Physical Human-robot Interaction Research Articles

Adaptive technique for physical human–robot interaction handling using proprioceptive sensors

The work focuses on the development of an adaptive technique for the physical interaction handling between a human and a robot, as well as its experimental validation. The proposed technique is based on the deep residual neural network and dedicated finite state machine, where the states are the robot behavior modes and transitions are the switchings between the states that depend on the interaction parameters and characteristics. It ensures the human operator safety and improves the human–robot collaboration performance by implementing various scenarios. In the scope of this technique, the parameters of human–robot interaction are used to select an appropriate robot reaction strategy using data from internal robot sensors only, i.e. proprioceptive sensors. These parameters define the interaction force vector and its application point on the robot surface, which allow to classify the interaction within the set of predefined categories. This classification distinguishes interactions applied at the tool or intermediate link (Tool/Link), having soft or hard nature (Soft/Hard), as well as having different intention (Intl/Accd) or duration (Short/Long). Based on identified category and the current robot state, the algorithm chooses an appropriate robot reaction. To confirm the efficiency the developed technique, an experimental study was conducted, which involved the collaboration between the real industrial manipulator KUKA LBR iiwa and the human operator.

Human Modeling in Physical Human-Robot Interaction: A Brief Survey

Human Modeling in Physical Human-Robot Interaction: A Brief Survey

Applied Exoskeleton Technology: A Comprehensive Review of Physical and Cognitive Human–Robot Interaction

Exoskeletons and orthoses are wearable mobile systems providing mechanical benefits to users. Despite significant improvements in the last decades, the technology is not fully mature to be adopted for strenuous and non-programmed tasks. To accommodate this insufficiency, different aspects of this technology need to be analysed and improved. Numerous studies have tried to address some aspects of exoskeletons, e.g. mechanism design, intent prediction, and control scheme. However, most works have focused on a specific element of design or application without providing a comprehensive review framework. This study aims to analyse and survey the contributing aspects to this technology’s improvement and broad adoption. To address this, after introducing assistive devices and exoskeletons, the main design criteria will be investigated from both physical Human-Robot Interaction (HRI) perspectives. In order to establish an intelligent HRI strategy and enable intuitive control for users, cognitive HRI will be investigated after a brief introduction to various approaches to their control strategies. The study will be further developed by outlining several examples of known assistive devices in different categories. And some guidelines for exoskeleton selection and possible mitigation of current limitations will be discussed.

Human Factors Considerations for Quantifiable Human States in Physical Human-Robot Interaction: A Literature Review.

As the global population rapidly ages with longer life expectancy and declining birth rates, the need for healthcare services and caregivers for older adults is increasing. Current research envisions addressing this shortage by introducing domestic service robots to assist with daily activities. The successful integration of robots as domestic service providers in our lives requires them to possess efficient manipulation capabilities, provide effective physical assistance, and have adaptive control frameworks that enable them to develop social understanding during human-robot interaction. In this context, human factors, especially quantifiable ones, represent a necessary component. The objective of this paper is to conduct an unbiased review encompassing the studies on human factors studied in research involving physical interactions and strong manipulation capabilities. We identified the prevalent human factors in physical human-robot interaction (pHRI), noted the factors typically addressed together, and determined the frequently utilized assessment approaches. Additionally, we gathered and categorized proposed quantification approaches based on the measurable data for each human factor. We also formed a map of the common contexts and applications addressed in pHRI for a comprehensive understanding and easier navigation of the field. We found out that most of the studies in direct pHRI (when there is direct physical contact) focus on social behaviors with belief being the most commonly addressed human factor type. Task collaboration is moderately investigated, while physical assistance is rarely studied. In contrast, indirect pHRI studies (when the physical contact is mediated via a third item) often involve industrial settings, with physical ergonomics being the most frequently investigated human factor. More research is needed on the human factors in direct and indirect physical assistance applications, including studies that combine physical social behaviors with physical assistance tasks. We also found that while the predominant approach in most studies involves the use of questionnaires as the main method of quantification, there is a recent trend that seeks to address the quantification approaches based on measurable data.

A Passivity-Based Framework for Safe Physical Human–Robot Interaction

In this paper, the problem of making a safe compliant contact between a human and an assistive robot is considered. Users with disabilities have a need to utilize their assistive robots for physical human–robot interaction (PHRI) during certain activities of daily living (ADLs). Specifically, we propose a hybrid force/velocity/attitude control for a PHRI system based on measurements from a six-axis force/torque sensor mounted on the robot wrist. While automatically aligning the end-effector surface with the unknown environmental (human) surface, a desired commanded force is applied in the normal direction while following desired velocity commands in the tangential directions. A Lyapunov-based stability analysis is provided to prove both the convergence as well as passivity of the interaction to ensure both performance and safety. Simulation as well as experimental results verify the performance and robustness of the proposed hybrid controller in the presence of dynamic uncertainties as well as safe physical human–robot interactions for a kinematically redundant robotic manipulator.

