Weighted QMIX: Expanding Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learningcited by 197
- 2024Mixed Q-Functionals: Advancing Value-Based Methods in Cooperative MARL with Continuous Action Domainscited by 1
- 2024Investigating the Generalizability of Assistive Robots Models over Various Taskscited by 1
- 2024Design of Fuzzy Logic Parameter Tuners for Upper-Limb Assistive Robotscited by 1
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- 2025Imitation Learning for Adaptive Control of a Virtual Soft Exoglove
- 2023Towards AI-controlled FES-restoration of arm movements: Controlling for progressive muscular fatigue with Gaussian state-space models
- 2026ExoTraj: A General Lower-limb Exoskeleton Assistance Policy for Complex Environments ↗
- 2025Motion Adaptation Across Users and Tasks for Exoskeletons via Meta-Learning ↗
- 2022Latent-Variable Advantage-Weighted Policy Optimization for Offline RL
- 2025Learning Human Reaching Optimality Principles from Minimal Observation Inverse Reinforcement Learning
- 2025Diffusion Policies with Offline and Inverse Reinforcement Learning for Promoting Physical Activity in Older Adults Using Wearable Sensors
- 2020Human Preference-Based Learning for High-dimensional Optimization of Exoskeleton Walking Gaits