Ankle Exoskeleton Control

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Sep 14, 2026cs.RO

Assistance Torque Estimation via Dynamics-Aware Optimization for Lower-Limb Exoskeleton in Complex Environments

Ground-truth human joint torque estimation relies on motion capture systems, which suffer from limited outdoor usability and significant deployment expenses. Furthermore, direct scaling of ground-truth joint torques to obtain motor torque commands is not necessarily the optimal strategy. To address the aforementioned limitations, inspired by the human motion generation process, this paper proposes a novel assistance torque estimation method based on the dynamic model. From an optimization perspective, the proposed method directly generates motor-assist torque and lowers the cost of data acquisition. Then, a data-driven assistance torque prediction network is trained to enable accurate real-time prediction under complex outdoor environments. Experimental results demonstrate that optimized (estimated) assistance torque exhibits better phase consistency with gait trajectories and better alignment with task characteristics. Relative to the Zero torque condition, the predicted torque can decrease metabolic rate by 11.8%-17.7%, heart rate by 8.9%-14.3%, and peak muscle activation levels by 28.2%-54.0%, respectively. This provides a new perspective for low-cost adaptive exoskeleton assistance.

Xiao-Yin Liu, Guotao Li, Weiqun Wang +1

State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China. · The School of Artificial Intelligence, University of Chinese Academy Sciences, Beijing, China.

Data-Driven ControlAnkle Exoskeleton ControlAssistive Robotics
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Aug 10, 2026cs.RO

Personalized Lower-limb Exoskeleton Assistance via Preference-based Bayesian Optimization

A significant challenge in exoskeleton robotics is the need to dynamically adapt control profiles to individual motion preferences, thereby ensuring both efficient and comfortable assistance. Currently, since user experience can serve as a comprehensive metric for evaluating the effectiveness of assistance, user preference-based optimization methods have been widely studied for parameter tuning. However, the existing methods rely heavily on extensive human-robot online interactions and suffer from slow optimization speed, which not only induces user fatigue but also compromises optimization effectiveness. Therefore, this paper aims to explore an efficient preference-based optimization framework for personalized exoskeleton assistance that can learn optimal parameters with minimal interaction. We propose a preference-based Bayesian optimization (PbBO) approach that can improve sample efficiency by leveraging knowledge about the sampling distribution of candidate sets. For optimizing six control parameters, PbBO can converge to user-preferred parameters with 90.7% validation accuracy via 20 iterations. Moreover, the hierarchical controller is designed to generate personalized torque for different tasks and achieve interaction torque tracking in real time. The results of treadmill and outdoor experiments demonstrate that the optimized parameters can reduce metabolic rate by 14.5%-15.4%, heart rate by 6.3%-7.6%, and muscle activation by 6.7%-31.5% compared to unassisted walking.

Xiao-Yin Liu, Guotao Li, Weiqun Wang +1

State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China · School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China · CASIA-MUST Joint Laboratory of Intelligence Science and Technology, Institute of Systems Engineering, Macau University of Science and Technology, Macao, China

Bayesian OptimizationAnkle Exoskeleton ControlAssistive Robotics
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Aug 6, 2026cs.RO

ATP: Anatomical Torque with Passivity-based Control Framework for Safe Upper-Limb Exoskeleton Assistance

Providing assistance across diverse movements is a central objective of exoskeletons, and anatomical knowledge can enable responsive support that generalizes across tasks. However, anatomical assistance has mainly been studied for lower-limb exoskeletons, where periodic, weight-bearing motions impose lower demands on torque precision. Extending such assistance to complex, nonperiodic upper-limb movements remains challenging. This paper proposes Anatomical Torque with Passivity-Based Control (ATP) for safe upper-limb exoskeleton assistance. First, a scalable musculoskeletal simulation framework trains a unified reinforcement-learning muscle controller that generalizes across upper-limb movements and generates anatomical reference torques without complex biomechanical computations. Second, 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. Third, an interaction torque controller delivers assistance through a cable-driven compliant exoskeleton without constraining motion to predefined trajectories, while an energy tank preserves passivity with theoretical guarantees on torque tracking and system passivity. Simulations and real-world experiments show accurate tracking of long-duration motion sequences and generalization to real-time human movements. The controller achieves accurate torque tracking while preserving passivity and resumes tracking after energy-tank replenishment. An EMG study with five participants further shows reduced target-muscle activity during static and dynamic tasks compared with gravity compensation and open-loop assistance, with reductions of up to 48% relative to movement without the exoskeleton in a dynamic multi-joint task.

