Dieter Fox
University of Washington
H-index: 128
North America-United States
Description
Dieter Fox, With an exceptional h-index of 128 and a recent h-index of 81 (since 2020), a distinguished researcher at University of Washington, specializes in the field of Robotics, Artificial Intelligence, Computer Vision.
His recent articles reflect a diverse array of research interests and contributions to the field:
EVE: Enabling Anyone to Train Robot using Augmented Reality
Model predictive control techniques for autonomous systems
Collision-free motion generation
Training machine learning models using simulation for robotics systems and applications
Grasp determination for an object in clutter
Scaling Population-Based Reinforcement Learning with GPU Accelerated Simulation
Grasp pose prediction
Identifying objects using neural network-generated descriptors
Professor Information
University | University of Washington |
---|---|
Position | and NVIDIA |
Citations(all) | 91673 |
Citations(since 2020) | 31293 |
Cited By | 72595 |
hIndex(all) | 128 |
hIndex(since 2020) | 81 |
i10Index(all) | 326 |
i10Index(since 2020) | 263 |
University Profile Page | University of Washington |
Research & Interests List
Robotics
Artificial Intelligence
Computer Vision
Top articles of Dieter Fox
EVE: Enabling Anyone to Train Robot using Augmented Reality
The increasing affordability of robot hardware is accelerating the integration of robots into everyday activities. However, training a robot to automate a task typically requires physical robots and expensive demonstration data from trained human annotators. Consequently, only those with access to physical robots produce demonstrations to train robots. To mitigate this issue, we introduce EVE, an iOS app that enables everyday users to train robots using intuitive augmented reality visualizations without needing a physical robot. With EVE, users can collect demonstrations by specifying waypoints with their hands, visually inspecting the environment for obstacles, modifying existing waypoints, and verifying collected trajectories. In a user study (, ) consisting of three common tabletop tasks, EVE outperformed three state-of-the-art interfaces in success rate and was comparable to kinesthetic teaching-physically moving a real robot-in completion time, usability, motion intent communication, enjoyment, and preference (). We conclude by enumerating limitations and design considerations for future AR-based demonstration collection systems for robotics.
Authors
Jun Wang,Chun-Cheng Chang,Jiafei Duan,Dieter Fox,Ranjay Krishna
Journal
arXiv preprint arXiv:2404.06089
Published Date
2024/4/9
Model predictive control techniques for autonomous systems
Apparatuses, systems, and techniques to infer a sequence of actions to perform using one or more neural networks trained, at least in part, by optimizing a probability distribution function using a cost function, wherein the probability distribution represents different sequences of actions that can be performed. In at least one embodiment, a model predictive control problem is formulated as a Bayesian inference task to infer a set of solutions.
Published Date
2023/5/9
Collision-free motion generation
Apparatuses, systems, and techniques to perform collision-free motion generation (eg, to operate a real-world or virtual robot). In at least one embodiment, at least a portion of the collision-free motion generation is performed in parallel.
Published Date
2024/4/25
Training machine learning models using simulation for robotics systems and applications
Systems and techniques are described related to training one or more machine learning models for use in control of a robot. In at least one embodiment, one or more machine learning models are trained based at least on simulations of the robot and renderings of such simulations—which may be performed using one or more ray tracing algorithms, operations, or techniques.
Published Date
2024/3/21
Grasp determination for an object in clutter
Apparatuses, systems, and techniques determine a set of grasp poses that would allow a robot to successfully grasp an object that is proximate to at least one additional object. In at least one embodiment, the set of grasp poses is modified based on a determination that at least one of the grasp poses in the set of grasp poses would interfere with at least one additional object that is proximate to the object.
Published Date
2024/1/11
Scaling Population-Based Reinforcement Learning with GPU Accelerated Simulation
In recent years, deep reinforcement learning (RL) has shown its effectiveness in solving complex continuous control tasks like locomotion and dexterous manipulation. However, this comes at the cost of an enormous amount of experience required for training, exacerbated by the sensitivity of learning efficiency and the policy performance to hyperparameter selection, which often requires numerous trials of time-consuming experiments. This work introduces a Population-Based Reinforcement Learning (PBRL) approach that exploits a GPU-accelerated physics simulator to enhance the exploration capabilities of RL by concurrently training multiple policies in parallel. The PBRL framework is applied to three state-of-the-art RL algorithms -- PPO, SAC, and DDPG -- dynamically adjusting hyperparameters based on the performance of learning agents. The experiments are performed on four challenging tasks in Isaac Gym -- Anymal Terrain, Shadow Hand, Humanoid, Franka Nut Pick -- by analyzing the effect of population size and mutation mechanisms for hyperparameters. The results show that PBRL agents achieve superior performance, in terms of cumulative reward, compared to non-evolutionary baseline agents. The trained agents are finally deployed in the real world for a Franka Nut Pick} task, demonstrating successful sim-to-real transfer. Code and videos of the learned policies are available on our project website.
Authors
Asad Ali Shahid,Yashraj Narang,Vincenzo Petrone,Enrico Ferrentino,Ankur Handa,Dieter Fox,Marco Pavone,Loris Roveda
Journal
arXiv preprint arXiv:2404.03336
Published Date
2024/4/4
Grasp pose prediction
Apparatuses, systems, and techniques to generate and select grasp proposals. In at least one embodiment, grasp proposals are generated and selected using one or more neural networks, based on, for example, a latent code corresponding to an object.
Published Date
2024/4/18
Identifying objects using neural network-generated descriptors
Apparatuses, systems, and techniques are presented to identify one or more objects. In at least one embodiment, one or more neural networks can be used to identify one or more objects based, at least in part, on one or more descriptors of one or more segments of the one or more objects.
Published Date
2024/3/21
Professor FAQs
What is Dieter Fox's h-index at University of Washington?
The h-index of Dieter Fox has been 81 since 2020 and 128 in total.
What are Dieter Fox's top articles?
The articles with the titles of
EVE: Enabling Anyone to Train Robot using Augmented Reality
Model predictive control techniques for autonomous systems
Collision-free motion generation
Training machine learning models using simulation for robotics systems and applications
Grasp determination for an object in clutter
Scaling Population-Based Reinforcement Learning with GPU Accelerated Simulation
Grasp pose prediction
Identifying objects using neural network-generated descriptors
...
are the top articles of Dieter Fox at University of Washington.
What are Dieter Fox's research interests?
The research interests of Dieter Fox are: Robotics, Artificial Intelligence, Computer Vision
What is Dieter Fox's total number of citations?
Dieter Fox has 91,673 citations in total.
What are the co-authors of Dieter Fox?
The co-authors of Dieter Fox are Wolfram Burgard, Luke Zettlemoyer, Nicholas Roy, Henry Kautz, Frank Dellaert, Byron Boots.