Mobile robots are increasingly deployed in shared environments where they must learn to navigate alongside humans. Deep Reinforcement Learning (DRL) techniques have shown promise in developing navigation policies that account for interactions within crowds, fostering socially acceptable movement. However, these techniques often depend heavily on collision avoidance rewards to ensure safe navigation. In this study, we introduce a novel reward component based on relative velocity for collision avoidance, which integrates both the robot's and humans' kinematics within personal distance constraints. We conducted a thorough evaluation comparing this new reward model against a conventional one in simulated environments using advanced DRL methods. Our findings indicate that the proposed reward model improves the robots' ability to avoid collisions and navigate towards their goals while being socially acceptable.

When performing total hip replacement (THR) surgery, high-quality preparation of acetabulum is critical as it contributes to the patient’s recovery speed and the consistency of bone ingrowth. Conventionally, surgeons prepare the acetabulum manually by reaming it with a handheld electric drill and a reamer. It not only increases the surgeon’s workload but more importantly, it is difficult to control the reaming depth and direction accurately. Utilizing an admittance-controlled (AC) collaborative robot (cobot) to enable physical human-robot collaboration (pHRC) possesses a promising solution. For primitive AC, a compromise must be made between compliance and task accuracy. In this paper, we present a novel variable admittance control (VAC) design that considers the reactive force of bone while ensuring the passivity and stability of the system during pHRC-assisted acetabular preparation. The qualitative results show that VAC was more desirable by users than the conventional manual reaming method. Compared to other pHRC controls, quantitative results on user energy consumption, reaming error, and smoothness showed the proposed VAC can achieve a balance between physical workload and acetabular quality. Compared to manual reaming, VAC reduced the reaming error by 67.47% and improved the final acetabulum surface smoothness by 18.30%.

Advances in human sensing and machine learning are paving the way for new applications of robotics in sports and fitness, making skill coaching smarter, easier and more accessible. Physical and social human robot interaction in particular has received special attention as a feedback mechanism for human performance augmentation. A core challenge in deploying robots that interact physically with humans in dynamic environments such as sports, relates to modeling human skills and designing appropriate interaction schemes. We present the first ML-based HRI platform for physical robot to human skill coaching in real-time in Martial Arts which can be extended to various sports. Our system comprises of the Sawyer robot, our specially developed IoT katana and a skill-training program for the Martial Art of Iaido. We built and deployed in real-time a ML-based Iaido strike recognition model trained on expert and beginner data, and achieved accuracies ranging between 94.8% and 99.97%. We assessed the system’s effectiveness in coaching skills through robot interaction in a sparring experiment and a survey involving 12 participants practicing key Iaido techniques with guided training from Sawyer. Our results demonstrated improvement in all participants’ Iaido strike skill after training with Sawyer, and they responded positively to robot-assisted skill coaching.

Mobile physical human-robot collaboration (pHRC) using collaborative robots (cobots) and mobile robots has attracted much research attention. Many researchers have focused on improving the control performance to comply with human intentions. However, a problem that generally exists with mobile pHRC but often gets neglected is the impact of non-rigid components e.g. deformable tyres, suspension systems and uneven terrain on human interaction experience and task performance. To fullfil this current research gap, we carried out an investigation on the above-mentioned problem by altering a cobot’s base rigidity level (also referred to as base compliance level or BCL) during pHRC experiments. We explored how the task performance is affected by base compliance as well as human operator’s experience and cobot control parameters. Measurements include the human operator’s physical effort, task velocity, and task error. From the experimental results, it is discovered that base compliance has a significant impact on task accuracy as it can easily excite the system if an inadequate control strategy is deployed. Furthermore, through ANOVA, it is discovered that the influence of base compliance can be minimized and system excitation can be avoided by sufficient human operator training and the appropriate selection of cobot’s control parameters.

The Rating Scale method has been long deemed the standard for measuring subjective perceptions. However, in the field of physical human-robot collaboration (pHRC), its aptness should be put under scrutiny due to inherent challenges such as response bias, between-subject variations, and the granularity nature.Individual variances can introduce significant bias in the rating scale results. A high granularity in the scale could overwhelm participants, leading to unclear and biased responses, while a low granularity may gloss over the fine nuances of human feelings. Additionally, there’s a notable risk of receiving careless responses, which compromise data reliability. Recognizing these challenges, this paper proposes the application of Pairwise Comparison (PC) in pHRC — an alternative survey technique that emphasizes direct comparisons between items on the defined criteria. By using the NASA Task Load Index (NASA-TLX) as a template, RS and PC questionnaires are designed and used in a series of pHRC experiments. Our preliminary findings suggest that PC is more precise and robust than the rating scale method. Compared to RS, PC fosters authentic participant interests in the experiment by intuitive question design and reducing the experimental duration. Besides, the accuracy and reliability of PC are also found to be consistent regardless of the variations in our experimental procedure design.

