Resolving Perception Based Problems in Human-Computer Dialogue

We investigate the effect of sensor errors on situated human­ computer dialogues. If a human user instructs a robot to perform a task in a spatial environment, errors in the robot\u27s sensor based perception of the environment may result in divergences between the user\u27s and the robot\u27s understanding of the environment. If the user and the robot communicate through a language based interface, these problems may result in complex misunderstand­ ings. In this work we investigate such situations. We set up a simulation based scenario in which a human user instructs a robot to perform a series of manipulation tasks, such as lifting, moving and re-arranging simple objects. We induce errors into the robot\u27s perception, such as misclassification of shapes and colours, and record and analyse the user\u27s attempts to resolve the problems. We evaluate a set of methods to alleviate the problems by allowing the operator to access the robot\u27s understanding of the scene. We investigate a uni-directional language based option, which is based on automatically generated scene descriptions, a visually based option, in which the system highlights objects and provides known properties, and a dialogue based assistance option. In this option the participant can a.sk simple questions about the robot\u27s perception of the scene. As a baseline condition we perform the experiment without introducing any errors. We evaluate and compare the success and problems in all four conditions. We identify and compare strategies the participants used in each condition. We find that the participants appreciate and use the information request options successfully. We find that that all options provide an improvement over the condition without information. We conclude that allowing the participants to access information about the robot\u27s perception state is an effective way to resolve problems in the dialogue

The Role of Perception in Situated Spatial Reference

This position paper set out the argument that an interesting avenue of exploration and study of universals and variation in spatial reference is to address this topic in termsa of the universals in human perception and attention and to explore how these universals impact on spatial reference across cultures and languages

Learning how to learn: an adaptive dialogue agent for incrementally learning visually grounded word meanings

We present an optimised multi-modal dialogue agent for interactive learning of visually grounded word meanings from a human tutor, trained on real human-human tutoring data. Within a life-long interactive learning period, the agent, trained using Reinforcement Learning (RL), must be able to handle natural conversations with human users and achieve good learning performance (accuracy) while minimising human effort in the learning process. We train and evaluate this system in interaction with a simulated human tutor, which is built on the BURCHAK corpus -- a Human-Human Dialogue dataset for the visual learning task. The results show that: 1) The learned policy can coherently interact with the simulated user to achieve the goal of the task (i.e. learning visual attributes of objects, e.g. colour and shape); and 2) it finds a better trade-off between classifier accuracy and tutoring costs than hand-crafted rule-based policies, including ones with dynamic policies.Comment: 10 pages, RoboNLP Workshop from ACL Conferenc

Gender differences in navigation dialogues with computer systems

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Gender is among the most influential of the factors underlying differences in spatial abilities, human communication and interactions with and through computers. Past research has offered important insights into gender differences in navigation and language use. Yet, given the multidimensionality of these domains, many issues remain contentious while others unexplored. Moreover, having been derived from non-interactive, and often artificial, studies, the generalisability of this research to interactive contexts of use, particularly in the practical domain of Human-Computer Interaction (HCI), may be problematic. At the same time, little is known about how gender strategies, behaviours and preferences interact with the features of technology in various domains of HCI, including collaborative systems and systems with natural language interfaces. Targeting these knowledge gaps, the thesis aims to address the central question of how gender differences emerge and operate in spatial navigation dialogues with computer systems. To this end, an empirical study is undertaken, in which, mixed-gender and same-gender pairs communicate to complete an urban navigation task, with one of the participants being under the impression that he/she interacts with a robot. Performance and dialogue data were collected using a custom system that supported synchronous navigation and communication between the user and the robot. Based on this empirical data, the thesis describes the key role of the interaction of gender in navigation performance and communication processes, which outweighed the effect of individual gender, moderating gender differences and reversing predicted patterns of performance and language use. This thesis has produced several contributions; theoretical, methodological and practical. From a theoretical perspective, it offers novel findings in gender differences in navigation and communication. The methodological contribution concerns the successful application of dialogue as a naturalistic, and yet experimentally sound, research paradigm to study gender and spatial language. The practical contributions include concrete design guidelines for natural language systems and implications for the development of gender-neutral interfaces in specific domains of HCI

Markerless Vision-Based Skeleton Tracking in Therapy of Gross Motor Skill Disorders in Children

This chapter presents a research towards implementation of a computer vision system for markerless skeleton tracking in therapy of gross motor skill disorders in children suffering from mild cognitive impairment. The proposed system is based on a low-cost 3D sensor and a skeleton tracking software. The envisioned architecture is scalable in the sense that the system may be used as a stand-alone assistive tool for tracking the effects of therapy or it may be integrated with an advanced autonomous conversational agent to maintain the spatial attention of the child and to increase her motivation to undergo a long-term therapy