The findings indicate that integrating reinforcement learning with Sigmoid parameter adaptation provides a systematic and effective solution for adaptive compliance regulation in mobile exoskeleton systems, enhancing adaptability, safety, and functional relevance for stroke patients undergoing lower limb rehabilitation training.
Abstract
This paper proposes a hierarchical control architecture that is established at both lower limb joint and platform levels based on a simplified admittance model, where compliance is regulated through virtual mass and damping. At the lower limb joint level, admittance control governs lower limb motion tracking, while at the platform level it adjusts the omnidirectional mobile platform velocity in response to human interaction forces. Within this framework, a Sarsa-based reinforcement learning agent dynamically optimizes the parameters of a Sigmoid function using dual state inputs. Based on hip joint angle error and human–robot interaction force, the controller dynamically adjusts virtual mass and damping to optimize the trade-off between tracking error and dynamic compliance. The simulation and experimental results on the prototype system demonstrate that, compared with traditional Sigmoid parameter-tuned admittance control, the proposed approach significantly enhances gait smoothness (dimensionless squared jerk reduced by 65.62% and 36.74% for hip and knee joints), and increases human–robot interaction compliance (RMS interaction force was reduced from 3.2502 N to 2.5109 N; EPUD decreased from 12.14 to 9.53). Moreover, the proposed strategy achieves smaller maximum overshoot (0.45° vs. 0.9°) and faster settling time (2.6 s vs. 4.59 s). These findings indicate that integrating reinforcement learning with Sigmoid parameter adaptation provides a systematic and effective solution for adaptive compliance regulation in mobile exoskeleton systems, enhancing adaptability, safety, and functional relevance for stroke patients undergoing lower limb rehabilitation training.
Results demonstrate that DRL-based methods, particularly when combined with traditional controllers, improve both force reduction and motion stability over conventional control strategies.
Mohammad Sahandi, G. Vossoughi, H. Zohoor et al.· IEEE Access· 0 citations
Lower limb exoskeletons and mobile robots hold great potential in improving motor function rehabilitation for patients with limb dysfunction. However, their widespread application is limited by the substantial time and effort investment required from professional rehabilitation therapists. A significant challenge in achieving autonomous and intelligent rehabilitation lies in addressing the coordinated control between the exoskeleton and the robotic walker. This paper proposes a novel collaborative learning control strategy based on deterministic learning, which aims to achieve high-performance coordinated control through precise closed-loop dynamics modeling of the exoskeleton-walker system. First, radial basis function neural networks (RBFNNs) are employed to approximate the system dynamics during the coordinated control process. Utilizing deterministic learning theory, it is rigorously demonstrated that, under persistent excitation conditions, the unknown dynamics of the system can be accurately approximated and stored as constant neural networks. Subsequently, an experience-based collaborative learning controller is designed, enabling autonomous coordinated control of the human-robot system and offering a viable approach for its broader application. The effectiveness and superior performance of the proposed control strategy are validated through experiments conducted on the CoppeliaSim robotic platform. Note to Practitioners—This work is intended for researchers and engineers working on rehabilitation robotics, particularly those focusing on lower limb exoskeletons and mobile robotic walkers. One of the main barriers to the practical deployment of these systems is the reliance on continuous assistance from rehabilitation professionals during use. To address this, we propose a deterministic learning-based collaborative control strategy that enables accurate modeling and reuse of system dynamics, ultimately achieving autonomous coordination between the exoskeleton and robotic walker. This reduces reliance on manual intervention and opens up the possibility for long-term, high-performance rehabilitation training in clinical and home environments. Practitioners can leverage this approach to develop smarter, more adaptive rehabilitation systems that respond to patient needs with greater precision and autonomy.
