Fuzzy logic-driven reinforcement learning and force field-based tunnel effect for assist-as-needed impedance modulation in upper-limb human-robot interaction
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.
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.
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
The results demonstrate that the proposed framework improves contact transition safety, force regulation accuracy, and task adaptability at the control-performance level, providing a feasible basis for further development of robotic physiotherapy systems.
Xueqian Zhai, Qihao Feng, Haochen Zheng et al.· Frontiers in Robotics and AI· 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
Safe and intuitive human robot interaction (HRI) requires precise regulation of contact forces and torques while adapting to dynamic and uncertain human behavior. Traditional impedance and admittance control strategies rely on fixed parameters and accurate system modeling, which often limit their performance in unstructured or collaborative environments. This paper presents an AI-enabled force and torque control framework that integrates machine learning techniques with conventional control methods to enhance adaptability, compliance, and safety in physical human robot interaction. The proposed approach employs deep neural networks and reinforcement learning to learn human intent and interaction dynamics directly from multi-modal sensor data, including force torque sensors, joint encoders, and inertial measurements. By continuously adjusting control gains in real time, the system achieves stable interaction while minimizing excessive contact forces and undesired torques. Experimental evaluations conducted on a collaborative robotic platform demonstrate significant improvements over classical control schemes, including reduced interaction force peaks, smoother torque profiles, and improved task execution efficiency during cooperative manipulation tasks. The results indicate that AI-driven force and torque control can substantially improve robustness, adaptability, and user comfort in human robot collaboration, making it a promising solution for applications in rehabilitation robotics, assistive devices, and industrial cobots.
Vishal Khanna· i-manager's Journal on Augme...· 0 citations
This paper presents a sensorless human-intention-based control framework for a differential-drive power-assist mobile robot for indoor cooperative transportation. The proposed method estimates interaction-consistent motion cues in the motion domain using wheel-encoder measurements and motor-side actuation information, without relying on dedicated force or torque sensors. An interaction observer is first used to extract an acceleration-like interaction cue from the discrepancy between the commanded robot motion and the measured robot response. This signal is then processed by a human-intent analysis module, in which encoder-derived motion features and statistical class modeling are used to distinguish representative human interaction patterns from disturbance-related motion variations. The resulting interaction-related signal is separated into estimated human-induced and disturbance-induced acceleration components. The human-induced component is converted into a compliant velocity command through a virtual impedance model, whereas the disturbance-induced component is used to construct a disturbance-compensation term in the power-assist controller. Experimental results obtained in indoor cooperative transport scenarios suggest that the proposed framework provides effective assistive control behavior while reducing sensitivity to environmental disturbances.
S. Kim, Bongsu Hahn· Robotica (Cambridge. Print)· 0 citations
