Sebastian Wrede presenting at LSoCS Research Seminar

Sebastian Wrede presenting at LSoCS Research Seminar

Everybody in Lincoln is invited to learn about some exciting research in robotics done by my friend and former colleague Sebastian:

Time/Date: 4pm, Tuesday, 1 Oct 2013

Venue: MB1015, Main Admin Building
Speaker:  Dr. Sebastian Wrede, Cognitive Systems Engineering, CoR-Lab, Bielefeld University

Title: Kinesthetic Teaching of Redundant Robots in Task and Configuration Space

Abstract: The recent advent of compliant and kinematically redundant robots poses new research challenges for human-robot interaction. While these robots provide high flexibility for the realization of complex applications, the gained flexibility generates the need for additional modeling steps and the definition of criteria for redundancy resolution constraining the robot’s movement generation. The explicit modeling of such criteria usually require experts to adapt the robot’s movement generation subsystem. A typical way of dealing with this configuration challenge is to utilize kinesthetic teaching and guide the robot to implicitly model the specific constraints in task and configuration space. However, in this presentation we report on experiments showing that current programming-by-demonstration approaches are not efficient for kinesthetic teaching of redundant robots and typical teach-in procedures are too complex for novice users. In order to enable non-experts to master the configuration and programming of a redundant robot in the presence of non-trivial constraints such as confined spaces, the talk presents a new interaction scheme combining kinesthetic teaching and learning within an integrated system architecture. The approach was evaluated in a user study with 49 industrial workers in a medium-sized manufacturing company.  Results show that the interaction concepts implemented on a KUKA Lightweight Robot IV are easy to handle for novice users, demonstrate the feasibility of kinesthetic teaching for implicit constraint modeling in configuration space and yield significantly improved performance for the teach-in of trajectories in task space.

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