A major challenge in robotics is the ability to learn, from novel experiences, new behaviour that is useful for achieving new goals and skills. Autonomous systems must be able to learn solely through the environment, thus ruling out a priori task knowledge, tuning, extensive training, or other forms of pre-programming. Learning must also be cumulative and incremental, as complex skills are built on top of primitive skills. Additionally, it must be driven by intrinsic motivation because formative experience is gained through autonomous activity, even in the absence of extrinsic goals or tasks. This paper presents an approach to these issues through robotic implementations inspired by the learning behaviour of human infants. We describe an approach to developmental learning and present results from a demonstration of longitudinal development on an iCub humanoid robot. The results cover the rapid emergence of staged behaviour, the role of constraints in development, the effect of bootstrapping between stages, and the use of a schema memory of experiential fragments in learning new skills. The context is a longitudinalexperiment in which the robot advanced from uncontrolled motor babbling to skilled hand/eyeintegrated reaching and basic manipulation of objects. This approach offers promise for furtherfast and effective sensory-motor learning techniques for robotic learning.

As robots become more ubiquitous, and their capabilities extend, novice users will require intuitive instructional information related to their use. This is particularly important in the manufacturing sector, which is set to be transformed under Industry 4.0 by the deployment of collaborative robots in support of traditionally low-skilled, manual roles. In the first study of its kind, this paper reports how static graphical signage can improve performance and reduce anxiety in participants physically collaborating with a semi-autonomous robot. Three groups of 30 participants collaborated with a robot to perform a manufacturing-type process using graphical information that was relevant to the task, irrelevant, or absent. The results reveal that the group exposed to relevant signage was significantly more accurate in undertaking the task. Furthermore, their anxiety towards robots significantly decreased as a function of increasing accuracy. Finally, participants exposed to graphical signage showed positive emotional valence in response to successful trials. At a time when workers are concerned about the threat posed by robots to jobs, and with advances in technology requiring upskilling of the workforce, it is important to provide intuitive and supportive information to users. Whilst increasingly sophisticated technical solutions are being sought to improve communication and confidence in human-robot co-working, our findings demonstrate how simple signage can still be used as an effective tool to reduce user anxiety and increase task performance.

There is an increasing interest in considering, measuring, and implementing trust in human-robot interaction (HRI). New avenues in this field include identifying social means for robots to influence trust, and identifying social aspects of trust such as a perceptions of robots’ integrity, sincerity or even benevolence. However, questions remain regarding robots’ authenticity in obtaining trust through social means and their capacity to increase such experiences through social interaction with users. We propose that the dyadic model of HRI misses a key complexity: a robot’s trustworthiness may be contingent on the user’s relationship with, and opinion of, the individual or organisation deploying the robot (termed here, Deployer). We present a case study in three parts on researching HRI and a LEGO^® Serious^® Play focus group on care robotics to indicate how Users’ trust towards the Deployer can affect trust towards robots and robotic research. Our Social Triad model (User, Robot, Deployer) offers novel avenues for exploring trust in a social context.

Collaborative robots offer opportunities to increase the sustainability of work and workforces by increasing productivity, quality, and efficiency, whilst removing workers from hazardous, repetitive, and strenuous tasks. They also offer opportunities for increasing accessibility to work, supporting those who may otherwise be disadvantaged through age, ability, gender, or other characteristics. However, to maximise the benefits, employers must overcome negative attitudes toward, and a lack of confidence in, the technology, and must take steps to reduce errors arising from misuse. This study explores how dynamic graphical signage could be employed to address these issues in a manufacturing task. Forty employees from one UK manufacturing company participated in a field experiment to complete a precision pick-and-place task working in conjunction with a collaborative robotic arm. Twenty-one participants completed the task with the support of dynamic graphical signage that provided information about the robot and the activity, while the rest completed the same task with no signage. The presence of the signage improved the completion time of the task as well as reducing negative attitudes towards the robots. Furthermore, participants provided with no signage had worse outcome expectancies as a function of their response time. Our results indicate that the provision of instructional information conveyed through appropriate graphical signage can improve task efficiency and user wellbeing, contributing to greater workforce sustainability. The findings will be of interest for companies introducing collaborative robots as well as those wanting to improve their workforce wellbeing and technology acceptance.

Human-robot collaboration is on the increase and having a major impact on areas such as manufacturing, where the abilities of the human worker, augmented by those of the robot, bring increased flexibility and performance. However, close collaboration, including physical interaction, brings with it complex safety and security issues that were previously mitigated by human-robot segregation and isolated control networks. Exoskeletons pose a particularly interesting case whereby physical coupling of the user and robot is required throughout operation. We envisage the use of continuous authentication to exoskeletons, i.e. to ensure a user is who they claim to be, and that they have sufficient authority to operate the device for the duration of its use. In this paper we demonstrate such an approach to behavioural biometrics using data acquired through wearable sensors (hand manipulations recorded by a sensorised glove) while the user performs a selection of industrial tasks, including handling loads and inserting screws. The results show that the approach can discriminate between users with a low Equal Error Rate (EER; <3% in the worst case analysed). We believe that such an approach will also benefit other applications where wearables are used in robot control, such as in tele-operation.