In this paper, we propose a robot skill-learning method that facilitates fast adaption to new tasks online. Our method is based on a hybrid learning from demonstration and reinforcement learning approach, which seeds learning with a compact and structured skill model, leading to efficient and stable behaviours. To facilitate fast skill adaption, we propose a bootstrapped learning framework that learns a policy for adapting a skill model across a wide range of initial conditions in simulation. This policy is then used to bootstrap a refinement process that quickly adapts the learnt skill model to new initial conditions in a few learning iterations. Our refined skill model is designed to be deployable on hardware and can correct for discrepancies between the simulation and the real world. Furthermore, we propose a novel method for constraining policy exploration to promising trajectories, which is crucial for enabling manipulation in complex environments. We evaluate our framework in simulation and hardware in multiple environments with varying task complexity. We showcase that compared to the state-of-the-art, which achieves an average success rate of only 56.6% across three different tasks of varying difficulty, our algorithm significantly outperforms it with an average success rate of 90%.

Role arbitration in human-robot collaboration (HRC) is a dynamically changing process that is affected by many factors such as physical workload, environmental changes and trust. In order to address this dynamic process, a trust-based role arbitration method is studied in this research. A computational model of robot trust and self-confidence (TSC) in physical human-robot collaboration (pHRC) is proposed. The TSC model is defined as a function of objective robot and human co-worker performance. A role arbitration method is then proposed based on the TSC model presented. The human-in-the-loop experiments with a collaborative robot are conducted to verify the TSC-based role arbitration method. The results show that the proposed method could achieve superior human-robot combined performance, reduce human co-workers' workload, and improve subjective preference.

Human and machine interaction is shaping the future of work in a growing body of applications ranging from big data analysis to healthcare. While many employer-centered solutions have emerged in recent years in areas such as crowd-sourcing and big-data analysis, worker-centered approaches have received less attention. In this work, we explore a worker-centered application of Human-Robot Interaction (HRI) for improving human well-being at work through actively sensing and recognising sitting posture habits. We present a platform for data-driven human sitting posture training based on adaptive HRI feedback using a novel robot called Owro. Owro is an owl-shaped emotive desktop robot that connects with the LifeChair, which is an IoT cushion for sitting posture training. The robot displays various emotive feedback to correct poor sitting habits based on the actively recognised human sitting posture. Our results demonstrated that the HRI feedback was effective in improving sitting posture by significantly increasing the percentage of time spent sitting upright from 15.62% to 47.96%, significantly reducing the percentage of amount of time spent slouching from 84.38% to 52.04%, and promoting a healthier back pressure distribution. We also show preliminary survey results on the human perception of the HRI feedback using Owro.

In physical human-robot collaboration (pHRC), singularity avoidance strategies are often critical to obtain stable interaction dynamics. It is hypothesised a predictable singularity avoidance strategy is preferred in pHRC as humans tend to maximise predictability when using complex systems. By using an electroencephalogram (EEG), it is possible to assess the predictability of a task through a feature found in event-related potentials (ERP) and called prediction-error negativity (PEN). In this paper, two research questions are addressed. Can a complex pHRC singularity avoidance strategy generate a detectable PEN? Are PEN and human preferences related when comparing different control settings in a singularity avoidance strategy? Fourteen participants compared two different sets of parameters (modes) in a singularity avoidance strategy based on the exponentially damped least-squared (EDLS) method. ERP results are presented in terms of power spectral density (PSD). ERP results were then compared with human preferences to see whether they are related. Results show that the mode that causes PEN is also the one that participants did not like, suggesting that a lack of predictability might have an impact on human preference.

When a robot manipulator approaches a kinematic singular configuration, control strategies need to be employed to ensure safe and robust operation. If this manipulator is being controlled by a human through physical human-robot collaboration, the choice of strategy for handling singularities can have a significant effect on the feelings and impressions of the user. To date the preferences of humans during physical human-robot collaboration regarding strategies for managing kinematic singularities have yet to be thoroughly explored.This work presents an empirical study of a damping-based strategy for handling singularities with regard to the preferences of the human operator. Two different parameters, damping rate and damping asymmetry, are tested using a double-blind A/B pairwise comparison testing protocol. Participants included two cohorts made up of the general public (n=51) and people working within a robotic research centre (n=18). In total 105 individual trials were performed. Results indicate a preference for a faster, asymmetric damping behavior that slows motions towards singularities whilst allowing for faster motions away.

