Exoskeleton Control through Learning to Reduce Biological Joint Moments in Simulations

• URL: http://arxiv.org/abs/2603.07629v1
• Date: Sun, 08 Mar 2026 13:31:06 GMT
• Title: Exoskeleton Control through Learning to Reduce Biological Joint Moments in Simulations
• Abstract summary: Data-driven joint-moment predictors offer a scalable alternative to laboratory-based inverse-dynamics pipelines for estimation and exoskeleton control.<n> physics-based reinforcement learning enables simulation-trained controllers to learn dynamics-aware assistance strategies.<n>This paper presents (1) an RL framework to learn exoskeleton assistance policies that reduce biological joint moments, and (2) a validation pipeline that verifies the trained control networks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Data-driven joint-moment predictors offer a scalable alternative to laboratory-based inverse-dynamics pipelines for biomechanics estimation and exoskeleton control. Meanwhile, physics-based reinforcement learning (RL) enables simulation-trained controllers to learn dynamics-aware assistance strategies without extensive human experimentation. However, quantitative verification of simulation-trained exoskeleton torque predictors, and their impact on human joint power injection, remains limited. This paper presents (1) an RL framework to learn exoskeleton assistance policies that reduce biological joint moments, and (2) a validation pipeline that verifies the trained control networks using an open-source gait dataset through inference and comparison with biological joint moments. Simulation-trained multilayer perceptron (MLP) controllers are developed for level-ground and ramp walking, mapping short-horizon histories of bilateral hip and knee kinematics to normalized assistance torques. Results show that predicted assistance preserves task-intensity trends across speeds and inclines. Agreement is particularly strong at the hip, with cross-correlation coefficients reaching 0.94 at 1.8 m/s and 0.98 during 5° decline walking, demonstrating near-matched temporal structure. Discrepancies increase at higher speeds and steeper inclines, especially at the knee, and are more pronounced in joint power comparisons. Delay tuning biases assistance toward greater positive power injection; modest timing shifts increase positive power and improve agreement in specific gait intervals. Together, these results establish a quantitative validation framework for simulation-trained exoskeleton controllers, demonstrate strong sim-to-data consistency at the torque level, and highlight both the promise and the remaining challenges for sim-to-real transfer.

