ICRA 2011 Paper Abstract


Paper TuA214.4

Narayanan, Krishna Kumar (Technische Universitšt Dortmund), Posada, Luis Felipe (Technische Universitšt Dortmund), Hoffmann, Frank (Technische Universitšt Dortmund), Bertram, Torsten (Technische Universitšt Dortmund)

Scenario and Context Specific Visual Robot Behavior Learning

Scheduled for presentation during the Regular Sessions "Visual Navigation II" (TuA214), Tuesday, May 10, 2011, 10:50−11:05, Room 5J

2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China

This information is tentative and subject to change. Compiled on April 2, 2020

Keywords Visual Learning, Visual Navigation, Behaviour-Based Systems


The design of visual robot behaviors constitutes a substantial challenge. It requires to draw meaningful relationships and constraints between the acquired visual perception and the geometry of the environment both empirically and programmatically. This contribution proposes a novel robot learning framework to classify and acquire scenario specific autonomous behaviors through demonstration. During demonstration, robocentric 3D range and omnidirectional images are recorded as training instances of typical robot navigation situations pertaining to different contexts in multiple indoor scenarios. A programming by demonstration approach generalizes the demonstrated trajectories to a general mapping between visual features extracted from the omnidirectional image onto a corresponding robot motion. The approach is able to distinguish among different traversing scenarios and further identifies the best matching context within the scenario to predict an appropriate robot motion. As a comparison to context matching, the behaviors are trained by means of an artificial neural network and its generalization ability is evaluated against the former. The experimental results on the mobile robot indicate that the acquired visual behavior is robust and generalizes meaningful actions beyond the specific environments and scenarios presented during training.



Technical Content © IEEE Robotics & Automation Society

This site is protected by copyright and trademark laws under US and International law.
All rights reserved. © 2002-2020 PaperCept, Inc.
Page generated 2020-04-02  11:43:17 PST  Terms of use