CORE
🇺🇦
make metadata, not war
Services
Services overview
Explore all CORE services
Access to raw data
API
Dataset
FastSync
Content discovery
Recommender
Discovery
OAI identifiers
OAI Resolver
Managing content
Dashboard
Bespoke contracts
Consultancy services
Support us
Support us
Membership
Sponsorship
Community governance
Advisory Board
Board of supporters
Research network
About
About us
Our mission
Team
Blog
FAQs
Contact us
Decoupling Behavior, Perception, and Control for Autonomous Learning of Affordances
Authors
Aaron F. Bobick
Tucker Hermans
James M. Rehg
Publication date
1 May 2013
Publisher
'Institute of Electrical and Electronics Engineers (IEEE)'
Doi
Cite
Abstract
©2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Presented at the 2013 IEEE International Conference on Robotics and Automation (ICRA), 6-10 May 2013, Karlsruhe, Germany.DOI: 10.1109/ICRA.2013.6631290A novel behavior representation is introduced that permits a robot to systematically explore the best methods by which to successfully execute an affordance-based behavior for a particular object. The approach decomposes affordance-based behaviors into three components. We first define controllers that specify how to achieve a desired change in object state through changes in the agent’s state. For each controller we develop at least one behavior primitive that determines how the controller outputs translate to specific movements of the agent. Additionally we provide multiple perceptual proxies that define the representation of the object that is to be computed as input to the controller during execution. A variety of proxies may be selected for a given controller and a given proxy may provide input for more than one controller. When developing an appropriate affordance-based behavior strategy for a given object, the robot can systematically vary these elements as well as note the impact of additional task variables such as location in the workspace. We demonstrate the approach using a PR2 robot that explores different combinations of controller, behavior primitive, and proxy to perform a push or pull positioning behavior on a selection of household objects, learning which methods best work for each object
Similar works
Full text
Open in the Core reader
Download PDF
Available Versions
Scholarly Materials And Research @ Georgia Tech
See this paper in CORE
Go to the repository landing page
Download from data provider
oai:smartech.gatech.edu:1853/5...
Last time updated on 19/05/2014
Crossref
See this paper in CORE
Go to the repository landing page
Download from data provider
info:doi/10.1109%2Ficra.2013.6...
Last time updated on 05/06/2019