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2015
Bicchi, A. (2015) : Of Robots, Humans, Bodies and Intelligence: Body Languages for Human Robot Interaction: 2015 10th ACM/IEEE International Conference on Human-Robot Interaction (HRI): Portland, Oregon, USA: Association for Computing Machinery

Abstract: Modern approaches to the design of robots with increasing amounts of embodied intelligence affect human-robot interaction paradigms. The physical structure of robots is evolving from traditional rigid, heavy industrial machines into soft bodies exhibiting new levels of versatility, adaptability, safety, elasticity, dynamism and energy efficiency. New challenges and opportunities arise for the control of soft robots: for instance, carefully planning for collision avoidance may no longer be a dominating concern, being on the contrary physical interaction with the environment not only allowed, but even desirable to solve complex tasks. To address these challenges, it is often useful to look at how humans use their own bodies in similar tasks, and even in some cases have a direct dialogue between the natural and artificial counterparts. ACM Classification B.0 General.

Keywords: body languages, Body-Robot Interaction, Collision avoidance, contrary physical interaction, Dexterous Manipulation, embodied intelligence, Grasp Control, human-robot interaction, ieee xplore, Impedance Control, Künstliche Intelligenz, Physical Human-Robot Interaction, physical structure, safety, service robot, soft bodies, Soft robotics, soft robots, Task Analysis
2014
Kronander, Klas; Billard, Aude (2014): Learning Compliant Manipulation through Kinesthetic and Tactile Human-Robot Interaction. In: IEEE Transactions on Haptics 7 (3), S. 367-380. DOI: 10.1109/TOH.2013.54

DOI: https://doi.org/10.1109/TOH.2013.54

Abstract: Robot Learning from Demonstration (RLfD) has been identified as a key element for making robots useful in daily lives. A wide range of techniques has been proposed for deriving a task model from a set of demonstrations of the task. Most previous works use learning to model the kinematics of the task, and for autonomous execution the robot then relies on a stiff position controller. While many tasks can and have been learned this way, there are tasks in which controlling the position alone is insufficient to achieve the goals of the task. These are typically tasks that involve contact or require a specific response to physical perturbations. The question of how to adjust the compliance to suit the need of the task has not yet been fully treated in Robot Learning from Demonstration. In this paper, we address this issue and present interfaces that allow a human teacher to indicate compliance variations by physically interacting with the robot during task execution. We validate our approach in two different experiments on the 7 DoF Barrett WAM and KUKA LWR robot manipulators. Furthermore, we conduct a user study to evaluate the usability of our approach from a non-roboticists perspective.

Keywords: Algorithms, Analysis, Bedienung & Handhabung, Biomechanical Phenomena, compliance control, compliance variations, compliant control, compliant manipulation, Computer Simulation, education, Force, haptic feedback, haptic interfaces, human-robot interaction, Humans, Impedance, Joints, Kinesthesis, kinesthetic human-robot interaction, KUKA LWR robot manipulators, Learning, manipulator kinematics, Physical Human-Robot Interaction, position control, RLfD, Robot kinematics, robot learning from demonstration, Robot sensing systems, Robotics, stiff position controller, tactile human-robot interaction, tactile interfaces, task kinematics, task model, Task Performance, Touch

2015

2014

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