Types of verbal interaction with instructable robots

An instructable robot is one that accepts instruction in some natural language such as English and uses that instruction to extend its basic repertoire of actions. Such robots are quite different in conception from autonomously intelligent robots, which provide the impetus for much of the research on inference and planning in artificial intelligence. Examined here are the significant problem areas in the design of robots that learn from vebal instruction. Examples are drawn primarily from our earlier work on instructable robots and recent work on the Robotic Aid for the physically disabled. Natural-language understanding by machines is discussed as well as in the possibilities and limits of verbal instruction. The core problem of verbal instruction, namely, how to achieve specific concrete action in the robot in response to commands that express general intentions, is considered, as are two major challenges to instructability: achieving appropriate real-time behavior in the robot, and extending the robot's language capabilities

Interactive Imitation Learning in Robotics: A Survey

Interactive Imitation Learning (IIL) is a branch of Imitation Learning (IL) where human feedback is provided intermittently during robot execution allowing an online improvement of the robot's behavior. In recent years, IIL has increasingly started to carve out its own space as a promising data-driven alternative for solving complex robotic tasks. The advantages of IIL are its data-efficient, as the human feedback guides the robot directly towards an improved behavior, and its robustness, as the distribution mismatch between the teacher and learner trajectories is minimized by providing feedback directly over the learner's trajectories. Nevertheless, despite the opportunities that IIL presents, its terminology, structure, and applicability are not clear nor unified in the literature, slowing down its development and, therefore, the research of innovative formulations and discoveries. In this article, we attempt to facilitate research in IIL and lower entry barriers for new practitioners by providing a survey of the field that unifies and structures it. In addition, we aim to raise awareness of its potential, what has been accomplished and what are still open research questions. We organize the most relevant works in IIL in terms of human-robot interaction (i.e., types of feedback), interfaces (i.e., means of providing feedback), learning (i.e., models learned from feedback and function approximators), user experience (i.e., human perception about the learning process), applications, and benchmarks. Furthermore, we analyze similarities and differences between IIL and RL, providing a discussion on how the concepts offline, online, off-policy and on-policy learning should be transferred to IIL from the RL literature. We particularly focus on robotic applications in the real world and discuss their implications, limitations, and promising future areas of research

Effects of timing on users' perceived control when interacting with intelligent systems

This research relates to the usability of mixed-initiative interaction systems, in which actions can be initiated either through a choice by the user or through intelligent decisions taken by the system. The key issue addressed here is how to preserve the user's perceived control ("sense of agency'') when the control of the interaction is being transferred between the system and the user in a back-and-forth manner. Previous research in social psychology and cognitive neuroscience suggests timing is a factor that can influence perceived control in such back-and-forth interactions. This dissertation explores the hypothesis that in mixed-initiative interaction, a predictable interaction rhythm can preserve the user's sense of control and enhance their experience during a task (e.g. higher confidence in task performance, stronger temporal alignment, lower perceived levels of stress and effort), whereas irregular interaction timing can have the opposite effect. Three controlled experiments compare alternative rhythmic strategies when users interact with simple visual stimuli, simple auditory stimuli, and a more realistic assisted text labelling task. The results of all three experiments support the hypothesis that a predictable interaction rhythm is beneficial in a range of interaction modalities and applications. This research contributes to the field of human-computer interaction (HCI) in four ways. Firstly, it builds novel connections between existing theories in cognitive neuroscience, social psychology and HCI, highlighting how rhythmic temporal structures can be beneficial to the user's experience: particularly, their sense of control. Secondly, it establishes timing as a crucial design resource for mixed-initiative interaction, and provides empirical evidence of how the user's perceived control and other task experiences (such as reported levels of confidence, stress and effort) can be influenced by the manipulation of timing. Thirdly, it provides quantitative measures for the user's entrainment behaviours that are applicable to a wide range of interaction timescales. Lastly, it contextualises the design of timing in a realistic application scenario and offers insights to the design of general end-user automation and decision support tools.Cambridge Commonwealth European and International Trust Scholarship Cambridge Philosophical Society research studentship Gonville & Caius College hardship grant China Scholarship Council (CSC) (The agreement with CSC has been officially cancelled on 28 December 2017.

