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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
2007
Gockley, R.; Forlizzi, J.; Simmons, R. (2007) : Natural person-following behavior for social robots: 2007 2nd ACM/IEEE International Conference on Human-Robot Interaction (HRI): New York, NY, US: Association for Computing Machinery, S. 17-24

DOI: https://doi.org/10.1145/1228716.1228720

Abstract: We are developing robots with socially appropriate spatial skills not only to travel around or near people, but also to accompany people side-by-side. As a step toward this goal, we are investigating the social perceptions of a robot’s movement as it follows behind a person. This paper discusses our laser-based person-tracking method and two different approaches to person-following: direction-following and path-following. While both algorithms have similar characteristics in terms of tracking performance and following distances, participants in a pilot study rated the direction-following behavior as significantly more human-like and natural than the path-following behavior. We argue that the path-following method may still be more appropriate in some situations, and we propose that the ideal person-following behavior may be a hybrid approach, with the robot automatically selecting which method to use.

Keywords: Abstracts, Angemessen(heit) (von Technik), direction-following method, Hardware, human-robot interaction, ieee xplore, laser-based person-tracking method, Lasers, Mobile robots, natural person-following behavior, path-following method, person following, person tracking, person-following method, position control, Reliability, Robots, social robots, social sciences, spatial skills, speech

2014

2007

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