D
Dieter Fox
Researcher at Nvidia
Publications - 95
Citations - 4836
Dieter Fox is an academic researcher from Nvidia. The author has contributed to research in topics: Robot & GRASP. The author has an hindex of 21, co-authored 95 publications receiving 2489 citations. Previous affiliations of Dieter Fox include University of Washington.
Papers
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Deep Object Pose Estimation for Semantic Robotic Grasping of Household Objects
TL;DR: This network is the first deep network trained only on synthetic data that is able to achieve state-of-the-art performance on 6-DoF object pose estimation and demonstrates a real-time system estimating object poses with sufficient accuracy for real-world semantic grasping of known household objects in clutter by a real robot.
Journal ArticleDOI
The limits and potentials of deep learning for robotics
Niko Sünderhauf,Oliver Brock,Walter J. Scheirer,Raia Hadsell,Dieter Fox,Jürgen Leitner,Ben Upcroft,Pieter Abbeel,Wolfram Burgard,Michael Milford,Peter Corke +10 more
TL;DR: The need for better evaluation metrics is explained, the importance and unique challenges for deep robotic learning in simulation are highlighted, and the spectrum between purely data-driven and model-driven approaches is explored.
Proceedings ArticleDOI
6-DOF GraspNet: Variational Grasp Generation for Object Manipulation
TL;DR: This work forms the problem of grasp generation as sampling a set of grasps using a variational autoencoder and assess and refine the sampled graspts using a grasp evaluator model, trained purely in simulation and works in the real-world without any extra steps.
Proceedings ArticleDOI
Closing the Sim-to-Real Loop: Adapting Simulation Randomization with Real World Experience
Yevgen Chebotar,Ankur Handa,Viktor Makoviychuk,Miles Macklin,Jan Issac,Nathan Ratliff,Dieter Fox +6 more
TL;DR: In this paper, instead of manually tuning the randomization of simulations, the authors adapt the simulation parameter distribution using a few real world roll-outs interleaved with policy training.
Posted Content
Closing the Sim-to-Real Loop: Adapting Simulation Randomization with Real World Experience
Yevgen Chebotar,Ankur Handa,Viktor Makoviychuk,Miles Macklin,Jan Issac,Nathan Ratliff,Dieter Fox +6 more
TL;DR: This work adapts the simulation parameter distribution using a few real world roll-outs interleaved with policy training to improve the policy transfer by matching the policy behavior in simulation and the real world.