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

Robots as Powerful Allies for the Study of Embodied Cognition from the Bottom Up

A large body of compelling evidence has been accumulated demonstrating that embodiment - the agent's physical setup, including its shape, materials, sensors and actuators - is constitutive for any form of cognition and as a consequence, models of cognition need to be embodied. In contrast to methods from empirical sciences to study cognition, robots can be freely manipulated and virtually all key variables of their embodiment and control programs can be systematically varied. As such, they provide an extremely powerful tool of investigation. We present a robotic bottom-up or developmental approach, focusing on three stages: (a) low-level behaviors like walking and reflexes, (b) learning regularities in sensorimotor spaces, and (c) human-like cognition. We also show that robotic based research is not only a productive path to deepening our understanding of cognition, but that robots can strongly benefit from human-like cognition in order to become more autonomous, robust, resilient, and safe.Comment: 22 pages, 3 figure

Setting Up the Beam for Human-Centered Service Tasks

We introduce the Beam, a collaborative autonomous mobile service robot, based on SuitableTech's Beam telepresence system. We present a set of enhancements to the telepresence system, including autonomy, human awareness, increased computation and sensing capabilities, and integration with the popular Robot Operating System (ROS) framework. Together, our improvements transform the Beam into a low-cost platform for research on service robots. We examine the Beam on target search and object delivery tasks and demonstrate that the robot achieves a 100% success rate.Comment: 10 page

AltURI: a thin middleware for simulated robot vision applications

Fast software performance is often the focus when developing real-time vision-based control applications for robot simulators. In this paper we have developed a thin, high performance middleware for USARSim and other simulators designed for real-time vision-based control applications. It includes a fast image server providing images in OpenCV, Matlab or web formats and a simple command/sensor processor. The interface has been tested in USARSim with an Unmanned Aerial Vehicle using two control applications; landing using a reinforcement learning algorithm and altitude control using elementary motion detection. The middleware has been found to be fast enough to control the flying robot as well as very easy to set up and use

Robots as Powerful Allies for the Study of Embodied Cognition from the Bottom Up

A large body of compelling evidence has been accumulated demonstrating that embodiment – the agent’s physical setup, including its shape, materials, sensors and actuators – is constitutive for any form of cognition and as a consequence, models of cognition need to be embodied. In contrast to methods from empirical sciences to study cognition, robots can be freely manipulated and virtually all key variables of their embodiment and control programs can be systematically varied. As such, they provide an extremely powerful tool of investigation. We present a robotic bottom-up or developmental approach, focusing on three stages: (a) low-level behaviors like walking and reflexes, (b) learning regularities in sensorimotor spaces, and (c) human-like cognition. We also show that robotic based research is not only a productive path to deepening our understanding of cognition, but that robots can strongly benefit from human-like cognition in order to become more autonomous, robust, resilient, and safe

ToBI - Team of Bielefeld A Human-Robot Interaction System for RoboCup@Home 2018

Wachsmuth S, Lier F, Meyer zu Borgsen S. ToBI - Team of Bielefeld A Human-Robot Interaction System for RoboCup@Home 2018. Presented at the RoboCup 2018, Montreal, Canada.The Team of Bielefeld (ToBI) was founded in 2009. The RoboCup team’s activities are embedded in a long-term research agenda towards human-robot interaction with laypersons in regular and smart home environments. The RoboCup@Home competition is an important benchmark and milestone for this goal in terms of robot capabilities as well as the system integration effort. In order to achieve a robust and stable system performance, we apply a systematic approach for reproducible robotic experimentation including automated tests. A second focus of research is the development of reusable robot behaviors and robot skills. By re-usability we mean both, the re-use in different robot tasks as well as the reuse across different platforms. For RoboCup 2018, we plan to enhance this approach for the standard platform Pepper which comes with certain requirements and limitations, like its own runtime and development ecosystem, limited computing resources onboard, or a limited range of sensor devices. We further introduce a simulation environment for the Pepper robot that is based on MORSE and allows to define additional artificial agents as human-like interaction partners. This is one of the key features for simulating complete RoboCup@Home tasks. In this paper, we will present a generic approach to these issues. System descriptions as well as build and deployment procedures are modeled in the Cognitive Interaction Toolkit. The overall framework inherently supports the idea of open research and offers direct access to reusable components and reproducible systems via a web-based catalo

qiBullet, a Bullet-based simulator for the Pepper and NAO robots

The Pepper and NAO robots are widely used for in-store advertizing and education, but also as robotic platforms for research purposes. Their presence in the academic field is expressed through various publications, multiple collaborative projects, and by being the standard platforms of two different RoboCup leagues. Developing, gathering data and training humanoid robots can be tedious: iteratively repeating specific tasks can present risks for the robots, and some environments can be difficult to setup. Software tools allowing to simulate complex environments and the dynamics of robots can thus alleviate that problem, allowing to perform the aforementioned processes on virtual models. One current drawback of the Pepper and NAO platforms is the lack of a physically accurate simulation tool, allowing to test scenarios involving repetitive movements and contacts with the environment on a virtual robot. In this paper, we introduce the qiBullet simulation tool, using the Bullet physics engine to provide such a solution for the Pepper and NAO robots.Comment: 4 pages, 5 figure

Robotics CTF (RCTF), a playground for robot hacking

Robots state of insecurity is onstage. There is an emerging concern about major robot vulnerabilities and their adverse consequences. However, there is still a considerable gap between robotics and cybersecurity domains. For the purpose of filling that gap, the present technical report presents the Robotics CTF (RCTF), an online playground to challenge robot security from any browser. We describe the architecture of the RCTF and provide 9 scenarios where hackers can challenge the security of different robotic setups. Our work empowers security researchers to a) reproduce virtual robotic scenarios locally and b) change the networking setup to mimic real robot targets. We advocate for hacker powered security in robotics and contribute by open sourcing our scenarios

Target Reaching Behaviour for Unfreezing the Robot in a Semi-Static and Crowded Environment

Robot navigation in human semi-static and crowded environments can lead to the freezing problem, where the robot can not move due to the presence of humans standing on its path and no other path is available. Classical approaches of robot navigation do not provide a solution for this problem. In such situations, the robot could interact with the humans in order to clear its path instead of considering them as unanimated obstacles. In this work, we propose a robot behavior for a wheeled humanoid robot that complains with social norms for clearing its path when the robot is frozen due to the presence of humans. The behavior consists of two modules: 1) A detection module, which make use of the Yolo v3 algorithm trained to detect human hands and human arms. 2) A gesture module, which make use of a policy trained in simulation using the Proximal Policy Optimization algorithm. Orchestration of the two models is done using the ROS framework