1 code implementation • 22 Feb 2019 • Jayesh K. Gupta, Kunal Menda, Zachary Manchester, Mykel J. Kochenderfer
We address the need for a flexible, gray-box model of mechanical systems that can seamlessly incorporate prior knowledge where it is available, and train expressive function approximators where it is not.
Model-based Reinforcement Learning Reinforcement Learning (RL)
no code implementations • 25 Sep 2019 • Kunal Menda, Jean de Becdelièvre, Jayesh K Gupta, Ilan Kroo, Mykel J. Kochenderfer, Zachary Manchester
System identification is the process of building a mathematical model of an unknown system from measurements of its inputs and outputs.
1 code implementation • 22 Oct 2019 • Simon Le Cleac'h, Mac Schwager, Zachary Manchester
We evaluate our solver in the context of autonomous driving on scenarios with a strong level of interactions between the vehicles.
2 code implementations • 26 Feb 2020 • Jan Brüdigam, Zachary Manchester
Most dynamic simulation tools parameterize the configuration of multi-body robotic systems using minimal coordinates, also called generalized or joint coordinates.
Robotics Computational Physics
1 code implementation • L4DC 2020 • Jayesh K. Gupta, Kunal Menda, Zachary Manchester, Mykel J. Kochenderfer
Deep neural networks have been used to learn models of robot dynamics from data, but they suffer from data-inefficiency and the difficulty to incorporate prior knowledge.
1 code implementation • ICML 2020 • Kunal Menda, Jean de Becdelièvre, Jayesh K. Gupta, Ilan Kroo, Mykel J. Kochenderfer, Zachary Manchester
System identification is a key step for model-based control, estimator design, and output prediction.
1 code implementation • 5 May 2021 • Kunal Menda, Jayesh K. Gupta, Zachary Manchester, Mykel J. Kochenderfer
Structured Mechanical Models (SMMs) are a data-efficient black-box parameterization of mechanical systems, typically fit to data by minimizing the error between predicted and observed accelerations or next states.
1 code implementation • NeurIPS 2021 • Swaminathan Gurumurthy, Shaojie Bai, Zachary Manchester, J. Zico Kolter
Many tasks in deep learning involve optimizing over the \emph{inputs} to a network to minimize or maximize some objective; examples include optimization over latent spaces in a generative model to match a target image, or adversarially perturbing an input to worsen classifier performance.
no code implementations • 17 Oct 2022 • Simon Le Cleac'h, Hong-Xing Yu, Michelle Guo, Taylor A. Howell, Ruohan Gao, Jiajun Wu, Zachary Manchester, Mac Schwager
A robot can use this simulation to optimize grasps and manipulation trajectories of neural objects, or to improve the neural object models through gradient-based real-to-simulation transfer.
no code implementations • ICCV 2023 • Gengshan Yang, Shuo Yang, John Z. Zhang, Zachary Manchester, Deva Ramanan
Given monocular videos, we build 3D models of articulated objects and environments whose 3D configurations satisfy dynamics and contact constraints.
no code implementations • 24 Jun 2023 • Giusy Falcone, Jacob B. Willis, Zachary Manchester
In this work, we achieve propellantless control of both cross-track and along-track separation of a satellite formation by manipulating atmospheric drag.