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.

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.

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.

As autonomous robots move towards ubiquity, the need for robots to make decisions under risk that are trustworthy becomes increasingly significant; both to aid acceptance and to fully utilise their autonomous capabilities. We propose that incorporating a human approach to risk assessment into a robot's decision making process will increase user trust. This work investigates four robotic approaches to risk and, through a user study, explores the levels of trust placed in each. These approaches are: risk averse, risk seeking, risk neutral and a human approach to risk. Risk is artificially stimulated through performance-based compensation, in line with previous studies. The study was conducted in a virtual nuclear environment created using the Unity games engine. Forty participants were asked to complete a robot supervision task, in which they observed a robot making risk based decisions and were able to question the robot, question the robot further and ultimately accept or alter the robot's decision. It is shown that a robot that is risk seeking is significantly less trusted than a risk averse robot, a risk neutral robot and a robot utilising human approach to risk. There was found to be no significant difference between the levels of trust placed in the risk averse, risk neutral and human approach to risk. It is also found that the level to which participants question a robot's decisions does not form an accurate measure of trust. The results suggest that when designing a robot that must make risk based decisions during teleoperation in a hazardous environment, an engineer should avoid a risk seeking robot. However, that same engineer may choose whichever of the remaining risk profiles best suits the implementation, with knowledge that the trust in their system is unlikely to be significantly affected.

Previous work on ethical machine reasoning has largely been theoretical, and where such systems have been implemented, it has, in general, been only initial proofs of principle. Here, we address the question of desirable attributes for such systems to improve their real world utility, and how controllers with these attributes might be implemented. We propose that ethically critical machine reasoning should be proactive, transparent, and verifiable. We describe an architecture where the ethical reasoning is handled by a separate layer, augmenting a typical layered control architecture, ethically moderating the robot actions. It makes use of a simulation-based internal model and supports proactive, transparent, and verifiable ethical reasoning. To do so, the reasoning component of the ethical layer uses our Python-based belief-desire-intention (BDI) implementation. The declarative logic structure of BDI facilitates both transparency, through logging of the reasoning cycle, and formal verification methods. To prove the principles of our approach, we use a case study implementation to experimentally demonstrate its operation. Importantly, it is the first such robot controller where the ethical machine reasoning has been formally verified.

The so-called fourth industrial revolution and its economic and societal implications are no longer solely an academic concern, but a matter for political as well as public debate. Characterized as the convergence of robotics, AI, autonomous systems and information technology – or cyberphysical systems – the fourth industrial revolution was the focus of the World Economic Forum, at Davos, in 2016[1]. Also in 2016 the US White House initiated a series of public workshops on artificial intelligence (AI) and the creation of an interagency working group, and the European Parliament Committee for Legal Affairs published a draft report with recommendations to the Commission on Civil Law Rules on Robotics.

Concerns over the risks associated with advances in Artificial Intelligence have prompted calls for greater efforts toward robust and beneficial AI, including machine ethics. Recently, roboticists have responded by initiating the development of so-called ethical robots. These robots would, ideally, evaluate the consequences of their actions and morally justify their choices. This emerging field promises to develop extensively over the next few years. However, in this paper, we point out an inherent limitation of the emerging field of ethical robots. We show that building ethical robots also inevitably enables the construction of unethical robots. In three experiments, we show that it is remarkably easy to modify an ethical robot so that it behaves competitively, or even aggressively. The reason for this is that the cognitive machinery required to make an ethical robot can always be corrupted to make unethical robots. We discuss the implications of this finding to the governance of ethical robots. We conclude that the risks that unscrupulous actors might compromise a robot's ethics are so great as to raise serious doubts over the wisdom of embedding ethical decision making in real-world safety-critical robots, such as driverless cars.

The evidence available to a multi-agent system can take at least two distinct forms. There can be direct evidence from the environment resulting, for example, from sensor measurements or from running tests or experiments. In addition, agents also gain evidence from other individuals in the population with whom they are interacting. We, therefore, envisage an agent's beliefs as a probability distribution over a set of hypotheses of interest, which are updated either on the basis of direct evidence using Bayesian updating, or by taking account of the probabilities of other agents using opinion pooling. This paper investigates the relationship between these two processes in a multi-agent setting. We consider a possible Bayesian interpretation of probability pooling and then explore properties for pooling operators governing the extent to which direct evidence is diluted, preserved or amplified by the pooling process. We then use simulation experiments to show that pooling operators can provide a mechanism by which a limited amount of direct evidence can be efficiently propagated through a population of agents so that an appropriate consensus is reached. In particular, we explore the convergence properties of a parameterised family of operators with a range of evidence propagation strengths.

We compare the ability of two different robot controllers for collective foraging to cope with noise in robot global positioning data and show how recruitment, in the form of broadcast messages near worksites, can make swarms more robust. Swarms of five e-puck robots are used in a semi-virtual environment, facilitated by the VICON positioning system. This setup allows us to control the amount of noise in the robot positioning data and to generate pseudo-random environments, while retaining important physical aspects of the experiment. The effect of inherent noise in the robot infra-red sensors, used for obstacle avoidance, is noted and the importance of modelling such noise in agent-based simulations is highlighted.

