Enabling a Pepper Robot to provide Automated and Interactive Tours of a Robotics Laboratory

The Pepper robot has become a widely recognised face for the perceived potential of social robots to enter our homes and businesses. However, to date, commercial and research applications of the Pepper have been largely restricted to roles in which the robot is able to remain stationary. This restriction is the result of a number of technical limitations, including limited sensing capabilities, and have as a result, reduced the number of roles in which use of the robot can be explored. In this paper, we present our approach to solving these problems, with the intention of opening up new research applications for the robot. To demonstrate the applicability of our approach, we have framed this work within the context of providing interactive tours of an open-plan robotics laboratory.Comment: 8 pages, Submitted to IROS 2018 (2018 IEEE/RSJ International Conference on Intelligent Robots and Systems), see https://bitbucket.org/pepper_qut/ for access to the softwar

A Brief Wellbeing Training Session Delivered by a Humanoid Social Robot: A Pilot Randomized Controlled Trial

Mental health and psychological distress are rising in adults, showing the importance of wellbeing promotion, support, and technique practice that is effective and accessible. Interactive social robots have been tested to deliver health programs but have not been explored to deliver wellbeing technique training in detail. A pilot randomised controlled trial was conducted to explore the feasibility of an autonomous humanoid social robot to deliver a brief mindful breathing technique to promote information around wellbeing. It contained two conditions: brief technique training (Technique) and control designed to represent a simple wait-list activity to represent a relationship-building discussion (Simple Rapport). This trial also explored willingness to discuss health-related topics with a robot. Recruitment uptake rate through convenience sampling was high (53%). A total of 230 participants took part (mean age = 29 years) with 71% being higher education students. There were moderate ratings of technique enjoyment, perceived usefulness, and likelihood to repeat the technique again. Interaction effects were found across measures with scores varying across gender and distress levels. Males with high distress and females with low distress who received the simple rapport activity reported greater comfort to discuss non-health topics than males with low distress and females with high distress. This trial marks a notable step towards the design and deployment of an autonomous wellbeing intervention to investigate the impact of a brief robot-delivered mindfulness training program for a sub-clinical population

Instructing and training robots through a natural language dialogue

This thesis focused on the problem of allowing non-expert users, such as the elderly, to teach robots how to perform everyday tasks through dialogue. In particular, this thesis addressed issues relating to how task knowledge, extracted from spoken instructions, should be encoded by a robot to allow the robot to learn complex tasks; how to generalise knowledge provided by human instructors such that the robot can perform the same task across different scenarios; as well as how to handle information gaps present in the explanation of tasks provided by novice users

Path algebra for mobile robots

In this paper, we introduce a path algebra well suited for navigation in environments that can be abstracted as topological graphs. From this path algebra, we derive algorithms to reduce routes in such environments. The routes are reduced in the sense that they are shorter (contain fewer edges), but still connect the endpoints of the initial routes. Contrary to planning methods descended from Disjktra’s Shortest Path Algorithm like D , the navigation methods derived from our path algebra do not require any graph representation. We prove that the reduced routes are optimal when the graphs are without cycles. In the case of graphs with cycles, we prove that whatever the length of the initial route, the length of the reduced route is bounded by a constant that only depends on the structure of the environment

Enabling a Pepper Robot to provide Automated and Interactive Tours of a Robotics Laboratory

The Pepper robot is a well recognised social robot that has found use in both research and commercial applications. However, to date, the use of the Pepper robot in these applications has been largely restricted to roles in which the robot is able to remain stationary. This restriction is the result of a number of technical limitations, including limited sensing capabilities, that restrict how well the robot is able to navigate within its environment. In this paper, we present our approach to solving these problems, with the intention of opening up new research applications for the robot. We demonstrate the utility of our approach by showing that it provides greater navigation capabilities in contrast to the standard capabilities of the robot, while providing an illustrative example of the robot making use of these enhanced capabilities by providing an interactive tour of an open-plan robotics laboratory