Physical Human-Robot Collaboration (pHRC) is about the interaction between one or more human operator(s) and one or more robot(s) in direct contact and voluntarily exchanging forces to accomplish a common task. In any pHRC, the intuitiveness of the interaction has always been a priority, so that the operator can comfortably and safely interact with the robot. So far, the intuitiveness has always been described in a qualitative way. In this paper, we suggest an objective way to evaluate intuitiveness, known as prediction error negativity (PEN) using electroencephalogram (EEG). PEN is defined as a negative deflection in event related potential (ERP) due to cognitive conflict, as a consequence of a mismatch between perception and reality. Experimental results showed that the forces exchanged between robot and human during pHRC modulate the amplitude of PEN, representing different levels of cognitive conflict. We also found that PEN amplitude significantly decreases (p <; 0.05) when a mechanical resistance is being applied smoothly and more time in advance before an invisible obstacle, when compared to a scenario in which the resistance is applied abruptly before the obstacle. These results indicate that an earlier and smoother resistance reduces the conflict level. Consequently, this suggests that smoother changes in resistance make the interaction more intuitive.

We present the ANBOT, an intelligent robotic coworker for physical human-robot collaboration. The ANBOT system assists workers performing industrial abrasive blasting, shielding them from the large forces experienced during this physically demanding task. The co-operative robotic system combines the strength and endurance of robots with the decision making of skilled workers. The inherent challenges in human-robot collaboration, combined with the difficult blasting environment required novel design decisions to be made and new solutions to be developed. These include an approach for handling kinematic singularities in a manner suitable for human-robot co-operation, estimating worker pose under poor visibility conditions, and an intuitive control scheme that adapts the robotic assistance based on the estimated strength of the worker. In this work we summarise the ANBOT system and present findings from preliminary site trials. The trials included several real industrial blasting tasks under the control of a skilled abrasive blasting worker who had no experience working alongside a robot. Results demonstrate the suitability of the ANBOT for practical industrial applications.

This paper presents a new configuration for single axis tactile sensor arrays molded in rubber to enable tri-axial force measurement. The configuration requires the sensing axis of each sensor in the array to be rotated out of alignment with respect to external forces. This angled sensor array measures shear forces along axes in a way that is different to a planar sensor array. Three sensors using the angled configuration (22.5°, 45° and 67.5°) and a fourth sensor using the planar configuration (0°) have been fabricated for experimental comparison. Artificial neural networks were trained to interpret the external force applied along each axis (X, Y and Z) from raw pressure sensor values. The results show that the angled sensor configuration is capable of measuring tri-axial external forces with a root mean squared error of 1.79N, less error in comparison to the equivalent sensor utilizing the planar configuration (4.52N). The sensors are then implemented to control a robotic arm. Preliminary findings show angled sensor arrays to be a viable alternative to planar sensor arrays for shear force measurement; this has wide applications in physical Human Robot Interaction (pHRI).

Autonomous Underwater Vehicle-Manipulator Systems (AUVMS) operating in shallow waters or near-surface environments may be exposed to wave disturbances which will cause undesired motion of the end effector. This paper presents a method to maneuver the manipulator joints and counteract undesired motion of the vehicle body, in order to maintain a steady end-effector position in the inertial frame. An Autoregressive (AR) model is used to predict vehicle motion, and then combined with Model Predictive Control (MPC) to optimize joint motion. Simulation was conducted using real data to verify the efficacy of this method.

Compared to a robotic system that performs a task alone, a robot coworker performing tasks in collaboration with a human operator is subject to additional constraints which can limit the ability of the system to perform the task as required. This work presents a framework for analyzing the ability of a robotic coworker to perform specific tasks in collaboration with a human. The framework allows systematic evaluation of robotic systems based on traditional robot performance measures such as reachable workspace and payload capacity, as well as considering additional factors which arise due to the task being performed collaboratively with a human; such as the reach and strength of the human, human-robot collision, and satisfying desired assistance paradigms. Application of the framework is demonstrated in a case study analyzing a robot designed to assist a human during a materials handling task.

Robotic systems such as exoskeletons can be effectively used in the reduction of fatigue and musculoskeletal disorders (MSD) associated with physical tasks, but robots which work in physical contact with humans pose problems with user safety. A novel approach to developing intrinsically safe robots is to use passive actuators which have the advantage of being safer, ensuring stability, high force/weight ratios and lower power consumption. It is however not clear how effective an exoskeleton utilizing passive actuators would be in reducing fatigue and the risk of MSD. This paper analyzes the benefit of using such a system with results from dynamic simulations and an experiment using a specially designed mechanism used for evaluation. Results indicate that fatigue and effort could be reduced if robot impedance is minimized. Experiments also highlighted issues of implementing such a system into practice.