Vision and Autonomous Systems Seminar

  • Gates Hillman Centers
  • Traffic21 Classroom 6501
  • Ph.D. Candidate
  • Department of Computer Science, Intelligent Robot Lab
  • Duke University

Bayesian Eigenobjects: A Unified Framework for 3D Robot Perception

Robot-object interaction requires several key perceptual building blocks including object pose estimation, object classification, and partial-object completion. These tasks form the perceptual foundation for many higher level operations including object manipulation and world-state estimation. Most existing approaches to these problems in the context of 3D robot perception assume an existing database of objects that the robot expects to encounter. In real-world settings, robots will inevitably be required to interact with previously unseen objects; novel approaches are required to allow for generalization across highly variable objects. We introduce a new approach: Bayesian Eigenobjects (BEOs), which comprise a novel object representation for robots designed to facilitate this generalization. BEOs allow a robot to observe a previously unseen object from a single viewpoint and jointly estimate that object's class, pose, and hidden geometry. BEOs significantly outperform competing approaches to joint classification and completion and are the first representation to enable joint estimation of class, pose, and 3D geometry.

Ben Burchfiel is a PhD Candidate at Duke University in the field of Robotics, Computer Vision, and Machine Learning advised by Dr. George Konidaris. His primary work lies in the area of robot perception: how can robots better interpret and reason about the world around them. Ben's research seeks to enable robots to move out of the lab and into real-world settings by allowing them to reason about novel objects using information from previous interactions with other objects. Ben received his Bachelor's degree in Computer Science from the University of Wisconsin-Madison in 2012 and his Master's degree in Computer Science from Duke University in 2015. Ben's other interests include Reinforcement Learning (and its inverse), data-driven grounded symbolic planning, and reasoning under uncertainty with sub-optimal (noisy) data.


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