AI-enhanced reasoning enables robots to create detailed accounts of their own situated behaviour as well as the behaviour of other people. This capability is currently employed by robot designers to achieve transparency, trust, and enhance robot social and communicative capabilities. Furthermore, robots may be designed to resemble humans both in their physical appearance and their behaviour. This approach is intended to facilitate more effective interactions with people. In this article we identify and examine some of the ethical, social and legal implications of these capabilities for the investigation of robot accidents. We consider two aspects in particular. The first of these is the role of robots as subjects in a testimony regarding an incident in which they are directly or indirectly involved. This can be described as a case of robots acting as witnesses. The second aspect is the role of robots as objects in a human testimony. This can be described as a case of robots being witnessed.

In this paper, we address the question: what practices would be required for the responsible design and operation of real-world swarm robotic systems? We argue that swarm robotic systems must be developed and operated within a framework of ethical governance. We will also explore the human factors surrounding the operation and management of swarm systems, advancing the view that human factors are no less important to swarm robots than social robots. Ethical governance must be anticipatory, and a powerful method for practical anticipatory governance is ethical risk assessment (ERA). As case studies, this paper includes four worked examples of ERAs for fictional but realistic real-world swarms. Although of key importance, ERA is not the only tool available to the responsible roboticist. We outline the supporting role of ethical principles, standards, and verification and validation. Given that real-world swarm robotic systems are likely to be deployed in diverse ecologies, we also ask: how can swarm robotic systems be sustainable? We bring all of these ideas together to describe the complete life cycle of swarm robotic systems, showing where and how the tools and interventions are applied within a framework of anticipatory ethical governance.This article is part of the theme issue 'The road forward with swarm systems'.

As robot numbers in the home increase, creating a market for second-hand robotic systems is essential to reduce the waste impact of the industry. Via a survey, consumer attitudes of United Kingdom participants towards second-hand robots were investigated; finding that second-hand robots with guarantees have an equal purchasing interest compared to new systems, highlighting the opportunity for manufacturers and retailers to develop certification standards for second-hand robots to move towards a circular economy. Consumer demographics also demonstrated that those most open to the purchase of both new and second-hand systems were women, those aged 18-25 years old, and those who have previously owned a robot for the home. Participants' prior ownership of second-hand electronic devices (such as phones and laptops) did not affect rates of interest for second-hand robotic systems suggesting that the technology is still too new for people to be able to project their experience of current second-hand electronics to that of a robot. Additionally, this research found the robotics industry can consider the potential market for second-hand robots to be more similar to the second-hand smartphone market than to the household electronics market, and lessons learnt from the concerns raised by consumers for other internet-enabled electronic devices are similar to those concerns for second-hand robots. This provides an opportunity for the industry to break down the barriers for a circular economy earlier in the technology maturity process than has been seen for other electronics.

The discrepancy between simulated and hardware experiments, the reality gap, is a challenge in evolutionary robotics. While strategies have been proposed to address this gap in fixed-body robots, they are not viable when dealing with populations and generations where the body is in constant change. The continual evolution of body designs necessitates the manufacturing of new robotic structures, a process that can be time-consuming if carried out manually. Moreover, the increased manufacturing time not only prolongs hardware experimental durations but also disrupts the synergy between hardware and simulated experiments. Failure to effectively manage these challenges could impede the implementation of evolutionary robotics in real-life environments. The Autonomous Robot Evolution project presents a framework to tackle these challenges through a case study. This paper describes the main three contributions of this work: Firstly, it analyses the different reality gap experienced by each different robot or the heterogenous reality gap. Secondly, it emphasizes the importance of automation in robot manufacturing. And thirdly, it highlights the necessity of a framework to orchestrate the synergy between simulated and hardware experiments. In the long term, integrating these contributions into evolutionary robotics is envisioned to enable the continuous production of robots in real-world environments.

This paper reports a new process which can be used to repurpose a robot at the end of its primary life. Repurposing a robot enables it to continue delivering value beyond the point at which it might have been otherwise recycled or scrapped. The repurposing process was created using the future thinking forecasting methodology known as a Delphi Study. This paper shows there are some similarities between a process for repurposing, to processes for developing new systems and remanufacturing systems. However this new process contains unique elements, highlighting the value and potential of challenging accepted norms for the management of robot systems when they reach the end of their useful life and could otherwise be considered e-waste.

