This thesis was motivated by a study of how robots can be taught by humans, with an emphasis on allowing persons without programming skills to teach robots. The focus of this thesis was to investigate what criteria could or should be used by a robot to evaluate whether a human teacher is (or potentially could be) a good teacher in robot learning by demonstration. In effect, choosing the teacher that can maximize the benefit to the robot using learning by imitation/demonstration. The study approached this topic by taking a technology snapshot in time to see if a representative example of research laboratory robot technology is capable of assessing teaching quality. With this snapshot, this study evaluated how humans observe teaching quality to attempt to establish measurement metrics that can be transferred as rules or algorithms that are beneficial from a robot's point of view. To evaluate teaching quality, the study looked at the teacher-student relationship from a human-human interaction perspective. Two factors were considered important in defining a good teacher: engagement and immediacy. The study gathered more literature reviews relating to further detailed elements of engagement and immediacy. The study also tried to link physical effort as a possible metric that could be used to measure the level of engagement of the teachers. An investigatory experiment was conducted to evaluate which modality the participants prefer to employ in teaching a robot if the robot can be taught using voice, gesture demonstration, or physical manipulation. The findings from this experiment suggested that the participants appeared to have no preference in terms of human effort for completing the task. However, there was a significant difference in human enjoyment preferences of input modality and a marginal difference in the robot's perceived ability to imitate. A main experiment was conducted to study the detailed elements that might be used by a robot in identifying a 'good' teacher. The main experiment was conducted in two subexperiments. The first part recorded the teacher's activities and the second part analysed how humans evaluate the perception of engagement when assessing another human teaching a robot. The results from the main experiment suggested that in human teaching of a robot (human-robot interaction), humans (the evaluators) also look for some immediacy cues that happen in human-human interaction for evaluating the engagement.

abstract: Myoelectric control is lled with potential to signicantly change human-robot interaction. Humans desire compliant robots to safely interact in dynamic environments associated with daily activities. As surface electromyography non-invasively measures limb motion intent and correlates with joint stiness during co-contractions, it has been identied as a candidate for naturally controlling such robots. However, state-of-the-art myoelectric interfaces have struggled to achieve both enhanced functionality and long-term reliability. As demands in myoelectric interfaces trend toward simultaneous and proportional control of compliant robots, robust processing of multi-muscle coordinations, or synergies, plays a larger role in the success of the control scheme. This dissertation presents a framework enhancing the utility of myoelectric interfaces by exploiting motor skill learning and exible muscle synergies for reliable long-term simultaneous and proportional control of multifunctional compliant robots. The interface is learned as a new motor skill specic to the controller, providing long-term performance enhancements without requiring any retraining or recalibration of the system. Moreover, the framework oers control of both motion and stiness simultaneously for intuitive and compliant human-robot interaction. The framework is validated through a series of experiments characterizing motor learning properties and demonstrating control capabilities not seen previously in the literature. The results validate the approach as a viable option to remove the trade-o between functionality and reliability that have hindered state-of-the-art myoelectric interfaces. Thus, this research contributes to the expansion and enhancement of myoelectric controlled applications beyond commonly perceived anthropomorphic and \intuitive control" constraints and into more advanced robotic systems designed for everyday tasks. / Dissertation/Thesis / Doctoral Dissertation Mechanical Engineering 2015

abstract: While developing autonomous intelligent robots has been the goal of many research programs, a more practical application involving intelligent robots is the formation of teams consisting of both humans and robots. An example of such an application is search and rescue operations where robots commanded by humans are sent to environments too dangerous for humans. For such human-robot interaction, natural language is considered a good communication medium as it allows humans with less training about the robot's internal language to be able to command and interact with the robot. However, any natural language communication from the human needs to be translated to a formal language that the robot can understand. Similarly, before the robot can communicate (in natural language) with the human, it needs to formulate its communique in some formal language which then gets translated into natural language. In this paper, I develop a high level language for communication between humans and robots and demonstrate various aspects through a robotics simulation. These language constructs borrow some ideas from action execution languages and are grounded with respect to simulated human-robot interaction transcripts. / Dissertation/Thesis / M.S. Computer Science 2012

