Obstacle avoidance is a fundamental requirement for safe and reliable robot operation. Due to the lack of techniques capable of efficiently and accurately computing volumetric distances and their derivatives, many obstacle avoidance frameworks neglect the volumetric characteristics of objects by using points or spheres as the choice of object representation. To overcome this, an optimisation-based method is used in this letter to accurately compute the minimum distance between convex shapes. In order to enable integration with gradient-based obstacle avoidance algorithms, a method that calculates the gradient of the minimum distance is introduced. This information is incorporated into a Quadratic Program to demonstrate the effectiveness of the proposed approach for online obstacle avoidance. In contrast to methods that rely on closed-form expressions, the proposed framework leverages optimisation to simultaneously compute pairwise distances between multiple convex shapes. Simulation results, averaged over 1000 runs, demonstrate that the proposed approach completes tasks more accurately and consistently, improving trajectory tracking accuracy by up to 35% compared to the state-of-the-art baseline. The proposed framework is further validated on a physical robot system, confirming its practical applicability and robustness on real hardware.

General robot skill adaptation requires expressive representations robust to varying task configurations. While recent learning-based skill adaptation methods refined via Reinforcement learning (RL) have shown success, existing skill models often lack sufficient representational capacity for anything beyond minor environmental changes. In contrast, Gaussian process (GP)-based skill modeling provides an expressive representation with useful analytical properties; however, adaptation of GP-based skills remains underexplored. This letter proposes a novel, robust skill adaptation framework that utilises GPs with sparse via-points for compact and expressive modeling. The model leverages the demonstrated trajectory's first and second analytical derivatives to preserve the skill's kinematic profile. We present three adaptation methods to cater for the variability between initial and observed configurations. Firstly, an optimisation agent that adjusts the path's via-points while preserving the demonstration velocity. Second, a behaviour cloning agent trained to replicate output trajectories from the optimisation agent. Lastly, an RL agent that has learnt to modify via-points whilst maintaining the kinematic profile and faster feed-forward adaptation. Evaluated across three tasks (drawer opening, cube-pushing and bar manipulation) in both simulation and hardware, our proposed methods outperform every benchmark in success rates. Furthermore, the results demonstrate that the GP-based representation enables all three methods to attain high cosine similarity and low velocity magnitude errors, indicating strong preservation of the kinematic profile. Overall, our formulation provides a compact representation capable of adapting to large deviations from a single demonstrated skill.

A central problem in human–robot interaction is the risk of severe injury in humans in the event of a collision with a rigid robot arm. The introduction of variable stiffness into a robot arm mitigates the effects of impact and generates a safe interaction in its compliant state. An approach to vary the stiffness of members in a robotic arm is Laminar Jamming. In this article, a new lock/unlock mechanism for Laminar Jamming is proposed. The solution consists of a pneumatic actuator that drives a trapezoidal pin to interfere mechanically with the layers, and, in turn, changing the stiffness of the Laminar Jamming Structure. Additionally, frames are placed along the structure to avoid local buckling of the layers. Experiments and finite element simulations were carried out to study the mechanical performance of this new mechanism. Experiments show that the proposed mechanism reached a maximum stiffness ratio of 3.65, which is 15% higher than the stiffness ratio of an equivalent flat clamp mechanism. Experiments also demonstrate that the proposed mechanism does not show the stick-slip phenomenon that exists in the flat clamp mechanism. Computational case studies were carried out to investigate the effects of the angle of the trapezoidal pin, the number of frames, the direction of the transverse force and the behavior at high deflections. Simulations show that the 30° trapezoidal pin has the highest stiffness for pressures larger than 500 kPa, three frames placed along the Laminar Jamming generate the maximum stiffness ratio, the stiffness slightly varies when the transverse force changes direction, and the stiffness decreases with increasing deflection.

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%.

Navigation presents a significant challenge for persons with visual impairments (PVI). While traditional aids such as white canes and guide dogs are invaluable, they fall short in delivering detailed spatial information and precise guidance to desired locations. Recent developments in large language models (LLMs) and vision-language models (VLMs) offer new avenues for enhancing assistive navigation. In this paper, we introduce Guide-LLM, an embodied LLM-based agent designed to assist PVI in navigating large indoor environments. Our approach features a novel text-based topological map that enables the LLM to plan global paths using a simplified environmental representation, focusing on straight paths and right-angle turns to facilitate navigation. Additionally, we utilize the LLM's commonsense reasoning for hazard detection and personalized path planning based on user preferences. Simulated experiments demonstrate the system's efficacy in guiding PVI, underscoring its potential as a significant advancement in assistive technology. The results highlight Guide-LLM's ability to offer efficient, adaptive, and personalized navigation assistance, pointing to promising advancements in this field.

