Adaptive Human-Aware Robot Navigation in Close Proximity to Humans

For robots to be able coexist with people in future everyday human environments, they must be able to act in a safe, natural and comfortable way. This work addresses the motion of a mobile robot in an environment, where humans potentially want to interact with it. The designed system consists of three main components: a Kalman filter-based algorithm that derives a person's state information (position, velocity and orientation) relative to the robot; another algorithm that uses a Case-Based Reasoning approach to estimate if a person wants to interact with the robot; and, finally, a navigation system that uses a potential field to derive motion that respects the person's social zones and perceived interest in interaction. The operation of the system is evaluated in a controlled scenario in an open hall environment. It is demonstrated that the robot is able to learn to estimate if a person wishes to interact, and that the system is capable of adapting to changing behaviours of the humans in the environment

Spatially-Distributed Interactive Behaviour Generation for Architecture-Scale Systems Based on Reinforcement Learning

This thesis is part of the research activities of the Living Architecture System Group (LASG). LASG develops immersive, interactive art sculptures combining concepts of architecture, art, and electronics which allow occupants to interact with immersively. The primary goal of this research is to investigate the design of effective human-robot interaction behaviours using reinforcement learning. In this thesis, reinforcement learning is used adapt human designed behaviours to maximize occupant engagement. Algorithms were tested in a simulation environment created using Unity. The system developed by LASG was simulated and simplified human visitor models are designed for the tests. Three adaptive behaviour modes and two exploration methods were compared in the simulated environment. We showed that reinforcement learning algorithms can learn to increase engagement by adapting to visitors' preferences and exploring with parameter noise performed better than action noise because of wider exploration. A field study was conducted based on the LASG's installation Aegis, Transforming Space exhibition at the Royal Ontario Museum (ROM) from June 2nd to October 8th, 2018. The experiment was conducted in a natural setting where no constraints are imposed on visitors and group interaction is accommodated. Experimental results demonstrated that learning on top of human designed pre-scripted behaviours (PLA) is better at increasing visitors engagement than only using pre-scripted behaviours (PB). Visitor responses to the GodSpeed standardized questionnaire suggested that PLA is more highly rated than PB in terms of Likeability and interactivity

An ambient agent model for reading companion robot

Reading is essentially a problem-solving task. Based on what is read, like problem solving, it requires effort, planning, self-monitoring, strategy selection, and reflection. Also, as readers are trying to solve difficult problems, reading materials become more complex, thus demands more effort and challenges cognition. To address this issue, companion robots can be deployed to assist readers in solving difficult reading tasks by making reading process more enjoyable and meaningful. These robots require an ambient agent model, monitoring of a reader’s cognitive demand as it could consist of more complex tasks and dynamic interactions between human and environment. Current cognitive load models are not developed in a form to have reasoning qualities and not integrated into companion robots. Thus, this study has been conducted to develop an ambient agent model of cognitive load and reading performance to be integrated into a reading companion robot. The research activities were based on Design Science Research Process, Agent-Based Modelling, and Ambient Agent Framework. The proposed model was evaluated through a series of verification and validation approaches. The verification process includes equilibria evaluation and automated trace analysis approaches to ensure the model exhibits realistic behaviours and in accordance to related empirical data and literature. On the other hand, validation process that involved human experiment proved that a reading companion robot was able to reduce cognitive load during demanding reading tasks. Moreover, experiments results indicated that the integration of an ambient agent model into a reading companion robot enabled the robot to be perceived as a social, intelligent, useful, and motivational digital side-kick. The study contribution makes it feasible for new endeavours that aim at designing ambient applications based on human’s physical and cognitive process as an ambient agent model of cognitive load and reading performance was developed. Furthermore, it also helps in designing more realistic reading companion robots in the future

Advances in Robot Navigation

Robot navigation includes different interrelated activities such as perception - obtaining and interpreting sensory information; exploration - the strategy that guides the robot to select the next direction to go; mapping - the construction of a spatial representation by using the sensory information perceived; localization - the strategy to estimate the robot position within the spatial map; path planning - the strategy to find a path towards a goal location being optimal or not; and path execution, where motor actions are determined and adapted to environmental changes. This book integrates results from the research work of authors all over the world, addressing the abovementioned activities and analyzing the critical implications of dealing with dynamic environments. Different solutions providing adaptive navigation are taken from nature inspiration, and diverse applications are described in the context of an important field of study: social robotics