Space human factors discipline science plan

The purpose of this Discipline Science Plan is to provide a conceptual strategy for NASA's Life Sciences Division research and development activities in the comprehensive areas of behavior, performance, and human factors. This document summarizes the current status of the program, outlines available knowledge, establishes goals and objectives, defines critical questions in the subdiscipline areas, and identifies technological priorities. It covers the significant research areas critical to NASA's programmatic requirements for the Extended Duration Orbiter, Space Station Freedom, and Exploration mission science activities. These science activities include ground-based and flight; basic, applied and operational; and animal and human research and development. This document contains a general plan that will be used by both NASA Headquarters program offices and the field centers to review and plan basic, applied, and operational research and development activities, both intramural and extramural, in this area

Aerospace medicine and biology: A cumulative index to a continuing bibliography (supplement 371)

This publication is a cumulative index to the abstracts contained in Supplements 359 through 370 of Aerospace Medicine and Biology: A Continuing Bibliography. It includes seven indexes: subject, personal author, corporate source, foreign technology, contract number, report number, and accession number

Reinforcement Learning Approaches in Social Robotics

This article surveys reinforcement learning approaches in social robotics. Reinforcement learning is a framework for decision-making problems in which an agent interacts through trial-and-error with its environment to discover an optimal behavior. Since interaction is a key component in both reinforcement learning and social robotics, it can be a well-suited approach for real-world interactions with physically embodied social robots. The scope of the paper is focused particularly on studies that include social physical robots and real-world human-robot interactions with users. We present a thorough analysis of reinforcement learning approaches in social robotics. In addition to a survey, we categorize existent reinforcement learning approaches based on the used method and the design of the reward mechanisms. Moreover, since communication capability is a prominent feature of social robots, we discuss and group the papers based on the communication medium used for reward formulation. Considering the importance of designing the reward function, we also provide a categorization of the papers based on the nature of the reward. This categorization includes three major themes: interactive reinforcement learning, intrinsically motivated methods, and task performance-driven methods. The benefits and challenges of reinforcement learning in social robotics, evaluation methods of the papers regarding whether or not they use subjective and algorithmic measures, a discussion in the view of real-world reinforcement learning challenges and proposed solutions, the points that remain to be explored, including the approaches that have thus far received less attention is also given in the paper. Thus, this paper aims to become a starting point for researchers interested in using and applying reinforcement learning methods in this particular research field

Expert-informed design and automation of persuasive, socially assistive robots

Socially assistive robots primarily provide useful functionality through their social interactions with user(s). An example application, used to ground work throughout this thesis, is using a social robot to guide users through exercise sessions. Initial works have demonstrated that interactions with a social robot can improve engagement with exercise, and that an embodied social robot is more effective for this than the equivalent virtual avatar. However, many questions remain regarding the design and automation of socially assistive robot behaviours for this purpose. This thesis identifies and practically works through a number of these questions in pursuit of one ultimate goal: the meaningful, real world deployment of a fully autonomous, socially assistive robot. The work takes an expert informed approach, looking to learn from human experts in socially assistive interactions and explore how their expert knowledge can be reflected in the design and automation of social robot behaviours. It is taking this approach that leads to the notion of socially assistive robots needing to be persuasive in order to be effective, but also identifies the difficulty in automating such complex, socially intelligent behaviour. The ethical implications of designing persuasive robot behaviours are also practically considered; with reference to a published standard on ethical robot design. The work culminates with use of a state of the art, interactive machine learning approach to have an expert fitness instructor train a robot ‘fitness coach’, deployed in a university gym, as it guides participants through an NHS exercise programme. After a total of 151 training sessions across 10 participants, the robot successfully ran 32 sessions autonomously. The results demonstrated that autonomous behaviour was generally comparable to that of the robot when controlled/supervised by the fitness instructor, and that overall, the robot played an important role in keeping participants motivated through the exercise programme