Yu Chen, Gong Chen, Xiang Li

Department of Automation, Tsinghua University, China · Shenzhen MileBot Robotics Co., Ltd, China

Ankle Exoskeleton ControlFeedback ControlAssistive RoboticsPassivity-Based Control
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Aug 3, 2026cs.LG

A 2-Block Architecture for Real-Time EEG Gait Decoding: A Pilot Study

Closed-loop lower-limb exoskeleton control via Electroencephalography (EEG) remains limited by motion artifacts, low signal-to-noise ratio, and binary gait formulations that fail to capture full cortical gait complexity. We propose a 2-block Brain-Computer Interface (BCI) architecture: a trainable session-specific Feature Extraction Block with real-time artifact suppression and multi-domain feature extraction, coupled with a Decoder Block built on a novel Polynomial Time-Varying Layer (PolyTVL)+LSTM for four-state gait classification (Stand, Initiate, Execute, Terminate). Ablation confirmed v01 (PolyTVL+LSTM) outperformed all variants (validation MCC: 0.435, gap: 0.187), with consistent EEG feature discriminability across ROIs and sub-bands (p<0.05). Closed-loop deployment with v01 achieved 55.3% (Rex-assisted) and 52.7% (volitional) gait initiation success, with a mean prediction time of 70.5~ms (+/-41.5), validating real-time feasibility in this pilot study.

Shantanu Sarkar, Saurabh Prasad, Jose L. Contreras-Vidal

IUCRC BRAIN Center, Cullen College of Engineering, University of Houston, Houston, TX, USA

Robotic ControlBrain-Computer InterfacesEEG DecodingAnkle Exoskeleton Control
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Jun 20, 2026cs.RO

Deep RL- Tuned Mo del-Free Adaptive Control for Lower-Limb Exoskeletons During Sit-to-Stand Transitions

Sit-to-stand (STS) transitions impose significant joint-loading demands on elderly individuals, making them a primary target for lower-limb exoskeleton assistance. However, accurate trajectory tracking during STS is challenging due to complex, time-varying human exoskeleton interaction dynamics and inter-subject variability that render model-based control approaches difficult to apply in practice. This paper presents an intelligent model free adaptive backstepping control strategy for a bilateral lower-limb exoskeleton during STS motion. The proposed controller design uses an ultra-local second-order model to avoid explicit system identification, while a Gaussian radial basis function (RBF) neural network estimates the unknown lumped dynamics online. To further improve phase-aware tracking performance, a Twin Delayed Deep Deterministic Policy Gradient (TD3) reinforcement learning agent is integrated as a supervisory gain scheduler that adaptively adjusts controller gains across the distinct phases of STS motion. The proposed controller is evaluated through co-simulation in MATLAB/Simulink and Simscape Multibody using OpenSim-derived reference trajectories and benchmarked against state-of-the-art controllers. Results demonstrate that the proposed controller achieves the lowest average RMSE of 0.078 degree across all joints, representing improvements of 60.2%, 54.4%, 48.7%, and 42.6% over proportional integral derivative (PID), model-free adaptive control (MFAC), linear quadratic regulator (LQR), and sliding-mode control (SMC), respectively. TD3 integration further reduces tracking error by 35%, 33%, and 79% at the hip, knee, and ankle joints compared to the standalone RBF-MFAC baseline. These results demonstrate the effectiveness and robustness of the proposed controller design for assistive exoskeleton control during STS transitions.

Ranjeet Kumbhar, Appaso M. Gadade, Rajmeet Singh +2

Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Patiala, India – 147004 · Department of Mechanical Engineering Thapar Institute of Engineering and Technology Patiala, India – 147004 · Mechatronics Engineer, Research Office Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) Abu Dhabi, UAE

RL ControlRobot Trajectory TrackingAnkle Exoskeleton ControlAssistive RoboticsAdaptive Control
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Jun 16, 2026cs.RO

Continual Online Personalization of Exoskeleton Control via Manifold-Aware Experience Replay

Personalizing exoskeleton control remains a critical challenge for clinical users with gait disabilities. Online adaptation (OA) offers an effective solution by adapting in real time to subject variability, device fit, and diverse locomotor tasks. However, OA involves a continual stream of user state data, which can lead to catastrophic forgetting of previously learned locomotor contexts. Here, we develop a manifold-aware experience replay-based online personalization framework designed to maintain user-specific representations across diverse tasks during OA of exoskeleton control. By replaying previously experienced tasks from a replay buffer, we preserve the personalized exoskeleton assistance across all learned tasks. Furthermore, we capture a gait manifold that distinguishes between different locomotor tasks, removing the need for explicit task labeling when selecting target replay bins. We evaluated our framework on emulated hemiplegic gait, which largely deviates from able-bodied patterns, across multiple forgetting scenarios with speed and incline transitions. Our manifold-aware replay framework achieved 40% and 60% improvements in torque and gait phase tracking accuracy, respectively, compared to a baseline framework without replay, which exhibited catastrophic forgetting during task transitions. This demonstrates that our proposed framework personalizes exoskeleton control in real time across diverse locomotor contexts in daily ambulation of clinical populations.