Audio signalling as a backup communication channel for multi-robot systems

This paper presents a low-bandwidth multi-robot communication system designed to serve as a backup communication channel in the event a robot suffers a network device fault. While much research has been performed in the area of distributing network communication across multiple robots within a system, individual robots are still susceptible to hardware failure. In the past, such robots would simply be removed from service, and their tasks re-allocated to other members. However, there are times when a faulty robot might be crucial to a mission, or be able to contribute in a less communication intensive area. By allowing robots to encode and decode messages into unique sequences of DTMF symbols, called words, our system is able to facilitate continued low-bandwidth communication between robots without access to network communication. Our results have shown that the system is capable of permitting robots to negotiate task initiation and termination, and is flexible enough to permit a pair of robots to perform a simple turn taking task

A simple vehicle for the transportation of a humanoid Nao robot

Locomotion and autonomy in humanoid robots is of utmost importance in integrating them into social and community service type roles. However, the limited range and speed of these robots severely limits their ability to be deployed in situations where fast response is necessary. While the ability for a humanoid to drive a vehicle would aide in increasing their overall mobility, the ability to mount and dismount a vehicle designed for human occupants is a non-trivial problem. To address this issue, this paper presents an innovative approach to enabling a humanoid robot to mount and dismount a vehicle by proposing a simple mounting bracket involving no moving parts. In conjunction with a purpose built robotic vehicle, the mounting bracket successfully allowed a humanoid Nao robot to mount, dismount and drive the vehicle

Learning and Executing Re-Usable Behaviour Trees From Natural Language Instruction

Domestic and service robots have the potential to transform industries such as health care and small-scale manufacturing, as well as the homes in which we live. However, due to the overwhelming variety of tasks these robots will be expected to complete, providing generic out-of-the-box solutions that meet the needs of every possible user is clearly intractable. To address this problem, robots must therefore not only be capable of learning how to complete novel tasks at run-time, but the solutions to these tasks must also be informed by the needs of the user. In this letter we demonstrate how behaviour trees, a well established control architecture in the fields of gaming and robotics, can be used in conjunction with natural language instruction to provide a robust and modular control architecture for instructing autonomous agents to learn and perform novel complex tasks. We also show how behaviour trees generated using our approach can be generalised to novel scenarios, and can be re-used in future learning episodes to create increasingly complex behaviours. We validate this work against an existing corpus of natural language instructions, demonstrate the application of our approach on both a simulated robot solving a toy problem, as well as two distinct real-world robot platforms which, respectively, complete a block sorting scenario, and a patrol scenario. </p

Learning functional argument mappings for hierarchical tasks from situation specific explanations

Hierarchical tasks learnt from situation specific explanations are typically limited in how well they generalise to situations beyond the explanation provided. To address this we present an approach to learning functional argument mappings for enabling task generalisation regardless of explanation specificity. These functional argument mappings allow subtasks within a hierarchical task to utilise both arguments provided to the parent task, as well as domain knowledge, to generalise to novel situations. We validate this approach with a number of scenarios in which the agent learns generalised tasks from situation specific explanations, and show that these tasks provide equal performance when compared to tasks learnt from generalisable explanations

A humanoid social robot to provide personalized feedback for health promotion in diet, physical activity, alcohol and cigarette use : A health clinic trial

Social robots have been used to promote health education and coaching to provide health information. Important behaviors to address and monitor include actions that can be modified, such as physical activity. These behaviours often require different personalised recommendations. Robots could be an effective way to give personalised health feedback based on scores, including in acute medical settings. This trial involved an automated social robot interaction in a health clinic to collect health data and provide personalized feedback on four key factors: exercise, diet, alcohol and cigarette use. Patients completed an 20-minute health questionnaire with a Pepper Robot in a clinic room during a health visit. The interaction was programmed to run autonomously with automatic scoring and feedback based on health scores. Instructions were delivered using co-verbal speech and detailed text on the tablet. Questions also included ratings on comfort to discuss health topics with a human or robot. Patients could choose to receive an optional follow-up in four weeks' time. A total of 47 patients completed the session. Patients reported being as comfortable to discuss health-related topics with a robot or human for exercise, diet, alcohol, cigarette use, and mental health. Program evaluation received moderate ratings for the robot on ease of use, usefulness and motivation to change a health behavior. No significant health changes were found 30 days later due to high initial health scores, leaving little room for improvement. This initial proof-of-concept trial found that a robot-delivered service could be deployed in a live health clinic in conjunction with patient visits.</p