Weitian He, Xinhao Zhang, Qinchen Yang et al.· IEEE Transactions on Automat...· 0 citations
This paper proposes a reinforcement learning-based Impact Forces-Optimized Gait Control framework for achieving smooth and quiet humanoid locomotion. The proposed method integrates a quintic polynomial swing-foot trajectory designed to ensure zero vertical velocity and acceleration at foot-ground contact with a joint-jerk minimization reward. Impact forces are provided as privileged observations during training to improve robustness under an asymmetric information architecture. The effectiveness of the proposed method was verified through evaluations conducted in Isaac Lab and MuJoCo (Multi-Joint dynamics with Contact), demonstrating reduced impact forces across various locomotion modes. Additionally, velocity tracking evaluations confirm that the proposed method does not significantly degrade locomotion performance. Furthermore, its performance was validated on the G1 humanoid robot, where real-world walking tests confirmed a reduction in walking-induced acoustic noise.
Hyeonseok Jeong, Yunsoo Kim, Changeui Shin et al.· 2026 23rd International Conf...· 0 citations
A novel assist-as-needed control framework that integrates reinforcement learning with a fuzzy supervisor and a force field-based tunnel for upper-limb three-dimensional reaching tasks and demonstrates the feasibility of the proposed adaptive impedance modulation and supervisory assistance architecture in healthy-subject human-robot interaction.
Knee rehabilitation robots can effectively assist the recovery of patients with lower limb motor dysfunction. This paper presents a lightweight single-drive adaptive knee rehabilitation robot for supine training. The robot consists of a waist support, a thigh mechanism, and a shank-foot mechanism, and provides continuous adjustment of thigh length, shank length, and foot support angle to improve geometric matching for users with different body sizes and postures. To reduce the influence of the device’s self-weight on training, a gravity compensation method based on kinematic coupling is developed, and a sensorless interaction torque estimation method based on motor current feedback is introduced. Using only a single encoder and motor current feedback, the system achieves passive, active-assistive, and resistive behaviors within a unified control framework, without explicit mode switching. Experimental results show that, in active-assist mode, the initial response delay is about 240.0 ms, the trajectory correlation coefficient reaches 0.9960, and the maximum position error is 13.48 mm. In admittance-based interaction control mode, the estimated interaction torque agrees well with the measured torque, with a Pearson correlation coefficient of 0.954, and the average volunteer rating under six impedance levels remains above 3.7/5. These results demonstrate that the proposed robot achieves good adaptability, stable tracking, and reliable human-robot interaction, providing a practical solution for low-cost and integrated knee rehabilitation.
Mei Feng, Xiang Chen, Chao Han et al.· 2026 IEEE International Conf...· 0 citations
Physical human-robot interaction (pHRI) offers considerable potential for improving task efficiency and alleviating operator workload. Nevertheless, the intrinsic variability of human motion intention (HMI) and robot model uncertainties pose substantial challenges to achieving accurate coordinated control. To address these issues, this paper proposes a guaranteed-performance neural adaptive admittance control framework. First, the damping coefficient is dynamically tuned using real-time interaction force feedback, while a neural network (NN) is employed to estimate HMI-induced uncertainties in the coupled human-robot system. These two components are then integrated into the admittance model to construct a high-level interaction strategy that generates compliant reference trajectories for smooth and stable collaboration. Subsequently, low-level motion control with error transformation is developed to enforce prescribed output constraints, thereby ensuring unified regulation of transient and steady-state performance. Moreover, another NN is introduced to approximate the lumped robot dynamics for improved tracking accuracy. Finally, the effectiveness and superiority of the proposed method are validated through trajectory tracking, circle drawing, and obstacle avoidance tasks. Note to Practitioners—This paper focuses on developing an active interaction control approach that enables high-performance tracking for robots subject to model uncertainties while providing high-quality assistance to operators with unknown motion intention. The proposed framework is well-suited to industrial applications such as human-robot cooperative assembly and co-transportation. By incorporating output-constraint-based neural adaptive admittance control, safe, reliable, and compliant physical interaction can be achieved. Consequently, the controller supports further extension to medical rehabilitation and exoskeleton systems, demonstrating broad promise across a wide range of interaction-intensive scenarios.
Chengguo Liu, Hefu Ye, Kai Zhao· IEEE Transactions on Automat...· 0 citations