abstract: Linear Temporal Logic is gaining increasing popularity as a high level specification language for robot motion planning due to its expressive power and scalability of LTL control synthesis algorithms. This formalism, however, requires expert knowledge and makes it inaccessible to non-expert users. This thesis introduces a graphical specification environment to create high level motion plans to control robots in the field by converting a visual representation of the motion/task plan into a Linear Temporal Logic (LTL) specification. The visual interface is built on the Android tablet platform and provides functionality to create task plans through a set of well defined gestures and on screen controls. It uses the notion of waypoints to quickly and efficiently describe the motion plan and enables a variety of complex Linear Temporal Logic specifications to be described succinctly and intuitively by the user without the need for the knowledge and understanding of LTL specification. Thus, it opens avenues for its use by personnel in military, warehouse management, and search and rescue missions. This thesis describes the construction of LTL for various scenarios used for robot navigation using the visual interface developed and leverages the use of existing LTL based motion planners to carry out the task plan by a robot. / Dissertation/Thesis / M.S. Computer Science 2013

abstract: As robots are increasingly migrating out of factories and research laboratories and into our everyday lives, they should move and act in environments designed for humans. For this reason, the need of anthropomorphic movements is of utmost importance. The objective of this thesis is to solve the inverse kinematics problem of redundant robot arms that results to anthropomorphic configurations. The swivel angle of the elbow was used as a human arm motion parameter for the robot arm to mimic. The swivel angle is defined as the rotation angle of the plane defined by the upper and lower arm around a virtual axis that connects the shoulder and wrist joints. Using kinematic data recorded from human subjects during every-day life tasks, the linear sensorimotor transformation model was validated and used to estimate the swivel angle, given the desired end-effector position. Defining the desired swivel angle simplifies the kinematic redundancy of the robot arm. The proposed method was tested with an anthropomorphic redundant robot arm and the computed motion profiles were compared to the ones of the human subjects. This thesis shows that the method computes anthropomorphic configurations for the robot arm, even if the robot arm has different link lengths than the human arm and starts its motion at random configurations. / Dissertation/Thesis / M.S.Tech Mechanical Engineering 2013

This thesis is a critique on the ease in which humans tend to anthropomorphize chatbots, assigning human characteristics to entities that fundamentally will never understand the human experience. It will be further exploring these consequences on our society's socio-cultural fabric, representations of the self and identity formation in terms of communication and the essence of humanity.

Context: Patients with diseases such as locked in syndrome or motor neuron are paralyzed and they need special care. To reduce the cost of their care, systems need to be designed where human involvement is minimal and affected people can perform their daily life activities independently. To assess the feasibility and robustness of combinations of input modalities, mobile robot (Spinosaurus) navigation is controlled by a combination of Eye gaze tracking and other input modalities. Objectives: Our aim is to control the robot using EEG brain signals and eye gaze tracking simultaneously. Different combinations of input modalities are used to control the robot and turret movement and then we find out which combination of control technique mapped to control command is most effective. Methods: The method includes developing the interface and control software. An experiment involving 15 participants was conducted to evaluate control of the mobile robot using a combination of eye tracker and other input modalities. Subjects were required to drive the mobile robot from a starting point to a goal along a pre-defined path. At the end of experiment, a sense of presence questionnaire was distributed among the participants to take their feedback. A qualitative pilot study was performed to find out how a low cost commercial EEG headset, the Emotiv EPOCTM, can be used for motion control of a mobile robot at the end. Results: Our study results showed that the Mouse/Keyboard combination was the most effective for controlling the robot motion and turret mounted camera respectively. In experimental evaluation, the Keyboard/Eye Tracker combination improved the performance by 9%. 86% of participants found that turret mounted camera was useful and provided great assistance in robot navigation. Our qualitative pilot study of the Emotiv EPOCTM demonstrated different ways to train the headset for different actions. Conclusions: In this study, we concluded that different combinations of control techniques could be used to control the devices e.g. a mobile robot or a powered wheelchair. Gaze-based control was found to be comparable with the use of a mouse and keyboard; EEG-based control was found to need a lot of training time and was difficult to train. Our pilot study suggested that using facial expressions to train the Emotiv EPOCTM was an efficient and effective way to train it.