This paper proposes a set of five ethical principles, together with seven high-level messages, as a basis for responsible robotics. The Principles of Robotics were drafted in 2010 and published online in 2011. Since then the principles have influenced, and continue to influence, a number of initiatives in robot ethics but have not, to date, been formally published. This paper remedies that omission.

This article describes research in which embodied imitation and behavioral adaptation are investigated in collective robotics. We model social learning in artificial agents with real robots. The robots are able to observe and learn each others' movement patterns using their on-board sensors only, so that imitation is embodied. We show that the variations that arise from embodiment allow certain behaviors that are better adapted to the process of imitation to emerge and evolve during multiple cycles of imitation. As these behaviors are more robust to uncertainties in the real robots' sensors and actuators, they can be learned by other members of the collective with higher fidelity. Three different types of learned-behavior memory have been experimentally tested to investigate the effect of memory capacity on the evolution of movement patterns, and results show that as the movement patterns evolve through multiple cycles of imitation, selection, and variation, the robots are able to, in a sense, agree on the structure of the behaviors that are imitated.

This short discussion paper sets out to explore the question: what is the energy cost of evolving complex artificial life? The paper takes an unconventional approach by first estimating the energy cost of natural evolution and, in particular, the species Homo Sapiens Sapiens. The paper argues that such an estimate has value because it forces us to think about the energy costs of co-evolution, and hence the energy costs of evolving complexity. Furthermore, an analysis of the real energy costs of evolving virtual creatures in a virtual environment, leads the paper to suggest an artificial life equivalent of Kleiber's law - relating neural and synaptic complexity (instead of mass) to computational energy cost (instead of real energy consumption). An underlying motivation for this paper is to counter the view that artificial evolution will facilitate the technological singularity, by arguing that the energy costs are likely to be prohibitively high. The paper concludes by arguing that the huge energy cost is not the only problem. In addition we will require a new approach to artificial evolution in which we construct complex scaffolds of co-evolving artificial creatures and ecosystems.

This paper presents a novel approach to the run-time detection of faults in autonomous mobile robots, based on simulated predictions of real robot behaviour. We show that although simulation can be used to predict real robot behaviour, drift between simulation and reality occurs over time due to the reality gap. This necessitates periodic reinitialisation of the simulation to reduce false positives. Using a simple obstacle avoidance controller afflicted with partial motor failure, we show that selecting the length of this reinitialisation time period is non-trivial, and that there exists a trade-off between minimising drift and the ability to detect the presence of faults.

Imitation, in which an individual observes and copies another's actions, is a powerful means of learning. This paper presents a way of using imitation to enhance the learning capability of individual agents. The agents employ Q-learning and we show that agents with imitation enhanced Q-learning learn faster than those with Q-learning alone.

Embodied fitness assessment of robotic controllers is slow but grounded, while assessment in a simulated environment is fast but can run foul of the `reality gap'. We present a distributed co-evolutionary method to adapt the environmental model of an on-board simulator within the context of swarm robotics.

In swarm robotic systems emergent swarm properties are particularly difficult to analyse and model. This paper describes a simple but effective algorithm for emergent swarm taxis (swarm motion toward a beacon) in a 2D or 3D wireless connected swarm of minimalist mobile robots. The paper then undertakes a deep analysis of the swarm taxis by identifying both first and second order micro-level robot interactions and quantifying the contribution of each such interaction to the macro-level swarm behaviour. From the analysis we develop a simple quantitative model that is able to predict swarm velocity with reasonable accuracy. Although the analysis is specific to the swarm algorithm in question, we believe that the methodology presented has generic value to swarm modellers.

Initiated by N. Reeves, the Mascarillons project stands at a crossroad between Art and Science. It aims to bring together researchers in both artistic and scientific domains to collaborate towards the production of a robotic environment dedicated to architectural research, with a major potential for multi-media performance. Because of the tight constraints of the project, a multi-level design methodology is proposed, consisting of the parallel development of real robots and increasingly abstract modeling tools. The interaction of these experimental levels is expected to hasten the progress towards an efficient solution by taking every technological and physical constraint into account.

In this paper we propose a new procedure for construction of motor schema typically used in behaviour-based robotics. The procedure reverses the standard stability analysis approach by searching for a control function to fit a pre-defined Lyapunov function. In order to improve the applicability of this procedure, a new second-order extension to Lyapunov's second method is proposed, allowing a stable schema to be defined directly in terms of actuator force demands. We propose a synthesis procedure called direct Lyapunov design, which searches for motor schema maps whose set points satisfy the second-order theorem. The procedure has been applied to a simple subsumption architecture controller for an inverted pendulum simulation, yielding stable behaviour.