Brian Okorn

I received my PhD at the CMU Robotics Institute as part of the R-PaD Lab. My goal is to develop vision systems that allow robots to interact intelligently with novel objects. In pursuit of this goal, I have developed zero-shot, and self-supervised methods of learning pose estimation, object detection, and scene flow prediction.

Prior to joining CMU, I worked as a researcher in the Unmanned Systems Branch of the Naval Information Warfare Center (NIWC) Pacific, formerly SPAWAR System Center Pacific, developing perception algorithms for surface, underground, and littoral robots and have worked on vision systems for the NASA Robonaut with the Johnson Space Center under the NSTRF Fellowship.

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Research

My research centers on object-centric vision for manipulation with a focus on zero-shot and self-supervised methods.

TAX-Pose: Task-Specific Cross-Pose Estimation for Robot Manipulation

Deep Projective Rotation Estimation through Relative Supervision

OSSID: Online Self-Supervised Instance Detection by (and for) Pose Estimation

IFOR: Iterative Flow Minimization for Robotic Object Rearrangement

Object Pose Estimation without Direct Supervision

Self-Supervised Point Cloud Completion via Inpainting

ZePHyR: Zero-shot Pose Hypothesis Rating

Visual Self-Supervised Reinforcement Learning with Object Reasoning

Learning Orientation Distributions for Object Pose Estimation

Cloth Region Segmentation for Robust Grasp Selection

Just Go with the Flow: Self-Supervised Scene Flow Estimation

Learning Adaptive Sampling Distributions for Motion Planning by Self-Imitation

Counter Tunnel Exploration, Mapping, and Localization with an Unmanned Ground Vehicle

Ego-Motion Estimation on Range Images using High-Order Polynomial Expansion

Smuggling Tunnel Mapping using Slide Image Registration

Toward Automated Modeling of Floor Plans

Using Laser Scanners for Modeling and Analysis in Architecture, Engineering, and Construction

Design and source code from Leo Keselman's website, originally from Jon Barron's website