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Eric Heiden 
I am a research scientist at NVIDIA, working on physics simulators and their applications in robotics. My research is focused on reducing the sim2real gap. I am investigating simulators as semantic models of the world that can be updated from measurements. Such models are parameterized by physically meaningful variables and can enable robots to perform complex tasks.
I obtained my Ph.D. degree in Computer Science from the University of Southern California (USC) advised by Gaurav Sukhatme and was supported by a Google PhD Fellowship. I received a Master’s degree from USC, and a Bachelor’s degree from the University of Rostock (Germany).
Interests
- Robot learning
- Physics simulators
- Autonomous robots
- Task and Motion Planning
Education
PhD Computer Science, 2022
University of Southern California
MSc Computer Science (Intelligent Robotics), 2017
University of Southern California
BSc Computer Science, 2015
University of Rostock, Germany
Projects
Selected Publications
Grasp'D: Differentiable Contact-rich Grasp Synthesis for Multi-fingered Hands
Synthesize contact-rich grasps for multi-fingered hands via differentiable simulation
Dylan Turpin, Liquan Wang, Eric Heiden, Yun-Chun Chen, Miles Macklin, Stavros Tsogkas, Scen Dickinson, Animesh Garg
Inferring Articulated Rigid Body Dynamics from RGBD Video
We present a pipeline to learn simulators from depth or RGB video. The “URDF” of a mechanism is reconstructed, and the simulation parameters are inferred through Bayesian inference.
Eric Heiden, Ziang Liu, Vibhav Vineet, Erwin Coumans, Gaurav S. Sukhatme
Probabilistic Inference of Simulation Parameters via Parallel Differentiable Simulation
Reducing the reality gap through a Bayesian inference algorithm that leverages massive GPU parallelism and differentiable simulators.
Eric Heiden, Christopher E. Denniston, David Millard, Fabio Ramos, Gaurav S. Sukhatme
DiSECt: A Differentiable Simulation Engine for Autonomous Robotic Cutting
We introduce a differentiable simulator for robotic cutting. It achieves highly accurate predictions of the knife forces, optimizes cutting actions & more!
Best Student Paper at RSS 2021
Best Student Paper at RSS 2021
Eric Heiden, Miles Macklin, Yashraj Narang, Dieter Fox, Animesh Garg, Fabio Ramos
NeuralSim: Augmenting Differentiable Simulators with Neural Networks
Differentiable simulators provide an avenue for closing the sim-to-real gap by enabling the use of efficient, gradient-based …
Eric Heiden, David Millard, Erwin Coumans, Yizhou Sheng, Gaurav S. Sukhatme
Journal Articles
Ali Agha
(2021).
NeBula: Quest for Robotic Autonomy in Challenging Environments; TEAM CoSTAR at the DARPA Subterranean Challenge.
Journal of Field Robotics.
Eric Heiden, Luigi Palmieri, Leonard Bruns, Kai O. Arras, Gaurav S. Sukhatme, Sven Koenig
(2020).
Bench-MR: A Motion Planning Benchmark for Wheeled Mobile Robots.
IEEE Robotics and Automation Letters (RA-L) 2021 and presented at ICRA 2021 and the ICAPS 2021 Workshop on Planning and Robotics (PlanRob).
Ryan C. Julian, Eric Heiden, Zhanpeng He, Hejia Zhang, Stefan Schaal, Joseph J. Lim, Gaurav S. Sukhatme, Karol Hausman
(2019).
Scaling simulation-to-real transfer by learning composable robot skills.
International Journal of Robotics Research.
Ali-akbar Agha-mohammadi, Eric Heiden, Karol Hausman, Gaurav S. Sukhatme
(2019).
Confidence-rich Grid Mapping.
International Journal of Robotics Research.
Conference Proceedings
Kamak Ebadi, Yun Chang, Matteo Palieri, Alex Stephens, Alex Hatteland, Eric Heiden, Abhishek Thakur, Benjamin Morrell, Luca Carlone, Ali-akbar Agha-mohammadi
(2019).
LAMP: Large-Scale Autonomous Mapping and Positioning for Exploration of Perceptually-Degraded Subterranean Environments.
International Conference on Robotics and Automation (ICRA) 2020.
Eric Heiden, Ziang Liu, Ragesh K. Ramachandran, Gaurav S. Sukhatme
(2019).