Human–Robot Attachment System for Exoskeletons: Design and Performance Analysis

Exoskeleton robots found application in neurorehabilitation, telemanipulation, and power augmentation. The human–robot attachment system of an exoskeleton should transmit all the interaction forces while keeping the anatomical and robotic joint axes aligned. Existing attachment concepts were bounding the performance of modern exoskeletons due to insufficient stiffness for high-performance force control, time-consuming adaption processes, and/or bulkiness. Therefore, we developed an augmented attachment system for a recent fully actuated nine-degree-of-freedom upper limb exoskeleton. The proposed system was compared to a conventional solution in a case study with four participants. The proposed attachment system lowered the relative motion between the human and the robot under static loads for all defined landmarks by 45% on average. The occurrence of undesired contacts in the trials was mitigated by 74%, thus improving conditions for closed-loop force control. Furthermore, the proposed system adapted better to the user's anatomy facilitating more accurate alignment and less obstruction. On average, self-attachment took <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathbf {43(8.3)}$</tex-math></inline-formula> <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathrm{s}$</tex-math></inline-formula> to don(doff). Thereby, the alignment of anatomic landmarks had typically less than 15 mm offset to a thorough expert alignment, making self-attachment eligible. The augmented attachment system and the insights gained by the case study are expected to enable improvement of the physical human–robot interaction of exoskeletons.

Aerial Physical Human Robot Interaction for Payload Transportation

Aerial Physical Human Robot Interaction for Payload Transportation

Validation of the Human Arm Stiffness Estimation Method Developed for Overground Physical Interaction Experiments.

To build a physically interactive robot for overground applications, it is crucial to first understand the biomechanics of humans underlying overground physical human-robot interaction (pHRI) tasks. Estimating human arm stiffness during overground interactive tasks is a promising first step toward this goal. For this, an arm stiffness estimation technique was developed in our previous works that consider the unique challenges involving overground pHRI, such as the need to estimate the arm stiffness from a short duration of data with fewer repetitions. In this work, our stiffness estimation method is further validated with a passive spring setup with known stiffness values, as well as with a human experiment setup that resembles the widely used seated reaching tasks. Results show that our method can estimate the passive spring stiffness within 0.5% of error. We also show that the human arm stiffness measured through our method is comparable to those reported in well-known literature. In addition, our method was able to discern experimental conditions such as early vs. late trials or differences in arm movement conditions. Implications of these results are discussed further.Clinical Relevance- This method can aid the design and development of overground interactive robots for human movement assistance and rehabilitation.

Collaborative robot dynamics with physical human–robot interaction and parameter identification with PINN

Collaborative robots are increasingly being used in dynamic and semi-structured environments because of their ability to perform physical Human–Robot Interaction (pHRI) to ensure safety. Therefore, it is crucial to model the dynamics of collaborative robots during pHRI to gain valuable insights into the system’s behavior when in contact with humans. In this work, a generic dynamic model is proposed for the quasi-static contact phase of pHRI, which considers the interaction dynamics and the complete structural dynamics of the collaborative robot. Moreover, a hybrid physics-informed neural network (PINN) is proposed, which utilizes a recurrent neural network (RNN) and the Runge–Kutta method to identify the joint dynamic parameters without complicated regressor construction. Experiments are conducted using a UR3e collaborative robot, and the PINN is trained using the acquired data. The results demonstrate the effectiveness of the PINN in identifying joint dynamics without prior knowledge, and the dynamic simulation of pHRI is consistent with the experimental results. The proposed model and PINN-based identification approach have the potential to improve safety and productivity in industrial environments by facilitating the control of pHRI.

RESEARCH ON HUMAN–ROBOT PHYSICAL INTERACTION CONTROL BASED ON ADAPTIVE IMPEDANCE CONTROL

An adaptive impedance control method based on dexterity for compliant interaction is proposed for the problem of compliance and motion performance of the human’s healthy side interacting with the manipulator in bilateral mirror rehabilitation motion-assisted training, and constructs a local virtual force field to hinder the movement of the manipulator to the low motion performance region, thus improving the motion performance of the manipulator. Firstly, the dynamic model of the manipulator and the human–robot interaction model based on impedance control are established. Then, a dexterity index based on the condition number is established, and an adaptive impedance control method based on the dexterity is proposed to construct a local virtual force field to hinder the movement of the manipulator to the low motion performance region. Finally, the effectiveness of the proposed method is demonstrated by experiments. The results have shown that the adaptive impedance control method based on dexterity can construct a local virtual force field, which can constrain the motion of the manipulator and keep the robot with good motion performance. It also laid the foundation for the training strategy of bilateral mirror rehabilitation.

