Pyro: A Python-Based Versatile Programming Environment For Teaching Robotics

In this article we describe a programming framework called Pyro, which provides a set of abstractions that allows students to write platform-independent robot programs. This project is unique because of its focus on the pedagogical implications of teaching mobile robotics via a top-down approach. We describe the background of the project, its novel abstractions, its library of objects, and the many learning modules that have been created from which curricula for different types of courses can be drawn. Finally, we explore Pyro from the students\u27 perspective in a case study

Pyro: A Python-based Versatile Programming Environment for Teaching Robotics

In this paper we describe a programming framework called Pyro which provides a set of abstractions that allows students to write platform­independent robot programs. This project is unique because of its focus on the pedagogical implications of teaching mobile robotics via a top­down approach. We describe the background of the project, novel abstractions created, its library of objects, and the many learning modules that have been created from which curricula for different types of courses can be drawn. Finally, we explore Pyro from the students\u27 perspective in a case study

Pyro: A Python-based Versatile Programming Environment for Teaching Robotics

In this paper we describe a programming framework called Pyro which provides a set of abstractions that allows students to write platform­independent robot programs. This project is unique because of its focus on the pedagogical implications of teaching mobile robotics via a top­down approach. We describe the background of the project, novel abstractions created, its library of objects, and the many learning modules that have been created from which curricula for different types of courses can be drawn. Finally, we explore Pyro from the students\u27 perspective in a case study

Virtual Reality Games for Motor Rehabilitation

This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion

Mobile Robotics

The book is a collection of ten scholarly articles and reports of experiences and perceptions concerning pedagogical practices with mobile robotics.“This work is funded by CIEd – Research Centre on Education, project UID/CED/01661/2019, Institute of Education, University of Minho, through national funds of FCT/MCTES-PT.

Foundations of Human-Aware Planning -- A Tale of Three Models

abstract: A critical challenge in the design of AI systems that operate with humans in the loop is to be able to model the intentions and capabilities of the humans, as well as their beliefs and expectations of the AI system itself. This allows the AI system to be "human- aware" -- i.e. the human task model enables it to envisage desired roles of the human in joint action, while the human mental model allows it to anticipate how its own actions are perceived from the point of view of the human. In my research, I explore how these concepts of human-awareness manifest themselves in the scope of planning or sequential decision making with humans in the loop. To this end, I will show (1) how the AI agent can leverage the human task model to generate symbiotic behavior; and (2) how the introduction of the human mental model in the deliberative process of the AI agent allows it to generate explanations for a plan or resort to explicable plans when explanations are not desired. The latter is in addition to traditional notions of human-aware planning which typically use the human task model alone and thus enables a new suite of capabilities of a human-aware AI agent. Finally, I will explore how the AI agent can leverage emerging mixed-reality interfaces to realize effective channels of communication with the human in the loop.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Toward an Argumentation-based Dialogue framework for Human-Robot Collaboration

Successful human-robot collaboration with a common goal requires peer interaction in which humans and robots cooperate and complement each other\u27s expertise. Formal human-robot dialogue in which there is peer interaction is still in its infancy, though. My research recognizes three aspects of human-robot collaboration that call for dialogue: responding to discovery, pre-empting failure, and recovering from failure. In these scenarios the partners need the ability to challenge, persuade, exchange and expand beliefs about a joint action in order to collaborate through dialogue. My research identifies three argumentation-based dialogues: a persuasion dialogue to resolve disagreement, an information-seeking dialogue to expand individual knowledge, and an inquiry dialogue to share knowledge. A theoretical logic-based framework, a formalized dialogue protocol based on argumentation theory, and argumentation-based dialogue games were developed to provide dialogue support for peer interaction. The work presented in this thesis is the first to apply argumentation theory and three different logic-based argumentation dialogues for use in human-robot collaboration. The research presented in this thesis demonstrates a practical, real-time implementation in which persuasion, inquiry, and information-seeking dialogues are applied to shared decision making for human-robot collaboration in a treasure hunt game domain. My research investigates if adding peer interaction enabled through argumentation-based dialogue to an HRI system improves system performance and user experience during a collaborative task when compared to an HRI system that is capable of only supervisory interaction with minimal dialogue. Results from user studies in physical and simulated human-robot collaborative environments, which involved 108 human participants who interacted with a robot as peer and supervisor, are presented in this thesis. My research contributes to both the human-robot interaction (HRI) and the argumentation communities. First, it brings into HRI a structured method for a robot to maintain its beliefs, to reason using those beliefs, and to interact with a human as a peer via argumentation-based dialogues. The structured method allows the human-robot collaborators to share beliefs, respond to discovery, expand beliefs to recover from failure, challenge beliefs, or resolve conflicts by persuasion. It allows a robot to challenge a human or a human to challenge a robot to prevent human or robot errors. Third, my research provides a comprehensive subjective and objective analysis of the effectiveness of an HRI System with peer interaction that is enabled through argumentation-based dialogue. I compare this peer interaction to a system that is capable of only supervisory interaction with minimal dialogue. My research contributes to the harder questions for human-robot collaboration: what kind of human-robot dialogue support can enhance peer-interaction? How can we develop models to formalize those features? How can we ensure that those features really help, and how do they help? Human-robot dialogue that can aid shared decision making, support the expansion of individual or shared knowledge, and resolve disagreements between collaborative human-robot teams will be much sought after as human society transitions from a world of robot-as-a-tool to robot-as-a-partner. My research presents a version of peer interaction enabled through argumentation-based dialogue that allows humans and robots to work together as partners