This study aims to evaluate a grip strength model designed to elucidate the relationship between measured grip force and muscular activity and assess its impact on an upper limb musculoskeletal model. This work aims to develop the grip strength model by utilizing a piecewise linear function based on the Woods and Bigland-Ritchie EMG-force model, which derives its parameters from 10 adult participants performing isometric and dynamic gripping tasks. Experimental results demonstrate the model's efficacy in estimating surface electromyography (sEMG) readings from force measurements, with a mean root mean square error (RMSE) of 0.2035 and a standard deviation of 0.1207. Moreover, incorporating sEMG readings associated with grip force does not significantly affect the optimization of muscle activation in the upper arm, as evidenced by kinematic data analysis from dynamic tasks. This validation underscores the model's potential to enhance musculoskeletal model-based motion analysis pipelines without distorting results. Consequently, this research emphasizes the prospect of integrating external models into existing human motion analysis frameworks, presenting promising implications for physical Human-Robot Interactions (pHRI). ### Competing Interest Statement The authors have declared no competing interest.

Learning from demonstration is one of the most promising methods to counteract the challenging long-term trends in repetitive industrial assembly. It offers not only a programming technique that is accessible to workers on the shop floor, reducing the need for robot experts and the associated costs but also a possible solution to the observable shift from mass-production to mass-customisation through flexible and generalising systems. Since the emergence of the learning from demonstration idea in the 1980s, its methodologies, capabilities, and achievements have constantly evolved. However, despite reports of continued progress in academic publications, the concept has not yet robustly emerged across the assembly industry. In light of its great potential, this paper presents the findings from a systematic literature review following the updated Preferred Reporting Items for Systematic Reviews (PRISMA) guidelines. It aims to provide an overview of the state-of-the-art learning from demonstration solutions developed for assembly-related tasks and offer a critical discussion of remaining obstacles in order to drive its progression towards meaningful deployments. The analysis includes a total of 61 papers over the period of 2013-2023 sourced from Scopus and Web of Science databases. Findings indicate that learning from demonstration has attained a significant level of maturity within the research environment, as evidenced by thorough experimental achievements, proving its great promise for industrial assembly applications. However, critical obstacles exist in the area of proven practicability, task complexity and diversity, generalisation, performance evaluation and integration concepts that require attention to promote its widespread adoption and create a seamless transition into industrial practices.

Actuator selection is critical in the design of human compatible robotics, such as prosthetics, exoskeletons, and humanoids. Each has its own set of parameters, from output specifications to package sizing and applicable environmental conditions. A multitude of design factors must be considered in selection, some are dictated by performance relations, while other engineering decisions are latent and unobserved. In biorobotic design, weight is often a key trade-off parameter with actuator performance. We analyze a database of over 1900 motors that are of relevant size for biorobotic designs to identify underlying trends that affect selection options. Selected motors range from 0.000013Nm to 3.66Nm in torque and 0.0016kg to 5.67kg in weight. We then generate Ashby-style charts to evaluate trends across motor selection dimensions. We find a wide disparity between manufacturers and where their actuators are specialized. The results provide a means for rapidly narrowing the selection space for designers, which is shown through an example application and reduces design time and improves the actuator selection.

With the development of collaborative robots (cobots), a paradigm shift in human-robot collaboration (HRC) is emerging in the workplace. When introducing cobots, a new range of hazards and harms needs to be considered. While physical hazards have been extensively studied and were paramount in the development of cobots, lesser-known hazards are related to mental and ethical wellbeing. Accordingly, most existing safety measures are designed to address exclusively physical hazards including ergonomics. To this end, this study sets out to develop holistic design principles for safe HRC by adopting a human-centred approach. A systematic review of the relevant literature combined with real-world insights gathered through interviews with industry and academic experts leads to design principles for safe HRC that can contribute to the future development of collaborative robot systems. This also highlights challenges which future research around safety guidelines and standards needs to address.

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.

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%.

