Search Results for author: Eric Heiden

Found 14 papers, 7 papers with code

Inferring Articulated Rigid Body Dynamics from RGBD Video

1 code implementation20 Mar 2022 Eric Heiden, Ziang Liu, Vibhav Vineet, Erwin Coumans, Gaurav S. Sukhatme

Being able to reproduce physical phenomena ranging from light interaction to contact mechanics, simulators are becoming increasingly useful in more and more application domains where real-world interaction or labeled data are difficult to obtain.

Probabilistic Inference of Simulation Parameters via Parallel Differentiable Simulation

no code implementations18 Sep 2021 Eric Heiden, Christopher E. Denniston, David Millard, Fabio Ramos, Gaurav S. Sukhatme

We address the latter problem of estimating parameters through a Bayesian inference approach that approximates a posterior distribution over simulation parameters given real sensor measurements.

Bayesian Inference Code Generation

NeBula: Quest for Robotic Autonomy in Challenging Environments; TEAM CoSTAR at the DARPA Subterranean Challenge

no code implementations21 Mar 2021 Ali Agha, Kyohei Otsu, Benjamin Morrell, David D. Fan, Rohan Thakker, Angel Santamaria-Navarro, Sung-Kyun Kim, Amanda Bouman, Xianmei Lei, Jeffrey Edlund, Muhammad Fadhil Ginting, Kamak Ebadi, Matthew Anderson, Torkom Pailevanian, Edward Terry, Michael Wolf, Andrea Tagliabue, Tiago Stegun Vaquero, Matteo Palieri, Scott Tepsuporn, Yun Chang, Arash Kalantari, Fernando Chavez, Brett Lopez, Nobuhiro Funabiki, Gregory Miles, Thomas Touma, Alessandro Buscicchio, Jesus Tordesillas, Nikhilesh Alatur, Jeremy Nash, William Walsh, Sunggoo Jung, Hanseob Lee, Christoforos Kanellakis, John Mayo, Scott Harper, Marcel Kaufmann, Anushri Dixit, Gustavo Correa, Carlyn Lee, Jay Gao, Gene Merewether, Jairo Maldonado-Contreras, Gautam Salhotra, Maira Saboia Da Silva, Benjamin Ramtoula, Yuki Kubo, Seyed Fakoorian, Alexander Hatteland, Taeyeon Kim, Tara Bartlett, Alex Stephens, Leon Kim, Chuck Bergh, Eric Heiden, Thomas Lew, Abhishek Cauligi, Tristan Heywood, Andrew Kramer, Henry A. Leopold, Chris Choi, Shreyansh Daftry, Olivier Toupet, Inhwan Wee, Abhishek Thakur, Micah Feras, Giovanni Beltrame, George Nikolakopoulos, David Shim, Luca Carlone, Joel Burdick

This paper presents and discusses algorithms, hardware, and software architecture developed by the TEAM CoSTAR (Collaborative SubTerranean Autonomous Robots), competing in the DARPA Subterranean Challenge.

Decision Making Motion Planning

NeuralSim: Augmenting Differentiable Simulators with Neural Networks

2 code implementations9 Nov 2020 Eric Heiden, David Millard, Erwin Coumans, Yizhou Sheng, Gaurav S. Sukhatme

Differentiable simulators provide an avenue for closing the sim-to-real gap by enabling the use of efficient, gradient-based optimization algorithms to find the simulation parameters that best fit the observed sensor readings.


Augmenting Differentiable Simulators with Neural Networks to Close the Sim2Real Gap

1 code implementation12 Jul 2020 Eric Heiden, David Millard, Erwin Coumans, Gaurav S. Sukhatme

We present a differentiable simulation architecture for articulated rigid-body dynamics that enables the augmentation of analytical models with neural networks at any point of the computation.

Confidence-rich grid mapping

no code implementations29 Jun 2020 Ali-akbar Agha-mohammadi, Eric Heiden, Karol Hausman, Gaurav S. Sukhatme

Representing the environment is a fundamental task in enabling robots to act autonomously in unknown environments.

Motion Planning

Experimental Comparison of Global Motion Planning Algorithms for Wheeled Mobile Robots

1 code implementation7 Mar 2020 Eric Heiden, Luigi Palmieri, Kai O. Arras, Gaurav S. Sukhatme, Sven Koenig

Planning smooth and energy-efficient motions for wheeled mobile robots is a central task for applications ranging from autonomous driving to service and intralogistic robotics.

Autonomous Driving Motion Planning

Automatic Differentiation and Continuous Sensitivity Analysis of Rigid Body Dynamics

no code implementations22 Jan 2020 David Millard, Eric Heiden, Shubham Agrawal, Gaurav S. Sukhatme

A key ingredient to achieving intelligent behavior is physical understanding that equips robots with the ability to reason about the effects of their actions in a dynamic environment.

Physics-based Simulation of Continuous-Wave LIDAR for Localization, Calibration and Tracking

no code implementations3 Dec 2019 Eric Heiden, Ziang Liu, Ragesh K. Ramachandran, Gaurav S. Sukhatme

Light Detection and Ranging (LIDAR) sensors play an important role in the perception stack of autonomous robots, supplying mapping and localization pipelines with depth measurements of the environment.

Interactive Differentiable Simulation

2 code implementations26 May 2019 Eric Heiden, David Millard, Hejia Zhang, Gaurav S. Sukhatme

While learning-based models of the environment dynamics have contributed to significant improvements in sample efficiency compared to model-free reinforcement learning algorithms, they typically fail to generalize to system states beyond the training data, while often grounding their predictions on non-interpretable latent variables.


Simulator Predictive Control: Using Learned Task Representations and MPC for Zero-Shot Generalization and Sequencing

1 code implementation4 Oct 2018 Zhanpeng He, Ryan Julian, Eric Heiden, Hejia Zhang, Stefan Schaal, Joseph J. Lim, Gaurav Sukhatme, Karol Hausman

We complete unseen tasks by choosing new sequences of skill latents to control the robot using MPC, where our MPC model is composed of the pre-trained skill policy executed in the simulation environment, run in parallel with the real robot.

Auto-conditioned Recurrent Mixture Density Networks for Learning Generalizable Robot Skills

no code implementations29 Sep 2018 Hejia Zhang, Eric Heiden, Stefanos Nikolaidis, Joseph J. Lim, Gaurav S. Sukhatme

Personal robots assisting humans must perform complex manipulation tasks that are typically difficult to specify in traditional motion planning pipelines, where multiple objectives must be met and the high-level context be taken into consideration.

Motion Planning

Scaling simulation-to-real transfer by learning composable robot skills

1 code implementation26 Sep 2018 Ryan Julian, Eric Heiden, Zhanpeng He, Hejia Zhang, Stefan Schaal, Joseph J. Lim, Gaurav Sukhatme, Karol Hausman

In particular, we first use simulation to jointly learn a policy for a set of low-level skills, and a "skill embedding" parameterization which can be used to compose them.

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