ArticleFrontiers in robotics and AI2021
Reinforcement Learning and Control of a Lower Extremity Exoskeleton for Squat Assistance.
Article in Frontiers in robotics and AI, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Myoelectric Control Systems for Upper Limb Wearable Robotic Exoskeletons and Exosuits-A Systematic Review.Sensors (Basel, Switzerland) · 2022Pooled it
- Adaptive Control of Lower-Limb Assistive Exoskeleton for Rehabilitation Using Deep Reinforcement Learning.Sensors (Basel, Switzerland) · 2026Article
- Optimizing Sensor and Data Selection on Lower Limbs via Deep Learning for Real-Time Human Activity Recognition.IEEE transactions on human-machine systems · 2026Article
- Robust neural network controller for a lower limb exoskeleton with minimal sensor configuration: a deep reinforcement learning approach with policy distillation.Journal of neuroengineering and rehabilitation · 2025Article
- A Survey on Design and Control Methodologies of High- Torque-Density Joints for Compliant Lower-Limb Exoskeleton.Sensors (Basel, Switzerland) · 2025Review
- Electromyography Signal Acquisition, Filtering, and Data Analysis for Exoskeleton Development.Sensors (Basel, Switzerland) · 2025Review
- Comprehensive human locomotion and electromyography dataset: Gait120.Scientific data · 2025Article
- Controlling Negative and Positive Power for Efficiency Enhancement and Muscle Strain Mitigation During Squatting with a Portable Knee Exoskeleton.Annals of biomedical engineering · 2025Article
- Squat Motion of a Humanoid Robot Using Three-Particle Model Predictive Control and Whole-Body Control.Sensors (Basel, Switzerland) · 2025Article
- Learning to suppress tremors: a deep reinforcement learning-enabled soft exoskeleton for Parkinson's patients.Frontiers in robotics and AI · 2025Article
- Characterization of Human Balance through a Reinforcement Learning-based Muscle Controller.PloS one · 2025Article
- Comparison of Empirical and Reinforcement Learning (RL)-Based Control Based on Proximal Policy Optimization (PPO) for Walking Assistance: Does AI Always Win?Biomimetics (Basel, Switzerland) · 2024Article
- Human-Exoskeleton Coupling Simulation for Lifting Tasks with Shoulder, Spine, and Knee-Joint Powered Exoskeletons.Biomimetics (Basel, Switzerland) · 2024Article
- Article
- Optimizing lower limb rehabilitation: the intersection of machine learning and rehabilitative robotics.Frontiers in rehabilitation sciences · 2024Review
- AI-based methodologies for exoskeleton-assisted rehabilitation of the lower limb: a review.Frontiers in robotics and AI · 2024Review
- Gait Analysis in Neurorehabilitation: From Research to Clinical Practice.Bioengineering (Basel, Switzerland) · 2023Review
- Robust walking control of a lower limb rehabilitation exoskeleton coupled with a musculoskeletal model via deep reinforcement learning.Journal of neuroengineering and rehabilitation · 2023Article
- A Human Lower Limb Mechanical Phantom for the Testing of Knee Exoskeletons.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2023Article
- Predicting the metabolic cost of exoskeleton-assisted squatting using foot pressure features and machine learning.Frontiers in robotics and AI · 2023Article
Corrections and comments
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Authors and funding
6 authors.
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Abstract
A significant challenge for the control of a robotic lower extremity rehabilitation exoskeleton is to ensure stability and robustness during programmed tasks or motions, which is crucial for the safety of the mobility-impaired user. Due to various levels of the user's disability, the human-exoskeleton interaction forces and external perturbations are unpredictable and could vary substantially and cause conventional motion controllers to behave unreliably or the robot to fall down. In this work, we propose a new, reinforcement learning-based, motion controller for a lower extremity rehabilitation exoskeleton, aiming to perform collaborative squatting exercises with efficiency, stability, and strong robustness. Unlike most existing rehabilitation exoskeletons, our exoskeleton has ankle actuation on both sagittal and front planes and is equipped with multiple foot force sensors to estimate center of pressure (CoP), an important indicator of system balance. This proposed motion controller takes advantage of the CoP information by incorporating it in the state input of the control policy network and adding it to the reward during the learning to maintain a well balanced system state during motions. In addition, we use dynamics randomization and adversary force perturbations including large human interaction forces during the training to further improve control robustness. To evaluate the effectiveness of the learning controller, we conduct numerical experiments with different settings to demonstrate its remarkable ability on controlling the exoskeleton to repetitively perform well balanced and robust squatting motions under strong perturbations and realistic human interaction forces.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.