Dieter Fox

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University of Washington ∙ Nvidia

Senior Director of Robotics Research at NVIDIA and Professor at University of Washington

IRIS: Implicit Reinforcement without Interaction at Scale for Learning Control from Offline Robot Manipulation Data

Learning from offline task demonstrations is a problem of great interest...
11/13/2019 ∙ by Ajay Mandlekar, et al. ∙ 108 ∙ share

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Motion Reasoning for Goal-Based Imitation Learning

We address goal-based imitation learning, where the aim is to output the...
11/13/2019 ∙ by De-An Huang, et al. ∙ 105 ∙ share

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LatentFusion: End-to-End Differentiable Reconstruction and Rendering for Unseen Object Pose Estimation

Current 6D object pose estimation methods usually require a 3D model for...
12/01/2019 ∙ by Keunhong Park, et al. ∙ 35 ∙ share

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Information Theoretic Model Predictive Q-Learning

Model-free Reinforcement Learning (RL) algorithms work well in sequentia...
12/31/2019 ∙ by Mohak Bhardwaj, et al. ∙ 29 ∙ share

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DexPilot: Vision Based Teleoperation of Dexterous Robotic Hand-Arm System

Teleoperation offers the possibility of imparting robotic systems with s...
10/07/2019 ∙ by Ankur Handa, et al. ∙ 20 ∙ share

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Conditional Driving from Natural Language Instructions

Widespread adoption of self-driving cars will depend not only on their s...
10/16/2019 ∙ by Junha Roh, et al. ∙ 18 ∙ share

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A Billion Ways to Grasp: An Evaluation of Grasp Sampling Schemes on a Dense, Physics-based Grasp Data Set

Robot grasping is often formulated as a learning problem. With the incre...
12/11/2019 ∙ by Clemens Eppner, et al. ∙ 14 ∙ share

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What Should I Do Now? Marrying Reinforcement Learning and Symbolic Planning

Long-term planning poses a major difficulty to many reinforcement learni...
01/06/2019 ∙ by Daniel Gordon, et al. ∙ 12 ∙ share

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6-DOF Grasping for Target-driven Object Manipulation in Clutter

Grasping in cluttered environments is a fundamental but challenging robo...
12/08/2019 ∙ by Adithyavairavan Murali, et al. ∙ 12 ∙ share

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Early Fusion for Goal Directed Robotic Vision

Increasingly, perceptual systems are being codified as strict pipelines ...
11/21/2018 ∙ by Aaron Walsman, et al. ∙ 10 ∙ share

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Part Segmentation for Highly Accurate Deformable Tracking in Occlusions via Fully Convolutional Neural Networks

Successfully tracking the human body is an important perceptual challeng...
08/05/2019 ∙ by Weilin Wan, et al. ∙ 10 ∙ share

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Riemannian Motion Policy Fusion through Learnable Lyapunov Function Reshaping

RMPflow is a recently proposed policy-fusion framework based on differen...
10/07/2019 ∙ by Mustafa Mukadam, et al. ∙ 9 ∙ share

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Online Replanning in Belief Space for Partially Observable Task and Motion Problems

To solve multi-step manipulation tasks in the real world, an autonomous ...
11/11/2019 ∙ by Caelan Reed Garrett, et al. ∙ 9 ∙ share

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Prospection: Interpretable Plans From Language By Predicting the Future

High-level human instructions often correspond to behaviors with multipl...
03/20/2019 ∙ by Chris Paxton, et al. ∙ 8 ∙ share

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

Tracking 6D poses of objects from videos provides rich information to a ...
05/22/2019 ∙ by Xinke Deng, et al. ∙ 8 ∙ share

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ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Tasks

We present ALFRED (Action Learning From Realistic Environments and Direc...
12/03/2019 ∙ by Mohit Shridhar, et al. ∙ 5 ∙ share

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The Best of Both Modes: Separately Leveraging RGB and Depth for Unseen Object Instance Segmentation

In order to function in unstructured environments, robots need the abili...
07/30/2019 ∙ by Christopher Xie, et al. ∙ 4 ∙ share

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Motion-Nets: 6D Tracking of Unknown Objects in Unseen Environments using RGB

In this work, we bridge the gap between recent pose estimation and track...
10/30/2019 ∙ by Felix Leeb, et al. ∙ 2 ∙ share

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Dynamic High Resolution Deformable Articulated Tracking

The last several years have seen significant progress in using depth cam...
11/21/2017 ∙ by Aaron Walsman, et al. ∙ 0 ∙ share

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Visual Semantic Planning using Deep Successor Representations

A crucial capability of real-world intelligent agents is their ability t...
05/23/2017 ∙ by Yuke Zhu, et al. ∙ 0 ∙ share

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Re3 : Real-Time Recurrent Regression Networks for Visual Tracking of Generic Objects

Robust object tracking requires knowledge and understanding of the objec...
05/17/2017 ∙ by Daniel Gordon, et al. ∙ 0 ∙ share

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DA-RNN: Semantic Mapping with Data Associated Recurrent Neural Networks

3D scene understanding is important for robots to interact with the 3D w...
03/09/2017 ∙ by Yu Xiang, et al. ∙ 0 ∙ share

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Reasoning About Liquids via Closed-Loop Simulation

Simulators are powerful tools for reasoning about a robot's interactions...
03/05/2017 ∙ by Connor Schenck, et al. ∙ 0 ∙ share

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Perceiving and Reasoning About Liquids Using Fully Convolutional Networks

Liquids are an important part of many common manipulation tasks in human...
03/05/2017 ∙ by Conor Schenck, et al. ∙ 0 ∙ share

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Building Hierarchies of Concepts via Crowdsourcing

