Master's Defense

Eigenobjects: A Unified Framework for Robot-Focused 3D Object Representation

Speaker:Benjamin Burchfiel
bcburch at cs.duke.edu
Date: Monday, April 25, 2016
Time: 2:00pm - 4:00pm
Location: N311 North, Duke

Abstract

One of the primary goals of robotics research is to enable robots to operate in unstructured human-centric environments such as homes or offices. To enable this capability, robots will need to reason about novel objects by generalizing from other previously encountered items. Three main subtasks are required to facilitate this: object classification, pose estimation, and 3D estimation of unobserved portions of the object. We propose a PCA-based approach to representing previously encountered (voxelized) 3D objects that learns low-dimensional spaces that capture each object class well. Our preliminary data shows that such a representation well suited to both classification and pose estimation, and also offers a natural extension to 3D completion of novel objects. We show that given a novel and partially specified object, it is possible to estimate the unobserved portions, estimate the object's pose, and assign it a class label. We present initial results on several data-sets showing our method performing classification, pose estimation, and object completion. As a next step, we propose extending our approach to create hierarchical clusters of object classes, propagating information between object classes, and learning parameterized functions (such as grasping controllers) that apply to portions of the learned hierarchy. The result will be a single representation that allows information from previously encountered objects to transfer to novel ones - even between classes - and supports fast real-time queries with sub-linear time complexity in the number of object classes.
Advisor(s): George Konidaris
Committee: Carlo Tomasi, Katherine Heller, Kris Hauser