Physically embodied artificial agents, or robots, are being incorporated into various practical and social contexts, from self-driving cars for personal transportation to assistive robotics in social care. To enable these systems to better perform under changing conditions, designers have proposed to endow robots with varying degrees of autonomous capabilities and the capacity to move between them—an approach known as variable autonomy. Researchers are beginning to understand how robots with fixed autonomous capabilities influence a person’s sense of autonomy, social relations, and, as a result, notions of responsibility; however, addressing these topics in scenarios where robot autonomy dynamically changes is underexplored. To establish a research agenda for variable autonomy that emphasises the responsible design and use of robotics, we conduct a developmental review. Based on a sample of 42 papers, we provide a synthesised definition of variable autonomy to connect currently disjointed research efforts, detail research approaches in variable autonomy to strengthen the empirical basis for subsequent work, characterise the dimensions of variable autonomy, and present design guidelines for variable autonomy research based on responsible robotics.

This paper describes the enactment of a simulated (mock) accident involving an upper-body exoskeleton and its investigation. The accident scenario is enacted by role-playing volunteers, one of whom is wearing the exoskeleton. Following the mock accident, investigators - also volunteers - interview both the subject of the accident and relevant witnesses. The investigators then consider the witness testimony alongside robot data logged by the ethical black box, in order to address the three key questions: what happened?, why did it happen?, and how can we make changes to prevent the accident happening again? This simulated accident scenario is one of a series we have run as part of the RoboTIPS project, with the overall aim of developing and testing both processes and technologies to support social robot accident investigation.

The use of evolutionary robotic systems to model aspects of evolutionary biology is well-established. Yet, few studies have asked the question, “What kind of model is an evolutionary robotic system?” This paper seeks to address that question in several ways. First, it is addressed by applying a structured model description developed for physical robot models of animal sensorimotor systems, then by outlining the strengths and limitations of evolutionary robotics for modelling evolutionary biology, and, finally, by considering the deepest questions in evolution and which of them might feasibly be modelled by evolutionary robotics. The paper concludes that although evolutionary robotics faces serious limitations in exploring deeper questions in evolutionary biology, its bottom-up approach to modelling populations of evolving phenotypes and their embodied interactions holds significant value for both testing and generating hypotheses.

Jointly optimising both the body and brain of a robot is known to be a challenging task, especially when attempting to evolve designs in simulation that will subsequently be built in the real world. To address this, it is increasingly common to combine evolution with a learning algorithm that can either improve the inherited controllers of new offspring to fine tune them to the new body design or learn them from scratch. In this paper an approach is proposed in which a robot is specified indirectly by two compositional pattern producing networks (CPPN) encoded in a single genome, one which encodes the brain and the other the body. The body part of the genome is evolved using an evolutionary algorithm (EA), with an individual learning algorithm (also an EA) applied to the inherited controller to improve it. The goal of this paper is to determine how to utilise the results of learning process most effectively to improve task performance of the robot. Specifically, three variants are investigated: (1) evolution of the body+controller only; (2) a learning algorithm is applied to the inherited controller with the learned fitness assigned to the genome; (3) learning is applied and the genome is updated with the learned controller, as well as being assigned the learned fitness. Experiments are performed in three different scenarios chosen to favour different bodies and locomotion patterns. It is shown that better performance can be obtained using learning but only if the learned controller is inherited by the offspring.

Benchmarking, standards and certification are closely related processes. Standards can provide normative requirements that robotics and AI systems may or may not conform to. Certification generally relies upon conformance with one or more standards as the key determinant of granting a certificate to operate. And benchmarks are sets of standardised tests against which robots and AI systems can be measured. Benchmarks therefore can be thought of as informal standards. In this paper we will develop these themes with examples from benchmarking, standards and certification, and argue that these three linked processes are not only useful but vital to the broader practice of Responsible Innovation.

