Decision shaping and strategy learning in multi-robot interactions

Recent developments in robot technology have contributed to the advancement of autonomous behaviours in human-robot systems; for example, in following instructions received from an interacting human partner. Nevertheless, increasingly many systems are moving towards more seamless forms of interaction, where factors such as implicit trust and persuasion between humans and robots are brought to the fore. In this context, the problem of attaining, through suitable computational models and algorithms, more complex strategic behaviours that can influence human decisions and actions during an interaction, remains largely open. To address this issue, this thesis introduces the problem of decision shaping in strategic interactions between humans and robots, where a robot seeks to lead, without however forcing, an interacting human partner to a particular state. Our approach to this problem is based on a combination of statistical modeling and synthesis of demonstrated behaviours, which enables robots to efficiently adapt to novel interacting agents. We primarily focus on interactions between autonomous and teleoperated (i.e. human-controlled) NAO humanoid robots, using the adversarial soccer penalty shooting game as an illustrative example. We begin by describing the various challenges that a robot operating in such complex interactive environments is likely to face. Then, we introduce a procedure through which composable strategy templates can be learned from provided human demonstrations of interactive behaviours. We subsequently present our primary contribution to the shaping problem, a Bayesian learning framework that empirically models and predicts the responses of an interacting agent, and computes action strategies that are likely to influence that agent towards a desired goal. We then address the related issue of factors affecting human decisions in these interactive strategic environments, such as the availability of perceptual information for the human operator. Finally, we describe an information processing algorithm, based on the Orient motion capture platform, which serves to facilitate direct (as opposed to teleoperation-mediated) strategic interactions between humans and robots. Our experiments introduce and evaluate a wide range of novel autonomous behaviours, where robots are shown to (learn to) influence a variety of interacting agents, ranging from other simple autonomous agents, to robots controlled by experienced human subjects. These results demonstrate the benefits of strategic reasoning in human-robot interaction, and constitute an important step towards realistic, practical applications, where robots are expected to be not just passive agents, but active, influencing participants

Continuous Authentication of Users to Robotic Technologies Using Behavioural Biometrics

Collaborative robots and current human–robot interaction systems, such as exoskeletons and teleoperation, are key technologies with profiles that make them likely security targets. Without sufficient protection, these robotics technologies might become dangerous tools that are capable of causing damage to their environments, increasing defects in work pieces and harming human co-workers. As robotics is a critical component of the current automation drive in many advanced economies, there may be serious economic effects if robot security is not appropriately handled. The development of suitable security for robots, particularly in industrial contexts, is critical. Collaborative robots, exoskeletons and teleoperation are all examples of robotics technologies that might need close collaboration with humans, and these interactions must be appropriately protected. There is a need to guard against both external hackers (as with many industrial systems) and insider malfeasance. Only authorised users should be able to access robots, and they should use only those services and capabilities they are qualified to access (e.g. those for which they are appropriately cleared and trained). Authentication is therefore a crucial enabling mechanism. Robot interaction will largely be ongoing, so continuous rather than one-time authentication is required. In robot contexts, continuous biometrics can be used to provide effective and practical authentication of individuals to robots. In particular, the working behaviour of human co-workers as they interact with robots can be used as a means of biometric authentication. This thesis demonstrates how continuous biometric authentication can be used in three different environments: a direct physical manipulation application, a sensor glove application and a remote access application. We show how information acquired from the collaborative robot's internal sensors, wearable sensors (similar to those found in an exoskeleton), and teleoperated robot control and programming can be harnessed to provide appropriate authentication. Thus, all authentication uses data that are collected or generated as part of the co-worker simply going about their work. No additional action is needed. For manufacturing environments, this lack of intrusiveness is an important feature. The results presented in this thesis show that our approaches can discriminate appropriately between users. We believe that our machine learning-based approaches can provide reasonable and practical solutions for continually authenticating users to robots in many environments, particularly in manufacturing contexts

A hand exoskeleton with series elastic actuation for rehabilitation : design, control and experimentation

Rehabilitation of the hands is critical for restoring independence in activities of daily living for individuals with upper extremity disabilities. Conventional therapies for hand rehabilitation have not shown significant improvement in hand function. Robotic exoskeletons have been developed to assist in therapy and there is initial evidence that such devices with force-control based strategies can help in effective rehabilitation of human limbs. However, to the best of our knowledge, none of the existing hand exoskeletons allow for accurate force or torque control. In this dissertation, we design and prototype a novel hand exoskeleton that has the following unique features: (i) Bowden-cable-based series elastic actuation allowing for bidirectional torque control of each joint individually, (ii) an underlying kinematic mechanism that is optimized to achieve large range of motion and (iii) a thumb module that allows for independent actuation of the four thumb joints. To control the developed hand exoskeleton for efficacious rehabilitation after a neuromuscular impairment such as stroke, we present two types of subject-specific assist-as-needed controllers. Learned force-field control is a novel control technique in which a neural-network-based model of the required torques given the joint angles for a specific subject is learned and then used to build a force-field to assist the joint motion of the subject to follow a trajectory designed in the joint-angle space. Adaptive assist-as-needed control, on the other hand, estimates the coupled digit-exoskeleton system torque requirement of a subject using radial basis function (RBF) and on-the-y adapts the RBF magnitudes to provide a feed-forward assistance for improved trajectory tracking. Experiments with healthy human subjects showed that each controller has its own trade-offs and is suitable for a specific type of impairment. Finally, to promote and optimize motor (re)-learning, we present a framework for robot-assisted motor (re)-learning that provides subject-specific training by allowing for simultaneous adaptation of task, assistance and feedback based on the performance of the subject on the task. To train the subjects for dexterous manipulation, we present a torque-based task that requires subjects to dynamically regulate their joint torques. A pilot study carried out with healthy human subjects using the developed hand exoskeleton suggests that training under simultaneous adaptation of task, assistance and feedback can module challenge and affect their motor learning.Mechanical Engineerin