Deep Imitation Learning of Nonlinear Model Predictive Control Laws for a Safe Physical Human–Robot Interaction

This paper proposes motion planning algorithms for industrial manipulators in the presence of human operators based on deep neural networks (DNNs), aimed at imitating the behavior of a nonlinear model predictive control (NMPC) scheme. The proposed DNN solutions retain the safety features of NMPC in terms of speed and separation monitoring, defined according to the guidelines in the ISO/TS 15066 standard. At the same time, they improve the robot performance in terms of task completion time, and of a-posteriori evaluation of the NMPC cost function on experimental data. The reasons for this improvement are the reduced computational delay of running a DNN compared to solving the nonlinear programs associated to NMPC, and the ability to implicitly learn how to predict the human operator's motion from the training set.

Safe High Stiffness Impedance Control for Series Elastic Actuators using Collocated Position Feedback

Physical human-robot interaction applications requiring safety and compliance characteristics usually employ mechanical devices such as series elastic actuators (SEAs). So far, impedance control schemes for SEAs have been investigated to overcome the limited displayable stiffness at the end point, represented by the stiffness of a physical spring, in a passive manner. SEAs usually mount two encoders on both the motor and load sides for position measurements. However, in several applications, the installation of a load-side encoder is not feasible from both cost and manufacturing perspectives. In this scenario, a Kalman Filter based on micro electro mechanical system accelerometers has been proposed for estimating the external force and load side quantities, without the need for load-side encoders. Herein, a novel impedance control scheme leveraging this approach is proposed to effectively overcome the stiffness of the physical spring while maintaining passivity at the end point. Compared with conventional load-side encoder-based impedance control schemes, the proposed control scheme presents comparable performance, as demonstrated by simulation and experimental results.

Imitation Learning-Based System for the Execution of Self-Paced Robotic-Assisted Passive Rehabilitation Exercises

The development of robotic-assisted rehabilitation exercises involving physical human-robot interaction requires extreme care since an injured limb may be in physical contact with the robot, so compliant behavior is imperative for these tasks. Typical approaches involve force control schemes like admittance controllers that allow humans to adapt the motion. However, when the patient's limb has limited mobility or is potentially injured, unintentional forces may occur during the robot's trajectory that could be incompatible with these controllers. This paper addresses a new way of generating compliant trajectories for passive rehabilitation exercises, considering that previous positions of the trajectory are attainable for the patient, so reversing the trajectory is a safe operation. Since there is no clear way to optimize such a goal due to the physiological variability among patients, the condition of reversal is based on imitation learning by taking the analogous healthy limb of the patient as a reference and encoding the forces using Gaussian Mixture Regression, and reversibility is accomplished by means of Reversible Dynamic Movement Primitives. The system allows for self-paced rehabilitation exercises by back-and-forth movements along the trajectory according to the patient's reaction, and it has been successfully applied to a 4-DOF parallel robot for lower-limb rehabilitation.

Collision avoidance analysis of human-robot physical interaction based on null-space impedance control of a dynamic reference arm plane.

When the terminal upper limb rehabilitation robot is used for motion-assisted training, collisions between the manipulator links and the human upper limb may occur due to the null-space self-motion of the redundant manipulator. A null-space impedance control method based on a dynamic reference arm plane is proposed to realize collision avoidance during human-robot physical interaction motion for the collision problem between the manipulator links and the human upper limb. Firstly, a dynamic model and a Cartesian impedance controller of the manipulator are established. Then, the null-space impedance controller of the redundant manipulator is established based on the dynamic reference plane, which manages the null-space self-motion of the redundant manipulator to prevent collision between the manipulator links and the human upper limb. Finally, it is experimentally verified that the method proposed in this paper can effectively manage the null-space self-motion of the redundant manipulator, and thus achieve collision avoidance during the human-robot physical interaction motion. This research has significant potential in improving the safety and feasibility of motion-assisted training with rehabilitation robots.

Development of an Active Physical Interface for Physical Human-Robot Interaction: Investigation of Soft Pneumatic Actuator Straps for Automatic Enclosure System

Wearable robots have become increasingly prevalent in various applications, including rehabilitation, power augmentation, and assistance. However, one of the challenges in designing wearable robots is how to attach them to the human body. The attachment method should be secure, reliable, comfortable, effective, and controlled for the user. Moreover, the attachment points should not interfere with the user’s daily activities, and the attachment process should not be time-consuming or complicated. Typical straps nowadays require a time-consuming and cumbersome donning and doffing procedure from therapists for users needing rehabilitation therapy. Therefore, we propose a novel pneumatically actuated soft strap to enclose the limb and automate part of the strapping procedure. This paper proposes a preliminary design utilizing soft bending actuators for attaching physical interfaces to humans, with integrated active elements for facilitating and automating the strapping process. Finite element analysis was conducted to assess pressure requirements, bending curvature, and geometry, with simulation results demonstrating a promising agreement, with a root mean square error (RMSE) of 3.4° in bending angle. In the future, an additional locking mechanism would be required to provide the necessary holding force and fully constrain the limb.