Face-to-face human communication is a multimodal and incremental process. Humans employ  different information channels (modalities) for their communication. Since some of these modalities are more error-prone to specic type of data, a multimodal communication can benefit from strengths of each modality and therefore reduce ambiguities during the interaction. Such interfaces can be applied to intelligent robots who operate in close relation with humans. With this approach, robots can communicate with their human colleagues in the same way they communicate with each other, thus leading to an easier and more robust human-robot interaction (HRI).In this work we suggest a new method for implementing multimodal interfaces in HRI domain and present the method employed on an industrial robot. We show that operating the system is made easier by using this interface. / Robot Colleague

As people respond strongly to faces and facial features, both consciously and subconsciously, faces are an essential aspect of social robots. Robotic faces and heads until recently belonged to one of the following categories: virtual, mechatronic or animatronic. As an original contribution to the field of human-robot interaction, I present the R-PAF technology (Retro-Projected Animated Faces): a novel robotic head displaying a real-time, computer-rendered face, retro-projected from within the head volume onto a mask, as well as its driving software designed with openness and portability to other hybrid robotic platforms in mind. The work constitutes the first implementation of a non-planar mask suitable for social human-robot interaction, comprising key elements of social interaction such as precise gaze direction control, facial expressions and blushing, and the first demonstration of an interactive video-animated facial mask mounted on a 5-axis robotic arm. The LightHead robot, a R-PAF demonstrator and experimental platform, has demonstrated robustness both in extended controlled and uncontrolled settings. The iterative hardware and facial design, details of the three-layered software architecture and tools, the implementation of life-like facial behaviours, as well as improvements in social-emotional robotic communication are reported. Furthermore, a series of evaluations present the first study on human performance in reading robotic gaze and another first on user’s ethnic preference towards a robot face.

Industrial robots have been identified as one of the most effective solutions for optimising output and quality within many industries. However, there are a number of manufacturing applications involving complex tasks and inconstant components which prohibit the use of fully automated solutions in the foreseeable future. A breakthrough in robotic technologies and changes in safety legislations have supported the creation of robots that coexist and assist humans in industrial applications. It has been broadly recognised that human-robot collaborative systems would be a realistic solution as an advanced production system with wide range of applications and high economic impact. This type of system can utilise the best of both worlds, where the robot can perform simple tasks that require high repeatability while the human performs tasks that require judgement and dexterity of the human hands. Robots in such system will operate as “intelligent assistants”. In a collaborative working environment, robot and human share the same working area, and interact with each other. This level of interface will require effective ways of communication and collaboration to avoid unwanted conflicts. This project aims to create a user interface for industrial collaborative robot system through integration of current robotic technologies. The robotic system is designed for seamless collaboration with a human in close proximity. The system is capable to communicate with the human via the exchange of gestures, as well as visual signal which operators can observe and comprehend at a glance. The main objective of this PhD is to develop a Human-Robot Interface (HRI) for communication with an industrial collaborative robot during collaboration in proximity. The system is developed in conjunction with a small scale collaborative robot system which has been integrated using off-the-shelf components. The system should be capable of receiving input from the human user via an intuitive method as well as indicating its status to the user ii effectively. The HRI will be developed using a combination of hardware integrations and software developments. The software and the control framework were developed in a way that is applicable to other industrial robots in the future. The developed gesture command system is demonstrated on a heavy duty industrial robot.