Robots have the potential to deliver very positive impacts for society, however, it's critical that in preparing for real-world deployments, we recognize and take steps to mitigate against the potential harms, both direct and indirect, that they may cause. In this paper, we explore how the ethics canvas (EC) and the ethical risk assessment (ERA) methodology defined in British Standard 8611 can be combined to better align robot technologies with ethics and their socio-cultural context of operation. We illustrate this through a practical case-study involving the real-world introduction of a disinfection robot to a radiology department in a European hospital. Using the EC, we identified 49 distinct ways that the technology was likely to impact key stakeholders and 11 ways that failure or misuse of the technology was likely to impact service provision. From this data, 8 mitigating measures were identified. Then, using the ERA tool, 9 risks were identified that were considered to represent a high likelihood of occurrence. From these insights, a further 8 mitigation measures were proposed. The combined use of both tools was found to be complementary, since the EC fostered a bottom-up, subjective critical thinking process whereas the ERA provided a broader, more top-down objective view. This example provides a practical template for robotics practitioners to better understand and manage the ethical and socio-cultural dimensions of their work, and contributes towards the standardization of ethical assessments in robotics with an emphasis on the move from principles to practice.

This paper draws on three case studies to examine some of the challenges and tensions involved in the use of Autonomous Decision-Making Systems (ADMS). In particular, the paper highlights: (i) challenges around the shifting “locale” of the decision, and the associated consequences for stakeholders; (ii) potential implications for stakeholders from regulation such as the General Data Protection Regulation (GDPR); (iii) the different values that stakeholder groups bring to the “decision” question; (iv) how complex pre-existing webs of stakeholders and decision-making authorities may be disrupted or disempowered by the use of an automated system and the lack of evaluation of possible consequences; (v) how ADMS for non-technical users can lead to circumvention of the boundaries of intended system use. We illustrate these challenges through case studies in three domains: adult social care, aviation, and vehicle driver monitoring systems. The paper closes with recommendations for both practice and policy in the deployment of ADMS.

Most work in evolutionary robotics centers on evolving a controller for a fixed body plan. However, previous studies suggest that simultaneously evolving both controller and body plan could open up many interesting possibilities. However, the joint optimization of body plan and control via evolutionary processes can be challenging in rich morphological spaces. This is because offspring can have body plans that are very different from either of their parents, leading to a potential mismatch between the structure of an inherited neural controller and the new body. To address this, we propose a framework that combines an evolutionary algorithm to generate body plans and a learning algorithm to optimize the parameters of a neural controller. The topology of this controller is created once the body plan of each offspring has been generated. The key novelty of the approach is to add an external archive for storing learned controllers that map to explicit “types” of robots (where this is defined with respect to the features of the body plan). By initiating learning from a controller with an appropriate structure inherited from the archive, rather than from a randomly initialized one, we show that both the speed and magnitude of learning increase over time when compared to an approach that starts from scratch, using two tasks and three environments. The framework also provides new insights into the complex interactions between evolution and learning.

The evolutionary robotics field offers the possibility of autonomously generating robots that are adapted to desired tasks by iteratively optimising across successive generations of robots with varying configurations until a high-performing candidate is found. The prohibitive time and cost of actually building this many robots means that most evolutionary robotics work is conducted in simulation, but to apply evolved robots to real-world problems, they must be implemented in hardware, which brings new challenges. This paper explores in detail the design of an example system for realising diverse evolved robot bodies, and specifically how this interacts with the evolutionary process. We discover that every aspect of the hardware implementation introduces constraints that change the evolutionary space, and exploring this interplay between hardware constraints and evolution is the key contribution of this paper. In simulation, any robot that can be defined by a suitable genetic representation can be implemented and evaluated, but in hardware, real-world limitations like manufacturing/assembly constraints and electrical power delivery mean that many of these robots cannot be built, or will malfunction in operation. This presents the novel challenge of how to constrain an evolutionary process within the space of evolvable phenotypes to only those regions that are practically feasible: the viable phenotype space. Methods of phenotype filtering and repair were introduced to address this, and found to degrade the diversity of the robot population and impede traversal of the exploration space. Furthermore, the degrees of freedom permitted by the hardware constraints were found to be poorly matched to the types of morphological variation that would be the most useful in the target environment. Consequently, the ability of the evolutionary process to generate robots with effective adaptations was greatly reduced. The conclusions from this are twofold. 1) Designing a hardware platform for evolving robots requires different thinking, in which all design decisions should be made with reference to their impact on the viable phenotype space. 2) It is insufficient to just evolve robots in simulation without detailed consideration of how they will be implemented in hardware, because the hardware constraints have a profound impact on the evolutionary space.