Changseob Song, Inseung Kang

Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA

Robotic ControlAnkle Exoskeleton ControlReplay-Based Continual LearningAssistive Robotics
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Jun 4, 2026cs.RO

Ensuring Interaction Safety in Multitask Exoskeleton Control: A Simulation-Trained Variable Impedance Framework

Wearable exoskeletons can augment human phys ical capabilities during complex activities. However, ensuring adaptation across diverse tasks while guaranteeing interaction safety remains a critical challenge. To address this, a simulation trained variable impedance control approach with stability guarantees is proposed. First, a simulation-based human exoskeleton motion data generation pipeline is established, utilizing Proximal Policy Optimization (PPO) to synthesize human muscle activations while the exoskeleton provides direct compensation for human biological joint torques. Subsequently, the generated dataset is used to train a dual modality policy that fuses semantic instructions with proprioceptive history, enabling the prediction of reference trajectories and variable impedance gains for nine different motion tasks. To guarantee safety, the network outputs are constrained by a stability criterion derived from Lyapunov stability theory, which bounds stiffness variations to ensure the asymptotic stability of the coupled system. Experimental results indicate that the proposed framework reduces metabolic cost in real-world scenarios com pared with standard baseline methods. These findings suggest the feasibility of the proposed framework for safe, multitask exoskeleton control.

Muyuan Ma, Houcheng Li, Haotian Zhai +4

Robotic ControlImpedance ControlAnkle Exoskeleton ControlLanguage-Conditioned Robot Control
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Jun 3, 2026cs.RO

OLIVE: Online Low-Rank Incremental Learning for Efficient Adaptive Exoskeletons

Wearable exoskeleton systems hold promise for restoring mobility in individuals with physical impairments, yet most existing controllers rely on static gait policies that lack the ability to adapt to dynamic real-world environments or individual user characteristics. We present \olive (\underline{O}nline \underline{L}ow-rank \underline{I}ncremental Learning for Efficient Adapti\underline{ve} Exoskeletons), a parameter-efficient online adaptation framework that continuously personalizes exoskeleton control during deployment. \olive decomposes the adaptive component of the control policy into a low-rank residual form~\dW=\At\Bt⊤\dW = \At\Bt^\top with rank~r!≪!min⁡(d,k)r!\ll!\min(d,k), reducing online update cost from O(dk)\mathcal{O}(dk) to O(r(d+k))\mathcal{O}(r(d{+}k)) while preserving the stability of a pretrained base controller~\Wz\Wz. Parameters are updated via a reward-shaped policy gradient driven purely by on-body sensor feedback (EMG, IMU, vibration), eliminating dependence on offline reference trajectories. A gating mechanism modulates the strength of personalization based on contextual state, and a dynamic rank scheduler adapts the update dimensionality to terrain complexity -- allocating minimal capacity on simple flat terrain and expanding to higher-rank updates on demanding uneven surfaces -- enabling robust performance across diverse activities: flat walking, stair navigation, slopes, and uneven terrain. Experiments on the wearable platform demonstrate that \olive achieves +13, +22, and +15 percentage-point improvements in gait smoothness, effort reduction, and motion stability over the strongest baseline, converging within ∼\sim1{,}800 walking steps at 7.4,ms end-to-end latency. Our code implementation is available at https://github.com/FastLM/OLIVE.

Dong Liu, Yanxuan Yu, Ben Lengerich +2

University of California, Los Angeles Los Angeles, California, USA · Columbia University New York, New York, USA · University of Wisconsin-Madison Madison, Wisconsin, USA +1

Robotic ControlLow-Rank AdaptationAnkle Exoskeleton ControlAssistive Robotics
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Aug 28, 2025cs.RO

Uncertainty-Aware Ankle Exoskeleton Control

Lower limb exoskeletons show promise to assist human movement, but their utility is limited by controllers designed for discrete, predefined actions in controlled environments, restricting their real-world applicability. We present an uncertainty-aware control framework that enables ankle exoskeletons to operate safely across diverse scenarios by automatically disengaging when encountering unfamiliar movements. Our approach uses an uncertainty estimator to classify movements as similar (in-distribution) or different (out-of-distribution) relative to actions in the training set. We evaluated three architectures (model ensembles, autoencoders, and generative adversarial networks) on an offline dataset and tested the strongest performing architecture (ensemble of gait phase estimators) online. The online test demonstrated the ability of our uncertainty estimator to turn assistance on and off as the user transitioned between in-distribution and out-of-distribution tasks (F1: 89.2). This new framework provides a path for exoskeletons to safely and autonomously support human movement in unstructured, everyday environments.

Fatima Mumtaza Tourk, Bishoy Galoaa, Sanat Shajan +3

Department of Mechanical Engineering, Northeastern University, Boston, MA 02115 USA

Uncertainty QuantificationAnkle Exoskeleton ControlRobot LocomotionAssistive RoboticsOOD Detection
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