1 code implementation • 11 Apr 2024 • Xueyi Liu, Kangbo Lyu, Jieqiong Zhang, Tao Du, Li Yi
We explore the dexterous manipulation transfer problem by designing simulators.
no code implementations • 3 Oct 2023 • Vaibhav Bihani, Utkarsh Pratiush, Sajid Mannan, Tao Du, Zhimin Chen, Santiago Miret, Matthieu Micoulaut, Morten M Smedskjaer, Sayan Ranu, N M Anoop Krishnan
In addition to our thorough evaluation and analysis on eight existing datasets based on the benchmarking literature, we release two new benchmark datasets, propose four new metrics, and three challenging tasks.
Ranked #1 on Formation Energy on GeTe
no code implementations • 27 Apr 2023 • Pingchuan Ma, Peter Yichen Chen, Bolei Deng, Joshua B. Tenenbaum, Tao Du, Chuang Gan, Wojciech Matusik
Many NN approaches learn an end-to-end model that implicitly models both the governing PDE and constitutive models (or material models).
no code implementations • 27 Mar 2023 • Sizhe Li, Zhiao Huang, Tao Chen, Tao Du, Hao Su, Joshua B. Tenenbaum, Chuang Gan
Reinforcement learning approaches for dexterous rigid object manipulation would struggle in this setting due to the complexity of physics interaction with deformable objects.
no code implementations • ICLR 2022 • Pingchuan Ma, Tao Du, Joshua B. Tenenbaum, Wojciech Matusik, Chuang Gan
To train this predictor, we formulate a new loss on rendering variances using gradients from differentiable rendering.
no code implementations • ICLR 2022 • Sizhe Li, Zhiao Huang, Tao Du, Hao Su, Joshua B. Tenenbaum, Chuang Gan
Extensive experimental results suggest that: 1) on multi-stage tasks that are infeasible for the vanilla differentiable physics solver, our approach discovers contact points that efficiently guide the solver to completion; 2) on tasks where the vanilla solver performs sub-optimally or near-optimally, our contact point discovery method performs better than or on par with the manipulation performance obtained with handcrafted contact points.
no code implementations • 30 Mar 2022 • Elvis Nava, John Z. Zhang, Mike Y. Michelis, Tao Du, Pingchuan Ma, Benjamin F. Grewe, Wojciech Matusik, Robert K. Katzschmann
For the deformable solid simulation of the swimmer's body, we use state-of-the-art techniques from the field of computer graphics to speed up the finite-element method (FEM).
no code implementations • NeurIPS 2021 • Mingyu Ding, Zhenfang Chen, Tao Du, Ping Luo, Joshua B. Tenenbaum, Chuang Gan
This is achieved by seamlessly integrating three components: a visual perception module, a concept learner, and a differentiable physics engine.
no code implementations • 30 Sep 2021 • John Z. Zhang, Yu Zhang, Pingchuan Ma, Elvis Nava, Tao Du, Philip Arm, Wojciech Matusik, Robert K. Katzschmann
Accurate simulation of soft mechanisms under dynamic actuation is critical for the design of soft robots.
1 code implementation • 9 Jun 2021 • Yifei Li, Tao Du, Kui Wu, Jie Xu, Wojciech Matusik
This work presents a differentiable cloth simulator whose additional gradient information facilitates cloth-related applications.
1 code implementation • ICLR 2021 • Zhiao Huang, Yuanming Hu, Tao Du, Siyuan Zhou, Hao Su, Joshua B. Tenenbaum, Chuang Gan
Experimental results suggest that 1) RL-based approaches struggle to solve most of the tasks efficiently; 2) gradient-based approaches, by optimizing open-loop control sequences with the built-in differentiable physics engine, can rapidly find a solution within tens of iterations, but still fall short on multi-stage tasks that require long-term planning.
no code implementations • 2 Apr 2021 • Pingchuan Ma, Tao Du, John Z. Zhang, Kui Wu, Andrew Spielberg, Robert K. Katzschmann, Wojciech Matusik
The computational design of soft underwater swimmers is challenging because of the high degrees of freedom in soft-body modeling.
no code implementations • 15 Jan 2021 • Tao Du, Kui Wu, Pingchuan Ma, Sebastien Wah, Andrew Spielberg, Daniela Rus, Wojciech Matusik
Inspired by Projective Dynamics (PD), we present Differentiable Projective Dynamics (DiffPD), an efficient differentiable soft-body simulator based on PD with implicit time integration.
1 code implementation • 5 Oct 2020 • Karl D. D. Willis, Yewen Pu, Jieliang Luo, Hang Chu, Tao Du, Joseph G. Lambourne, Armando Solar-Lezama, Wojciech Matusik
Parametric computer-aided design (CAD) is a standard paradigm used to design manufactured objects, where a 3D shape is represented as a program supported by the CAD software.
no code implementations • 28 Sep 2020 • Karl Willis, Yewen Pu, Jieliang Luo, Hang Chu, Tao Du, Joseph Lambourne, Armando Solar-Lezama, Wojciech Matusik
We provide a dataset of 8, 625 designs, comprising sequential sketch and extrude modeling operations, together with a complementary environment called the Fusion 360 Gym, to assist with performing CAD reconstruction.
1 code implementation • ICML 2020 • Pingchuan Ma, Tao Du, Wojciech Matusik
We present a novel, efficient method that generates locally continuous Pareto sets and Pareto fronts, which opens up the possibility of continuous analysis of Pareto optimal solutions in machine learning problems.
no code implementations • NeurIPS 2019 • Andrew Spielberg, Allan Zhao, Yuanming Hu, Tao Du, Wojciech Matusik, Daniela Rus
We validate the behavior of our algorithm with visualizations of the learned representation.
no code implementations • 25 Sep 2019 • Tao Du, Yunfei Li, Jie Xu, Andrew Spielberg, Kui Wu, Daniela Rus, Wojciech Matusik
Over the last decade, two competing control strategies have emerged for solving complex control tasks with high efficacy.
no code implementations • 8 Sep 2019 • Qi Zhang, Tao Du, Changzheng Tian
To improve the generalization ability for the driving behavior, the reinforcement learning method requires extrinsic reward from the real environment, which may damage the car.