Physics-based Simulation of Continuous-Wave LIDAR for Localization, Calibration and Tracking.
International Conference on Robotics and Automation (ICRA) 2020.
Hejia Zhang, Eric Heiden, Stefanos Nikolaidis, Joseph J. Lim, Gaurav S. Sukhatme
(2019).
Auto-conditioned Recurrent Mixture Density Networks for Learning Generalizable Robotic Manipulation Skills.
Southern California Robotics Symposium (SCR).
Eric Heiden, Daniel Pastor, Pradyumna Vyshnav, Ali-akbar Agha-mohammadi
(2018).
Heterogeneous Sensor Fusion via Confidence-rich 3D Grid Mapping: Application to Physical Robots.
International Symposium on Experimental Robotics (ISER).
Ryan C. Julian, Eric Heiden, Zhanpeng He, Hejia Zhang, Stefan Schaal, Joseph J. Lim, Gaurav S. Sukhatme, Karol Hausman
(2018).
Scaling simulation-to-real transfer by learning composable robot skills.
International Symposium on Experimental Robotics (ISER).
Eric Heiden, Luigi Palmieri, Sven Koenig, Kai O. Arras, Gaurav S. Sukhatme
(2018).
Gradient-Informed Path Smoothing for Wheeled Mobile Robots.
In International Conference on Robotics and Automation (ICRA).
Ali-akbar Agha-mohammadi, Eric Heiden, Karol Hausman, Gaurav S. Sukhatme
(2017).
Confidence-rich Grid Mapping.
International Symposium on Robotics Research (ISRR).
Eric Heiden, Karol Hausman, Gaurav S. Sukhatme, Ali-akbar Agha-mohammadi
(2017).
Planning High-speed Safe Trajectories in Confidence-rich Maps.
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
Eric Heiden, Sebastian Bader, Thomas Kirste
(2017).
Concept and Realization of a Diagnostic System for Smart Environments.
International Conference on Agents and Artificial Intelligence (ICAART).
Workshop articles & Abstracts
Eric Heiden, David Millard, Erwin Coumans, Gaurav S. Sukhatme
(2020).
Sparse-Input Neural Network Augmentations for Differentiable Simulators.
DiffCVGP Workshop.
Eric Heiden
(2020).
Closing the Sim2Real Gap using Invertible Simulators.
RSS Pioneers 2020 Workshop.
Eric Heiden, David Millard, Erwin Coumans, Gaurav S. Sukhatme
(2020).
Augmenting Differentiable Simulators with Neural Networks to Close the Sim2Real Gap.
R:SS 2020 Workshop on Closing the Reality Gap in Sim2Real Transfer for Robotics.
David Millard, Eric Heiden, Shubham Agrawal, Gaurav S. Sukhatme
(2019).
Automatic Differentiation and Continuous Sensitivity Analysis of Rigid Body Dynamics.
Eric Heiden, David Millard, Gaurav S. Sukhatme
(2019).
Real2Sim Transfer using Differentiable Physics.
R:SS Workshop on Closing the Reality Gap in Sim2real Transfer for Robotic Manipulation.
Experience
Deep Learning Intern
NVIDIA
May 2020 –
May 2022
Seattle, WA (Remote)
Closing the sim2real gap in robotic cutting via differentiable physics
Research Intern
Google Brain
Jan 2020 –
May 2020
Mountain View, CA
Ongoing research on differentiable rigid-body simulators.
Research Intern
NASA Jet Propulsion Laboratory (JPL)
May 2018 –
Aug 2018
Pasadena, CA
Researched representations and algorithms enabling autonomy of novel robot platforms.
Software Engineering Intern
Two Sigma Investments
May 2016 –
Aug 2016
New York, NY
Added support for parallel computing platform Apache Spark to the data science toolbox Beaker Notebook (now BeakerX).
Research Assistant
Information Sciences Institute (ISI)
Jan 2016 –
May 2017
Marina del Rey, CA
Built large-scale information retrieval system for gaining information about >300k firms from company web pages.
Software Engineering Intern
Microsoft
Sep 2014 –
Mar 2015
Dublin, Ireland
Built business intelligence dashboard and language processing tools for software engineering teams.
Research Assistant
University of Rostock
Oct 2012 –
Jun 2014
Rostock, Germany
Designed software tools and assisted with research projects in ubiquitous computing, bioinformatics, and web services.