· i-manager's Journal on Augmented & Virtual Reality· 0 citations· 4 references
Abstract
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.
Collaborative robotic manipulation has become a critical technology in modern industrial automation, healthcare, logistics, precision manufacturing, and service robotics by enabling safe human–robot collaboration within shared workspaces. Unlike conventional industrial robots operating with fixed, pre-programmed motions, collaborative robots (cobots) require adaptive force control to ensure stable interaction, precise manipulation, and human safety under dynamic and uncertain environments. This paper proposes an Adaptive Force Control Strategy for Collaborative Robotic Manipulation (AFCS-CRM) that integrates multi-modal sensing, sensor fusion, intelligent feature engineering, adaptive impedance control, machine learning-based force prediction, and reinforcement learning into a unified control framework. The system continuously acquires force, torque, tactile, vision, position, and velocity data, applies advanced preprocessing and feature extraction, predicts optimal interaction forces, and adaptively updates control parameters in real time. By combining predictive learning with impedance-based force control and continuous feedback optimization, AFCS-CRM maintains stable contact forces despite uncertainties, varying loads, and object deformations. Compared with conventional PID and fixed impedance controllers, the proposed framework significantly improves force tracking accuracy, manipulation stability, grasp reliability, response time, energy efficiency, and human safety. The scalable and intelligent architecture demonstrates strong potential for next-generation smart manufacturing, robotic assembly, precision surgery, warehouse automation, and assistive robotics, providing a robust foundation for safe, adaptive, and autonomous human–robot collaboration.
Pooja Agarwal, Rakesh Chandra· International Journal of Int...· 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
Wearable robots, particularly lower-limb exoskeletons, have gained attention for their use in rehabilitation and mobility assistance. A key challenge in their design is achieving natural, low-stress human-robot interaction with minimal discomfort. Zero-force control has emerged as an effective strategy for reducing contact forces and enhancing comfort. In this study a deep reinforcement learning (DRL)-based control method for a two-degree-of-freedom lower-limb wearable robot was developed to track natural walking motions while minimizing interaction forces and adapting to varying user characteristics. A simulation environment was created using MuJoCo, and the DRL agent was trained using the TD3 algorithm. Three control approaches were tested: direct velocity setpoint control, adaptive proportional-derivative controller tuning, and adaptive lead compensator tuning. Experimental results showed that the adaptive methods reduced average interaction forces from 3.1 N to 1.7 N (thigh) and from 7.2 N to 2.7 N (shank). The maximum forces during the walking experiments were also lower for the adaptive methods, with the third method achieving a maximum of 15 N. These 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
This paper proposes a safe and effective human–robot physical interaction control framework for exoskeleton robots that enhances system compliance and safety while enabling the robot to adapt to human motion. The framework is designed around two primary objectives: first, a model-free adaptive control method is employed for reference trajectory estimation to achieve real-time estimation of human motion intention; second, the Forgetting Factor Recursive Least Squares (FFRLS) method is utilized for online estimation and the learning of human impedance parameters, considering their time-varying nature. In addition, a model-free adaptive trajectory tracking control strategy is proposed to optimize control performance during human–robot physical interaction. Simulation results demonstrate that the proposed control framework outperforms conventional methods significantly in terms of safety and compliance.
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.
Robot-assisted physiotherapy has attracted increasing attention for its potential to provide repeatable, stable, and controllable physical interaction during rehabilitation-oriented therapy. However, contact-rich physiotherapy tasks remain challenging because the robot must reproduce therapist-demonstrated massage skills while adapting to non-planar and deformable body surfaces, suppressing impact during contact transition, and maintaining stable force regulation. This paper proposes a contact-aware robot-assisted physiotherapy framework that integrates task-space skill generalization, contact state estimation, dynamic velocity adjustment, force-error compensation, and bounded variable impedance control. Therapist-guided demonstrations are encoded using Dynamic Movement Primitives (DMPs) to construct a physiotherapy skill library, enabling typical massage skills, including kneading, patting, pushing, and pressing, to be generalized to new start and goal points in the robot Cartesian task space. During execution, force/torque feedback is used to estimate the contact point and surface normal, update the local task frame, regulate the approach velocity, and compensate for force-tracking errors. Experimental validation was conducted on a silicone abdominal model and in a preliminary healthy-volunteer back physiotherapy test. The results show that the proposed dynamic contact strategy suppresses excessive impact during contact transition, avoiding the 165.86 N peak impact observed under high-speed contact. The force compensation strategy reduces the force-tracking RMSE from 0.835 N to 0.395 N , corresponding to a 52.69% improvement. In addition, the bounded variable impedance strategy improves motion-force coordination compared with fixed-stiffness control. These 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