English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
 
 
DownloadE-Mail
  Visual Stability Prediction and Its Application to Manipulation

Li, W., Leonardis, A., & Fritz, M. (2016). Visual Stability Prediction and Its Application to Manipulation. Retrieved from http://arxiv.org/abs/1609.04861.

Item is

Files

show Files
hide Files
:
arXiv_1609.04861.pdf (Preprint), 6MB
 
File Permalink:
-
Name:
arXiv:1609.04861.pdf
Description:
File downloaded from arXiv at 2016-10-11 14:58
OA-Status:
Visibility:
Private
MIME-Type / Checksum:
application/pdf
Technical Metadata:
Copyright Date:
-
Copyright Info:
-

Locators

show

Creators

show
hide
 Creators:
Li, Wenbin1, Author           
Leonardis, Aleš2, Author
Fritz, Mario1, Author           
Affiliations:
1Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society, ou_1116547              
2External Organizations, ou_persistent22              

Content

show
hide
Free keywords: Computer Science, Computer Vision and Pattern Recognition, cs.CV,Computer Science, Robotics, cs.RO
 Abstract: Understanding physical phenomena is a key competence that enables humans and animals to act and interact under uncertain perception in previously unseen environments containing novel objects and their configurations. Developmental psychology has shown that such skills are acquired by infants from observations at a very early stage. In this paper, we contrast a more traditional approach of taking a model-based route with explicit 3D representations and physical simulation by an {\em end-to-end} approach that directly predicts stability from appearance. We ask the question if and to what extent and quality such a skill can directly be acquired in a data-driven way---bypassing the need for an explicit simulation at run-time. We present a learning-based approach based on simulated data that predicts stability of towers comprised of wooden blocks under different conditions and quantities related to the potential fall of the towers. We first evaluate the approach on synthetic data and compared the results to human judgments on the same stimuli. Further, we extend this approach to reason about future states of such towers that in turn enables successful stacking.

Details

show
hide
Language(s): eng - English
 Dates: 2016-09-152016-09-262016
 Publication Status: Published online
 Pages: 8 p.
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: arXiv: 1609.04861
URI: http://arxiv.org/abs/1609.04861
BibTex Citekey: li16arxivb
 Degree: -

Event

show

Legal Case

show

Project information

show

Source

show