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

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
Deep reinforcement learning; Musculoskeletal simulation; Pathological gait generalization; Sim-to-real matching
Abstract:

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 often unable 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. Please refer to the following link for supplemental materials https://iclr2026anonymous.github.io

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
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Overview

Overall Novelty Assessment

The paper presents Exo-plore, a simulation framework combining neuromechanical modeling with deep reinforcement learning to optimize hip exoskeleton assistance without human trials. It resides in the 'Reinforcement Learning with Neuromechanical Simulation' leaf under 'Simulation-Based Optimization Methods'. This leaf contains only two papers total, including the original work, indicating a relatively sparse research direction within the broader taxonomy of 26 papers across multiple branches. The sibling paper in this leaf represents the most directly comparable prior work in this specific methodological niche.

The taxonomy reveals neighboring approaches in adjacent leaves: 'Inverse Optimal Control Approaches' (2 papers) recover cost functions from human movement, while 'Neuromechanical Parameter Studies' (1 paper) conduct systematic parameter variation analyses. Parallel branches include 'EMG-Driven Musculoskeletal Control' (4 papers) for real-time assistance and 'Task-Specific Neuromechanical Controllers' (3 papers) for rehabilitation contexts. The scope note clarifies this leaf excludes inverse optimal control and forward optimal control methods, focusing specifically on RL-based offline optimization. The broader 'Simulation-Based Optimization Methods' branch (5 papers total) remains less populated than real-time control frameworks (9 papers), suggesting simulation-driven RL for exoskeletons is an emerging rather than saturated area.

Among 23 candidates examined across three contributions, no clearly refutable prior work was identified. The core 'Exo-plore simulation framework' contribution examined 10 candidates with zero refutations, the 'resistance minimization reward' examined 3 candidates with zero refutations, and the 'surrogate network optimizer' examined 10 candidates with zero refutations. This limited search scope (top-K semantic matches plus citations) suggests that within the examined literature, no single prior work directly overlaps all three technical components. The statistics indicate each contribution appears novel relative to the 23-paper candidate set, though this does not constitute exhaustive coverage of all possible related work.

Based on the limited 23-candidate search, the work appears to occupy a sparsely populated methodological niche combining RL, neuromechanical simulation, and pathological gait generalization. The taxonomy structure confirms this is an emerging direction rather than a crowded subfield. However, the analysis covers only top-ranked semantic matches and does not guarantee comprehensive coverage of adjacent methods in biomechanics, robotics, or RL literature that might employ similar techniques under different terminology or application contexts.

Taxonomy

Core-task Taxonomy Papers
26
3
Claimed Contributions
23
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: optimizing exoskeleton control parameters through neuromechanical simulation. The field encompasses diverse approaches ranging from neuromechanical model-based control frameworks that integrate muscle dynamics and neural reflexes, to simulation-based optimization methods leveraging reinforcement learning or evolutionary algorithms, and biomimetic controllers inspired by biological motor control. Design optimization branches focus on mechanical parameter tuning such as cable routing and attachment points, while task-specific controllers address particular activities like walking, lifting, or balance. Review and methodological surveys provide overarching perspectives on lower limb systems and modeling advances. Representative works illustrate this breadth: Ceinms-rt Framework[1] exemplifies real-time neuromechanical modeling, Coupled Exoskeleton Assistance[2] demonstrates model-based control integration, and Soft Back Exosuit[3] highlights design optimization for lifting tasks. Within simulation-based optimization, a particularly active line of work employs reinforcement learning with neuromechanical models to discover control policies that adapt to human physiology. Exo-Plore[0] sits squarely in this branch, using RL to optimize exoskeleton assistance by simulating muscle-tendon dynamics and metabolic cost. This contrasts with neighboring approaches like Human-Robot Movement[21], which also explores learning-based methods but may emphasize different interaction paradigms or task contexts. Trade-offs emerge between computational expense of high-fidelity muscle models versus the generalizability of learned policies, and between optimizing for energetic efficiency versus user comfort or natural gait patterns. Exo-Plore[0] emphasizes the coupling of detailed neuromechanical simulation with policy search, positioning it among works that prioritize physiological realism to inform control parameter selection, rather than purely kinematic or heuristic tuning strategies seen in biomimetic branches.

Claimed Contributions

Exo-plore simulation framework for exoskeleton optimization

The authors introduce Exo-plore, a framework that integrates neuromechanical simulation with deep reinforcement learning to discover optimal hip exoskeleton control parameters. This approach eliminates the need for extensive human experiments, addressing the paradox that mobility-impaired individuals who would benefit most from exoskeletons cannot participate in demanding optimization procedures.

10 retrieved papers
Human-exoskeleton interaction reward based on resistance minimization

The authors develop a novel reward function (r_HEI) grounded in a resistance-minimization hypothesis to model how humans actively adapt their movement patterns to exoskeleton assistance. This reward enables the simulation to reproduce empirical adaptation patterns such as assistive moment and power scaling across different assistance settings.

3 retrieved papers
Surrogate network-based optimizer for stochastic simulation environments

The authors propose using a neural network surrogate trained on Latin hypercube sampled simulation data to create smooth, differentiable cost-of-transport landscapes. This approach enables efficient gradient-based optimization despite the inherent stochasticity in reinforcement learning and simulation, replacing sample-inefficient methods like Bayesian optimization.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Exo-plore simulation framework for exoskeleton optimization

The authors introduce Exo-plore, a framework that integrates neuromechanical simulation with deep reinforcement learning to discover optimal hip exoskeleton control parameters. This approach eliminates the need for extensive human experiments, addressing the paradox that mobility-impaired individuals who would benefit most from exoskeletons cannot participate in demanding optimization procedures.

Contribution

Human-exoskeleton interaction reward based on resistance minimization

The authors develop a novel reward function (r_HEI) grounded in a resistance-minimization hypothesis to model how humans actively adapt their movement patterns to exoskeleton assistance. This reward enables the simulation to reproduce empirical adaptation patterns such as assistive moment and power scaling across different assistance settings.

Contribution

Surrogate network-based optimizer for stochastic simulation environments

The authors propose using a neural network surrogate trained on Latin hypercube sampled simulation data to create smooth, differentiable cost-of-transport landscapes. This approach enables efficient gradient-based optimization despite the inherent stochasticity in reinforcement learning and simulation, replacing sample-inefficient methods like Bayesian optimization.

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