# Exo-Plore: Exploring Exoskeleton Control Space through Human-aligned Simulation

arXiv: https://arxiv.org/abs/2601.22550 · HTML: https://mlsanity.com/paper/2601.22550/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 65 references that are in the map.
- First posted on arXiv 30 Jan 2026.

## Builds on (most cited first, 4 of 4)

- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration](https://mlsanity.com/paper/1809.11165/) (2018)
- [Generative GaitNet](https://mlsanity.com/paper/2201.12044/) (2022)
- [Bidirectional GaitNet: A Bidirectional Prediction Model of Human Gait and Anatomical Conditions](https://mlsanity.com/paper/2306.04161/) (2023)

## Built on by (most cited first, 6 of 6)

- [Learning Hip Exoskeleton Control Policy via Predictive Neuromusculoskeletal Simulation](https://mlsanity.com/paper/2603.04166/) (2026)
- [SMAT: Staged Multi-Agent Training for Co-Adaptive Exoskeleton Control](https://mlsanity.com/paper/2603.07618/) (2026)
- [Embodied Human Simulation for Quantitative Design and Analysis of Interactive Robotics](https://mlsanity.com/paper/2603.09218/) (2026)
- [Staged Multi-Agent Training (SMAT) for Hip Exoskeletons: Metabolic and Biomechanical Validation of a Simulation-Trained Co-Adaptive Controller](https://mlsanity.com/paper/2608.00715/) (2026)
- [Exoskeleton Control through Learning to Reduce Biological Joint Moments in Simulations](https://mlsanity.com/paper/2603.07629/) (2026)
- [UniExo: Unified Multi-Skill Policies for Musculoskeletal Locomotion and Co-Adaptive Exoskeleton Control](https://mlsanity.com/paper/2609.19690/) (2026)

## Abstract (arXiv)

Exoskeletons show great promise for enhancing mobility, but providing appropriate assistance remains challenging due to the complexity of human adaptation to external forces. Current state-of-the-art approaches for optimizing exoskeleton controllers require extensive human experiments in which participants must walk for hours, creating a paradox: those who could benefit most from exoskeleton assistance, such as individuals with mobility impairments, are rarely able to participate in such demanding procedures. We present Exo-plore, a simulation framework that combines neuromechanical simulation with deep reinforcement learning to optimize hip exoskeleton assistance without requiring real human experiments. Exo-plore can (1) generate realistic gait data that captures human adaptation to assistive forces, (2) produce reliable optimization results despite the stochastic nature of human gait, and (3) generalize to pathological gaits, showing strong linear relationships between pathology severity and optimal assistance.