Dieter Fox

About

Dieter Fox is a Professor in the Allen School of Computer Science & Engineering at the University of Washington. He grew up in Bonn, Germany, and received his Ph.D. in 1998 from the Computer Science Department at the University of Bonn. He joined the University of Washington faculty in the fall of 2000 and currently divides his time between the University of Washington and the Allen Institute for AI (Ai2). He leads the UW Robotics and State Estimation Lab (RSE-Lab). His research interests focus on robotics, artificial intelligence, and state estimation, with the goal of enabling systems to interact intelligently with people and their environment. Much of his work centers on perception and its connection to control, developing techniques to extract relevant information from raw sensor data. Application areas of his research include human activity recognition, 3D mapping and tracking, and robot manipulation and control. Dieter Fox is recognized as a Fellow of the AAAI, ACM, and IEEE, and has received prestigious awards such as the IEEE RAS Pioneer Award and the IJCAI John McCarthy Award. He has served as an editor of the IEEE Transactions on Robotics and has held leadership roles including Program Chair for the Robotics Science and Systems conference and the AAAI Conference. He teaches courses in robotics and AI at both undergraduate and graduate levels, including undergraduate capstone courses on robotics and interactive systems enabled by RGB-D cameras. He works closely with…

Research topics

• Computer Science
• Artificial Intelligence
• Computer vision
• Machine Learning
• Programming language
• Data Mining
• Mathematics
• Human–computer interaction
• Engineering
• Natural Language Processing

Selected publications

• DeepGMR: Learning Latent Gaussian Mixture Models for Registration

  Lecture notes in computer science · 2020 · 247 citations

  DOI

• PoseRBPF: A Rao–Blackwellized Particle Filter for 6-D Object Pose Tracking

  IEEE Transactions on Robotics · 2021 · 166 citations

  Senior authorCorresponding

  Tracking 6-D poses of objects from videos provides rich information to a robot in performing different tasks such as manipulation and navigation. In this article, we formulate the 6-D object pose tracking problem in the Rao–Blackwellized particle filtering framework, where the 3-D rotation and the 3-D translation of an object are decoupled. This factorization allows our approach, called PoseRBPF, to efficiently estimate the 3-D translation of an object along with the full distribution over the 3…

  DOI

• Sim2Real in Robotics and Automation: Applications and Challenges

  IEEE Transactions on Automation Science and Engineering · 2021 · 148 citations

  To Perform reliably and consistently over sustained periods of time, large-scale automation critically relies on computer simulation. Simulation allows us and supervisory AI to effectively design, validate, and continuously improve complex processes, and helps practitioners to gain insight into the operation and justify future investments. While numerous successful applications of simulation in industry exist, such as circuit simulation, finite element methods, and computeraided design (CAD), st…

  DOI

• Reactive Human-to-Robot Handovers of Arbitrary Objects

  2021 · 83 citations

  Senior authorCorresponding

  Human-robot object handovers have been an actively studied area of robotics over the past decade; however, very few techniques and systems have addressed the challenge of handing over diverse objects with arbitrary appearance, size, shape, and deformability. In this paper, we present a vision-based system that enables reactive human-to-robot handovers of unknown objects. Our approach combines closed-loop motion planning with real-time, temporally consistent grasp generation to ensure reactivity…

  DOI

• RMPflow: A Computational Graph for Automatic Motion Policy Generation

  Springer proceedings in advanced robotics · 2020 · 69 citations

  DOI

Recent grants

• NRI: Rich Task Perception for Programming by Demonstration

  NSF · $1.2M · 2015–2019

• Collaborative Research: NRI: FND: Graph Neural Networks for Multi-Object Manipulation

  NSF · $429k · 2020–2023

• NRI: Collaborative Research: Experiential Learning for Robots: From Physics to Actions to Tasks

  NSF · $760k · 2016–2020

Frequent coauthors

• Arsalan Mousavian

  99 shared

• Byron Boots

  85 shared

• Fábio Ramos

  79 shared

• Nathan Ratliff

  62 shared

• Chris Paxton

  59 shared

• Balakumar Sundaralingam

  58 shared

• Yashraj Narang

  58 shared

• Clemens Eppner

  51 shared

Education

• Ph.D.

  University of Bonn

  1998

Awards & honors

• Fellow of the AAAI
• Fellow of the ACM
• Fellow of IEEE
• IEEE RAS Pioneer Award
• IJCAI John McCarthy Award

Similar researchers at University of Washington

• Ali Farhadih-91
• Jeffrey Heerh-80
• Hannaneh Hajishirzih-75