Related papers

• Intern-S2-Preview: Scientific Agentic Foundation Model [199.88087357217503]
  Intern-S2-Preview is designed to support multimodal scientific understanding, reasoning, generation, and long-horizon tasks.<n>The training pipeline begins with scientific multimodal pre-training over rendered scientific documents, interleaved image-text data, and diverse scientific corpora.<n>This pipeline is supported by practical techniques that improve rollout and training stability and efficiency.
  arXiv  Detail & Related papers  (2026-08-13T17:31:28Z)
• ATP: Anatomical Torque with Passivity-based Control Framework for Safe Upper-Limb Exoskeleton Assistance [8.72367908819341]
  This paper proposes Anatomical Torque with Passivity-Based Control for safe upper-limb exoskeleton assistance.<n>An online torque-refinement scheme adapts the reference to diverse movements, suppresses tendon-induced spikes, and incorporates a learned anomaly score for safe and comfortable assistance.<n> Simulations and real-world experiments show accurate tracking of long-duration motion sequences and generalization to real-time human movements.
  arXiv  Detail & Related papers  (2026-08-06T08:06:40Z)
• FUSE: FK-Steered Multi-Modal Flow Matching for Efficient Simulation-Based Posterior Estimation [47.3675633392999]
  We introduce FUSE (Feynman-Kac steered mUlti-modal flow matching for efficient Simulation-based posterior Estimation)<n>Unlike prior work, FUSE employs a dual-track architecture that preserves the distinct features of multimodal inputs while facilitating dynamic interaction.<n>Our approach outperforms state-of-the-art baselines on standard SBI benchmarks, producing posteriors that closely match ground-truth MCMC.
  arXiv  Detail & Related papers  (2026-07-06T15:59:18Z)
• SCRIPT: Scalable Diffusion Policy with Multi-stage Training for Language-driven Physics-based Humanoid Control [72.5651722107621]
  SCRIPT is a scalable diffusion policy with a multi-stage training framework for language-driven physics-based humanoid control.<n>The core of SCRIPT is a Joint Action-State-Text Diffusion Transformer (JAST-DiT), which represents actions, physical states, and text as dedicated token streams.<n>To stabilize autore control, we introduce a nonlinear history conditioning mechanism, which preserves the dense recent context and samples increasingly sparse cues from long-term history.
  arXiv  Detail & Related papers  (2026-05-21T14:17:21Z)
• Muscle Synergy Priors Enhance Biomechanical Fidelity in Predictive Musculoskeletal Locomotion Simulation [0.0]
  Human locomotion emerges from high-dimensional neuromuscular control, making predictive musculoskeletal simulation challenging.<n>We present a physiology-informed reinforcement-learning framework that constrains control using muscle synergies.<n>These results show that embedding neurophysiological structure into reinforcement learning can improve biomechanical fidelity and generalization in predictive human locomotion simulation with limited experimental data.
  arXiv  Detail & Related papers  (2026-03-11T06:48:15Z)
• SMAT: Staged Multi-Agent Training for Co-Adaptive Exoskeleton Control [1.7061322026474812]
  We propose a four-stage curriculum designed to mirror how users naturally acclimate to a wearable device.<n>We implement SMAT in the MyoAssist simulation environment using a 26-muscle lower-limb model and an attached hip exoskeleton.<n>Our musculoskeletal simulations demonstrate that the learned exoskeleton control policy produces an average 10.1% reduction in hip muscle activation relative to the no-assist condition.
  arXiv  Detail & Related papers  (2026-03-08T13:06:36Z)
• Learning Hip Exoskeleton Control Policy via Predictive Neuromusculoskeletal Simulation [0.0]
  We present a physics-based neuromusculoskeletal learning framework that trains a hip-exoskeleton control policy entirely in simulation.<n>A reinforcement learning teacher policy is trained using a muscle-synergy action prior over a wide range of walking speeds and slopes.<n>In simulation, exoskeleton assistance reduces mean muscle activation by up to 3.4% and mean positive joint power by up to 7.0% on level ground and ramp ascent.
  arXiv  Detail & Related papers  (2026-03-04T15:23:50Z)
• Multi-Joint Physics-Informed Deep Learning Framework for Time-Efficient Inverse Dynamics [14.935995015069487]
  Time-efficient estimation of muscle activations and forces across multi-joint systems is critical for clinical assessment and assistive device control.<n>We propose a physics-informed deep learning framework that estimates muscle activations and forces directly from kinematics.<n>We show that PI-MJCA-BiGRU achieves performance comparable to conventional supervised methods without requiring ground-truth labels.
  arXiv  Detail & Related papers  (2025-11-14T01:15:10Z)
• Drift No More? Context Equilibria in Multi-Turn LLM Interactions [58.69551510148673]
  contexts drift is the gradual divergence of a model's outputs from goal-consistent behavior across turns.<n>Unlike single-turn errors, drift unfolds temporally and is poorly captured by static evaluation metrics.<n>We show that multi-turn drift can be understood as a controllable equilibrium phenomenon rather than as inevitable decay.
  arXiv  Detail & Related papers  (2025-10-09T04:48:49Z)
• Predictability Shapes Adaptation: An Evolutionary Perspective on Modes of Learning in Transformers [51.992454203752686]
  Transformer models learn in two distinct modes: in-weights learning (IWL) and in-context learning (ICL)<n>We draw inspiration from evolutionary biology's analogous adaptive strategies: genetic encoding and phenotypic plasticity.<n>We experimentally operationalize these dimensions of predictability and investigate their influence on the ICL/IWL balance in Transformers.
  arXiv  Detail & Related papers  (2025-05-14T23:31:17Z)
• Adaptive Torque Control of Exoskeletons under Spasticity Conditions via Reinforcement Learning [0.0]
  We describe a novel adaptive torque controller via deep reinforcement learning (RL) for a knee exoskeleton under joint spasticity conditions.<n>Results for a simulated knee extension movement showed that the agent learns to control the exoskeleton for individuals with different levels of spasticity.
  arXiv  Detail & Related papers  (2025-03-14T14:22:09Z)
• sEMG-Driven Physics-Informed Gated Recurrent Networks for Modeling Upper Limb Multi-Joint Movement Dynamics [5.524068837259551]
  Exoskeletons and rehabilitation systems have the potential to improve human strength and recovery by using adaptive human-machine interfaces.<n>We introduce the Physics-informed Gated Recurrent Network (PiGRN), a novel model designed to predict multi-joint movement dynamics from sEMG data.<n>PiGRN uses a Gated Recurrent Unit (GRU) to process time-series sEMG inputs, estimate multi-joint kinematics and external loads, and predict joint torque while incorporating physics-based constraints during training.
  arXiv  Detail & Related papers  (2024-08-29T15:09:04Z)
• A Multi-Grained Symmetric Differential Equation Model for Learning Protein-Ligand Binding Dynamics [73.35846234413611]
  In drug discovery, molecular dynamics (MD) simulation provides a powerful tool for predicting binding affinities, estimating transport properties, and exploring pocket sites. We propose NeuralMD, the first machine learning (ML) surrogate that can facilitate numerical MD and provide accurate simulations in protein-ligand binding dynamics. We demonstrate the efficiency and effectiveness of NeuralMD, achieving over 1K$times$ speedup compared to standard numerical MD simulations.
  arXiv  Detail & Related papers  (2024-01-26T09:35:17Z)
• AdaSAM: Boosting Sharpness-Aware Minimization with Adaptive Learning Rate and Momentum for Training Deep Neural Networks [76.90477930208982]
  Sharpness aware (SAM) has been extensively explored as it can generalize better for training deep neural networks. Integrating SAM with adaptive learning perturbation and momentum acceleration, dubbed AdaSAM, has already been explored. We conduct several experiments on several NLP tasks, which show that AdaSAM could achieve superior performance compared with SGD, AMS, and SAMsGrad.
  arXiv  Detail & Related papers  (2023-03-01T15:12:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.