Laminar jamming (LJ) is a method to achieve variable stiffness in robotics that has attracted notable attention because of its simple working principle and potential high stiffness variation. This article reviews the lock/unlock mechanisms of LJ structures. The application of these mechanisms in robotics is discussed, including grippers, continuum robots, wearable robots, robot arms, and more. Furthermore, the performance and limitations of the mechanisms to vary the stiffness of LJ are qualitatively and quantitatively analyzed. This performance analysis focuses mainly on the potential of LJ mechanisms to be applied in robot arms with variable stiffness and their potential to attenuate the impact between human beings and robot arms. The modeling of LJ through analytical and finite element methods is described, and their evolution towards design methodologies is discussed. To conclude, the directions and recommendations that should be followed in research on LJ are discussed. These include the improvement of existing lock/unlock mechanisms, the development of new lock/unlock mechanisms, and the development of more control algorithms for robot arms that incorporate LJ structures.

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.

Physical inactivity is still a major problem contributing to a growing public health crisis despite a fast-expanding body of technological solutions and wellness research around fitness training. The inaccessibility of professional fitness training remains a leading cause of this gap for reasons encompassing socioeconomic factors, cultural and demographic barriers, and more recently the threat of global pandemics that disrupt traditional modes of staying physically active. Previous lines of work have explored using AI for fitness activity recognition from various sensing modalities such as computer vision, wearable sensors, and force and pressure sensors. However, these works are limited by their feasibility, deployability, and accessibility in real-world scenarios, in addition to the technical challenges faced by each modality for accurate and reliable activity recognition. In this paper, we propose an accessible system for gym activity recognition and correction focusing on foundational fitness activities using ML and a novel pressure sensing mat, and validate its deployability in a real-world use case in a natural gym setting. We present the detailed and previously under-investigated Centre of Pressure (COP) profile of four main gym activities in terms of several COP-related metrics specifically as targets for ML-based recognition tasks. Based on this, we identify COP displacement and COP balance measures as important features for ML-based recognition of these fitness activities for future research in this area. Furthermore, we compare the performance of several ML models in the activity recognition task, achieving 98.5% recognition accuracy using ML models suitable for real-time deployment. Finally, we demonstrate the feasibility of our system in a live real-world with use case in a natural gym environment.

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.

Musculoskeletal models provide an approach towards simulating the ability of the human body in a variety of human-robot applications. A promising use for musculoskeletal models is to model the physical capabilities of the human body, for example, estimating the strength at the hand. Several methods of modelling and representing human strength with musculoskeletal models have been used in ergonomic analysis, human-robot interaction and robotic assistance. However, it is currently unclear which methods best suit modelling and representing limb strength. This paper compares existing methods for calculating and representing the strength of the upper limb using musculoskeletal models. It then details the differences and relative advantages of the existing methods, enabling the discussion on the appropriateness of each method for particular applications.

Dynamic Movement Primitives (DMPs) is a prominent method that enables single-shot learning from human demonstrated motion in a deterministically robust way. The acquired motion information can be translated into distinct situations with spatial and temporal deviations, allowing for robust repetition of the task taught. In this context, collision avoidance is a crucial aspect for autonomous reproduction within changing environments. Previous work on DMPs promoted potential fields for the avoidance of physical volumetric obstacles. In this work, the approach is extended to a multibody scene with four classes of shapes represented by superquadrics: the end-effector, physical obstacles, an imaginary workspace, and an attractive goal state. As a result, the volumetric end-effector allows both translational and rotational deviations to generate more effective avoiding manoeuvres. Furthermore, the novel workspace and attractive goal shapes are introduced to increase the robustness while deviating from the original path and accelerate the convergence to the final pose. The effectiveness of the proposed method is showcased in a series of experiments including a physical Learning from Demonstration use case, in which its value for practical applications is demonstrated.

In recent times, learning by demonstration has seen tremendous progress in robotic assembly operations. One of the most prominent trajectory-level task models applied is Dynamic Movement Primitives (DMP). However, it lacks the ability to tackle complex operations as often encountered in industrial assembly. Augmenting low-level models with a high-level framework in which different movement segments are deliberately parameterised is considered promising for such scenarios. This paper investigates the combination of trajectory-level DMPs with Methods-Time Measurement (MTM). We demonstrate how the MTM-1 system is utilised to establish distinguished DMP models for five of its basic elements, paving the way to benefitting from the sophisticated MTM system. The evaluation of the framework is conducted on a generic pick and place operation. Compared to a one-model-fits-all DMP approach for the whole task, the proposed method shows the advantage of appropriate temporal scaling, accuracy levelling and force consideration at adequate times.

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.