This paper introduces a draft open standard for the robot equivalent of an aircraft flight data recorder, which we call an ethical black box. This is a device, or software module, capable of securely recording operational data (sensor, actuator and control decisions) for a social robot, in order to support the investigation of accidents or near-miss incidents. The open standard, presented as an annex to this paper, is offered as a first draft for discussion within the robot ethics community. Our intention is to publish further drafts following feedback, in the hope that the standard will become a useful reference for social robot designers, operators and robot accident/incident investigators.

Risk Assessment is a well known and powerful method for discovering and mitigating risks, and hence improving safety. Ethical Risk AssessmentEthical Risk Assessment uses the same approach, but extends the scope of risk to cover ethical risks in addition to safety risks. In this paper we outline Ethical Risk AssessmentEthical Risk Assessment (ERA), and set ERA within the broader framework of Responsible RoboticsResponsible Robotics. We then illustrate ERA, first with a hypothetical smart robotRobots teddy bear (RoboTed), and later with an actual smart robot toySmart Robot Toy (Purrble). Through these two case studies this paper demonstrates the value of ERA and how consideration of ethical risks can prompt design changes, resulting in more ethical and sustainable robotsRobots.

There is evidence that humans understand how the world goes through narrative. We discuss what it might mean for embodied robots to understand the world, and communicate that understanding, in a similar manner. We suggest an architecture for adding narrative to robot cognition, and an experimental scenario for investigating the narrative hypothesis in a combination of physical and simulated robots

This work explored the use of human–robot interaction research to investigate robot ethics. A longitudinal human–robot interaction study was conducted with self-reported healthy older adults to determine whether expression of artificial emotions by a social robot could result in emotional deception and emotional attachment. The findings from this study have highlighted that currently there appears to be no adequate tools, or the means, to determine the ethical impact and concerns ensuing from long-term interactions between social robots and older adults. This raises the question whether we should continue the fundamental development of social robots if we cannot determine their potential negative impact and whether we should shift our focus to the development of human–robot interaction assessment tools that provide more objective measures of ethical impact.

This paper presents a series of experiments in collective social robotics, spanning more than 10 years, with the long-term aim of building embodied models of (aspects of) cultural evolution. Initial experiments demonstrated the emergence of behavioural traditions in a group of social robots programmed to imitate each other’s behaviours (we call these Copybots). These experiments show that the noisy (i.e. less than perfect fidelity) imitation that comes for free with real physical robots gives rise naturally to variation in social learning. More recent experimental work extends the robots’ cognitive capabilities with simulation-based internal models, equipping them with a simple artificial theory of mind. With this extended capability we explore, in our current work, social learning not via imitation but robot–robot storytelling, in an effort to model this very human mode of cultural transmission. In this paper, we give an account of the methods and inspiration for these experiments, the experiments and their results, and an outline of possible directions for this programme of research. It is our hope that this paper stimulates not only discussion but suggestions for hypotheses to test with the Storybots. This article is part of a discussion meeting issue ‘The emergence of collective knowledge and cumulative culture in animals, humans and machines’.

This paper describes IEEE P7001, a new draft standard on transparency of autonomous systems 1 . In the paper, we outline the development and structure of the draft standard. We present the rationale for transparency as a measurable, testable property. We outline five stakeholder groups: users, the general public and bystanders, safety certification agencies, incident/accident investigators and lawyers/expert witnesses, and explain the thinking behind the normative definitions of “levels” of transparency for each stakeholder group in P7001. The paper illustrates the application of P7001 through worked examples of both specification and assessment of fictional autonomous systems.