Spatiotemporal Compliance Control for a Wearable Lower Limb Rehabilitation Robot.

Compliance control is crucial for physical human-robot interaction, which can enhance the safety and comfort of robot-assisted rehabilitation. In this study, we designed a spatiotemporal compliance control strategy for a new self-designed wearable lower limb rehabilitation robot (WLLRR), allowing the users to regulate the spatiotemporal characteristics of their motion. The high-level trajectory planner consists of a trajectory generator, an interaction torque estimator, and a gait speed adaptive regulator, which can provide spatial and temporal compliance for the WLLRR. A radial basis function neural network adaptive controller is adopted as the low-level position controller. Over-ground walking experiments with passive control, spatial compliance control, and spatiotemporal compliance control strategies were conducted on five healthy participants, respectively. The results demonstrated that the spatiotemporal compliance control strategy allows participants to adjust reference trajectory through physical human-robot interaction, and can adaptively modify gait speed according to participants' motor performance. It was found that the spatiotemporal compliance control strategy could provide greater enhancement of motor variability and reduction of interaction torque than other tested control strategies. Therefore, the spatiotemporal compliance control strategy has great potential in robot-assisted rehabilitation training and other fields involving physical human-robot interaction.

Combined Admittance Control With Type II Singularity Evasion for Parallel Robots Using Dynamic Movement Primitives

This article addresses a new way of generating compliant trajectories for control using movement primitives to allow physical human–robot interaction where parallel robots (PRs) are involved. PRs are suitable for tasks requiring precision and performance because of their robust behavior. However, two fundamental issues must be resolved to ensure safe operation: first, the force exerted on the human must be controlled and limited, and second, Type II singularities should be avoided to keep complete control of the robot. We offer a unified solution under the dynamic movement primitives (DMP) framework to tackle both tasks simultaneously. DMPs are used to get an abstract representation for movement generation and are involved in broad areas, such as imitation learning and movement recognition. For force control, we design an admittance controller intrinsically defined within the DMP structure, and subsequently, the Type II singularity evasion layer is added to the system. Both the admittance controller and the evader exploit the dynamic behavior of the DMP and its properties related to invariance and temporal coupling, and the whole system is deployed in a real PR meant for knee rehabilitation. The results show the capability of the system to perform safe rehabilitation exercises.

Identification of human control law during physical Human–Robot Interaction

Identification of human control law during physical Human–Robot Interaction

Spatial Calibration of Humanoid Robot Flexible Tactile Skin for Human-Robot Interaction.

Recent developments in robotics have enabled humanoid robots to be used in tasks where they have to physically interact with humans, including robot-supported caregiving. This interaction-referred to as physical human-robot interaction (pHRI)-requires physical contact between the robot and the human body; one way to improve this is to use efficient sensing methods for the physical contact. In this paper, we use a flexible tactile sensing array and integrate it as a tactile skin for the humanoid robot HRP-4C. As the sensor can take any shape due to its flexible property, a particular focus is given on its spatial calibration, i.e., the determination of the locations of the sensor cells and their normals when attached to the robot. For this purpose, a novel method of spatial calibration using B-spline surfaces has been developed. We demonstrate with two methods that this calibration method gives a good approximation of the sensor position and show that our flexible tactile sensor can be fully integrated on a robot and used as input for robot control tasks. These contributions are a first step toward the use of flexible tactile sensors in pHRI applications.

Inflatable Humanoid Cybernetic Avatar for Physical Human–Robot Interaction

In a digital twin, a humanoid robot can be the counterpart of a simulated agent in the real world. In addition, a human, virtual avatar, and avatar robot might constitute digital triplets. We propose an inflatable cybernetic avatar (CA) with a humanoid upper body using an inflatable structure that can represent gestures. This inflatable CA is much lighter, safer, and cheaper than conventional humanoid robots and can be folded when deflated. These properties are ideal for physical human–robot interaction (pHRI) and allow real-time collection of human behavior through interaction. In the experiment, basic movements such as nodding and raising arms were measured using motion capture systems. This paper demonstrates the proposed inflatable CA in a hybrid event. We also conducted an experiment to measure the touch interactions using tactile sensors attached to the fabric of the inflatable part. A psychologically secure inflatable humanoid CA is a promising platform for physical interaction experiments.