# Investigating Adaptive Tuning of Assistive Exoskeletons Using Offline Reinforcement Learning: Challenges and Insights

arXiv: https://arxiv.org/abs/2505.00201 · HTML: https://mlsanity.com/paper/2505.00201/

## Key facts

- No citing papers in the map yet (data through 14 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.

## Placed in

- Offline RL and imitation learning from data / [Real-world robot offline RL](https://mlsanity.com/offlinerl/real-world-robot-offline-rl/)

## Builds on (most cited first, 9 of 9)

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems](https://mlsanity.com/paper/2005.01643/) (2020)
- [QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1803.11485/) (2018)
- [Value-Decomposition Networks For Cooperative Multi-Agent Learning](https://mlsanity.com/paper/1706.05296/) (2017)
- [QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1905.05408/) (2019)
- [Weighted QMIX: Expanding Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2006.10800/) (2020)
- [Mixed Q-Functionals: Advancing Value-Based Methods in Cooperative MARL with Continuous Action Domains](https://mlsanity.com/paper/2402.07752/) (2024)
- [Investigating the Generalizability of Assistive Robots Models over Various Tasks](https://mlsanity.com/paper/2405.02492/) (2024)
- [Design of Fuzzy Logic Parameter Tuners for Upper-Limb Assistive Robots](https://mlsanity.com/paper/2405.02495/) (2024)

## 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.