Hierarchies of concepts are useful in many applications from navigation ...
04/27/2015 ∙ by Yuyin Sun, et al. ∙ 0 ∙ share

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See the Glass Half Full: Reasoning about Liquid Containers, their Volume and Content

Humans have rich understanding of liquid containers and their contents; ...
01/10/2017 ∙ by Roozbeh Mottaghi, et al. ∙ 0 ∙ share

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Visual Closed-Loop Control for Pouring Liquids

Pouring a specific amount of liquid is a challenging task. In this paper...
10/09/2016 ∙ by Connor Schenck, et al. ∙ 0 ∙ share

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Towards Learning to Perceive and Reason About Liquids

Recent advances in AI and robotics have claimed many incredible results ...
08/02/2016 ∙ by Connor Schenck, et al. ∙ 0 ∙ share

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Detection and Tracking of Liquids with Fully Convolutional Networks

Recent advances in AI and robotics have claimed many incredible results ...
06/20/2016 ∙ by Connor Schenck, et al. ∙ 0 ∙ share

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SE3-Nets: Learning Rigid Body Motion using Deep Neural Networks

We introduce SE3-Nets, which are deep neural networks designed to model ...
06/08/2016 ∙ by Arunkumar Byravan, et al. ∙ 0 ∙ share

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Gaussian Processes for Data-Efficient Learning in Robotics and Control

Autonomous learning has been a promising direction in control and roboti...
02/10/2015 ∙ by Marc Peter Deisenroth, et al. ∙ 0 ∙ share

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The Revisiting Problem in Mobile Robot Map Building: A Hierarchical Bayesian Approach

We present an application of hierarchical Bayesian estimation to robot m...
10/19/2012 ∙ by Benjamin Stewart, et al. ∙ 0 ∙ share

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Multi-Task Policy Search

Learning policies that generalize across multiple tasks is an important ...
07/02/2013 ∙ by Marc Peter Deisenroth, et al. ∙ 0 ∙ share

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Simulating Action Dynamics with Neural Process Networks

Understanding procedural language requires anticipating the causal effec...
11/14/2017 ∙ by Antoine Bosselut, et al. ∙ 0 ∙ share

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IQA: Visual Question Answering in Interactive Environments

We introduce Interactive Question Answering (IQA), the task of answering...
12/09/2017 ∙ by Daniel Gordon, et al. ∙ 0 ∙ share

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SE3-Pose-Nets: Structured Deep Dynamics Models for Visuomotor Planning and Control

In this work, we present an approach to deep visuomotor control using st...
10/02/2017 ∙ by Arunkumar Byravan, et al. ∙ 0 ∙ share

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PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes

Estimating the 6D pose of known objects is important for robots to inter...
11/01/2017 ∙ by Yu Xiang, et al. ∙ 0 ∙ share

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DeepIM: Deep Iterative Matching for 6D Pose Estimation

Estimating the 6D pose of objects from images is an important problem in...
03/31/2018 ∙ by Yi Li, et al. ∙ 0 ∙ share

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The Limits and Potentials of Deep Learning for Robotics

The application of deep learning in robotics leads to very specific prob...
04/18/2018 ∙ by Niko Sünderhauf, et al. ∙ 0 ∙ share

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SPNets: Differentiable Fluid Dynamics for Deep Neural Networks

In this paper we introduce Smooth Particle Networks (SPNets), a framewor...
06/15/2018 ∙ by Connor Schenck, et al. ∙ 0 ∙ share

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Robust Learning of Tactile Force Estimation through Robot Interaction

Current methods for estimating force from tactile sensor signals are eit...
10/15/2018 ∙ by Balakumar Sundaralingam, et al. ∙ 0 ∙ share

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Closing the Sim-to-Real Loop: Adapting Simulation Randomization with Real World Experience

We consider the problem of transferring policies to the real world by tr...
10/12/2018 ∙ by Yevgen Chebotar, et al. ∙ 0 ∙ share

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GPU-Accelerated Robotic Simulation for Distributed Reinforcement Learning

Most Deep Reinforcement Learning (Deep RL) algorithms require a prohibit...
10/12/2018 ∙ by Jacky Liang, et al. ∙ 0 ∙ share

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Deep Object Pose Estimation for Semantic Robotic Grasping of Household Objects

Using synthetic data for training deep neural networks for robotic manip...
09/27/2018 ∙ by Jonathan Tremblay, et al. ∙ 0 ∙ share

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Object Discovery in Videos as Foreground Motion Clustering

We consider the problem of providing dense segmentation masks for object...
12/06/2018 ∙ by Christopher Xie, et al. ∙ 0 ∙ share

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Learning Latent Space Dynamics for Tactile Servoing

In order to achieve a dexterous robotic manipulation, we need to equip o...
11/08/2018 ∙ by Giovanni Sutanto, et al. ∙ 0 ∙ share

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RMPflow: A Computational Graph for Automatic Motion Policy Generation

We develop a novel policy synthesis algorithm, RMPflow, based on geometr...
11/16/2018 ∙ by Ching-An Cheng, et al. ∙ 0 ∙ share

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ContactGrasp: Functional Multi-finger Grasp Synthesis from Contact

Grasping and manipulating objects is an important human skill. Since mos...
04/07/2019 ∙ by Samarth Brahmbhatt, et al. ∙ 0 ∙ share

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Joint Inference of Kinematic and Force Trajectories with Visuo-Tactile Sensing

To perform complex tasks, robots must be able to interact with and manip...
03/08/2019 ∙ by Alexander Lambert, et al. ∙ 0 ∙ share

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Neural Autonomous Navigation with Riemannian Motion Policy

End-to-end learning for autonomous navigation has received substantial a...
04/03/2019 ∙ by Xiangyun Meng, et al. ∙ 0 ∙ share

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Dieter Fox

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