Exploring the Design Space of Robot Appearance and Behavior in an Attention-Seeking Living Room Scenario for a Robot Companion

This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.---- Copyright IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. --DOI : 10.1109/ALIFE.2007.36781

Teaching humanoid robotics by means of human teleoperation through RGB-D sensors

This paper presents a graduate course project on humanoid robotics offered by the University of Padova. The target is to safely lift an object by teleoperating a small humanoid. Students have to map human limbs into robot joints, guarantee the robot stability during the motion, and teleoperate the robot to perform the correct movement. We introduce the following innovative aspects with respect to classical robotic classes: i) the use of humanoid robots as teaching tools; ii) the simplification of the stable locomotion problem by exploiting the potential of teleoperation; iii) the adoption of a Project-Based Learning constructivist approach as teaching methodology. The learning objectives of both course and project are introduced and compared with the students\u2019 background. Design and constraints students have to deal with are reported, together with the amount of time they and their instructors dedicated to solve tasks. A set of evaluation results are provided in order to validate the authors\u2019 purpose, including the students\u2019 personal feedback. A discussion about possible future improvements is reported, hoping to encourage further spread of educational robotics in schools at all levels

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

The implications of embodiment for behavior and cognition: animal and robotic case studies

In this paper, we will argue that if we want to understand the function of the brain (or the control in the case of robots), we must understand how the brain is embedded into the physical system, and how the organism interacts with the real world. While embodiment has often been used in its trivial meaning, i.e. 'intelligence requires a body', the concept has deeper and more important implications, concerned with the relation between physical and information (neural, control) processes. A number of case studies are presented to illustrate the concept. These involve animals and robots and are concentrated around locomotion, grasping, and visual perception. A theoretical scheme that can be used to embed the diverse case studies will be presented. Finally, we will establish a link between the low-level sensory-motor processes and cognition. We will present an embodied view on categorization, and propose the concepts of 'body schema' and 'forward models' as a natural extension of the embodied approach toward first representations.Comment: Book chapter in W. Tschacher & C. Bergomi, ed., 'The Implications of Embodiment: Cognition and Communication', Exeter: Imprint Academic, pp. 31-5

Learning Human-Robot Collaboration Insights through the Integration of Muscle Activity in Interaction Motion Models

Recent progress in human-robot collaboration makes fast and fluid interactions possible, even when human observations are partial and occluded. Methods like Interaction Probabilistic Movement Primitives (ProMP) model human trajectories through motion capture systems. However, such representation does not properly model tasks where similar motions handle different objects. Under current approaches, a robot would not adapt its pose and dynamics for proper handling. We integrate the use of Electromyography (EMG) into the Interaction ProMP framework and utilize muscular signals to augment the human observation representation. The contribution of our paper is increased task discernment when trajectories are similar but tools are different and require the robot to adjust its pose for proper handling. Interaction ProMPs are used with an augmented vector that integrates muscle activity. Augmented time-normalized trajectories are used in training to learn correlation parameters and robot motions are predicted by finding the best weight combination and temporal scaling for a task. Collaborative single task scenarios with similar motions but different objects were used and compared. For one experiment only joint angles were recorded, for the other EMG signals were additionally integrated. Task recognition was computed for both tasks. Observation state vectors with augmented EMG signals were able to completely identify differences across tasks, while the baseline method failed every time. Integrating EMG signals into collaborative tasks significantly increases the ability of the system to recognize nuances in the tasks that are otherwise imperceptible, up to 74.6% in our studies. Furthermore, the integration of EMG signals for collaboration also opens the door to a wide class of human-robot physical interactions based on haptic communication that has been largely unexploited in the field.Comment: 7 pages, 2 figures, 2 tables. As submitted to Humanoids 201