Search Results for author: Wojciech Matusik

Found 49 papers, 20 papers with code

Prediction-Guided Multi-Objective Reinforcement Learning for Continuous Robot Control

2 code implementations ICML 2020 Jie Xu, Yunsheng Tian, Pingchuan Ma, Daniela Rus, Shinjiro Sueda, Wojciech Matusik

Many real-world control problems involve conflicting objectives where we desire a dense and high-quality set of control policies that are optimal for different objective preferences (called Pareto-optimal).

Multi-Objective Reinforcement Learning reinforcement-learning

Boundary Exploration for Bayesian Optimization With Unknown Physical Constraints

no code implementations12 Feb 2024 Yunsheng Tian, Ane Zuniga, Xinwei Zhang, Johannes P. Dürholt, Payel Das, Jie Chen, Wojciech Matusik, Mina Konaković Luković

In this paper, we observe that in such scenarios optimal solution typically lies on the boundary between feasible and infeasible regions of the design space, making it considerably more difficult than that with interior optima.

Bayesian Optimization Gaussian Processes

DiffAvatar: Simulation-Ready Garment Optimization with Differentiable Simulation

no code implementations20 Nov 2023 Yifei Li, Hsiao-yu Chen, Egor Larionov, Nikolaos Sarafianos, Wojciech Matusik, Tuur Stuyck

The realism of digital avatars is crucial in enabling telepresence applications with self-expression and customization.

Physical Simulations

ASAP: Automated Sequence Planning for Complex Robotic Assembly with Physical Feasibility

no code implementations29 Sep 2023 Yunsheng Tian, Karl D. D. Willis, Bassel Al Omari, Jieliang Luo, Pingchuan Ma, Yichen Li, Farhad Javid, Edward Gu, Joshua Jacob, Shinjiro Sueda, Hui Li, Sachin Chitta, Wojciech Matusik

The automated assembly of complex products requires a system that can automatically plan a physically feasible sequence of actions for assembling many parts together.

Hierarchical Grammar-Induced Geometry for Data-Efficient Molecular Property Prediction

1 code implementation4 Sep 2023 Minghao Guo, Veronika Thost, Samuel W Song, Adithya Balachandran, Payel Das, Jie Chen, Wojciech Matusik

Still, these techniques are faced with a common challenge in practice: Labeled data are limited by the cost of manual extraction from literature and laborious experimentation.

Drug Discovery Molecular Property Prediction +1

Learning Neural Constitutive Laws From Motion Observations for Generalizable PDE Dynamics

no code implementations27 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).

Out-of-Distribution Generalization

Category-Level Multi-Part Multi-Joint 3D Shape Assembly

no code implementations10 Mar 2023 Yichen Li, Kaichun Mo, Yueqi Duan, He Wang, Jiequan Zhang, Lin Shao, Wojciech Matusik, Leonidas Guibas

A successful joint-optimized assembly needs to satisfy the bilateral objectives of shape structure and joint alignment.

Graph Learning Graph Representation Learning

Accelerated Policy Learning with Parallel Differentiable Simulation

no code implementations ICLR 2022 Jie Xu, Viktor Makoviychuk, Yashraj Narang, Fabio Ramos, Wojciech Matusik, Animesh Garg, Miles Macklin

In this work we present a high-performance differentiable simulator and a new policy learning algorithm (SHAC) that can effectively leverage simulation gradients, even in the presence of non-smoothness.

Fast Aquatic Swimmer Optimization with Differentiable Projective Dynamics and Neural Network Hydrodynamic Models

no code implementations30 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).

Computational Efficiency

Data-Efficient Graph Grammar Learning for Molecular Generation

1 code implementation ICLR 2022 Minghao Guo, Veronika Thost, Beichen Li, Payel Das, Jie Chen, Wojciech Matusik

This is a non-trivial task for neural network-based generative models since the relevant chemical knowledge can only be extracted and generalized from the limited training data.

Evolution Gym: A Large-Scale Benchmark for Evolving Soft Robots

no code implementations NeurIPS 2021 Jagdeep Singh Bhatia, Holly Jackson, Yunsheng Tian, Jie Xu, Wojciech Matusik

In this paper, we propose Evolution Gym, the first large-scale benchmark for co-optimizing the design and control of soft robots.

Sim2Real for Soft Robotic Fish via Differentiable Simulation

no code implementations30 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.

MORPH

Closed-Loop Control of Additive Manufacturing via Reinforcement Learning

no code implementations29 Sep 2021 Michal Piovarci, Michael Foshey, Timothy Erps, Jie Xu, Vahid Babaei, Piotr Didyk, Wojciech Matusik, Szymon Rusinkiewicz, Bernd Bickel

We further show that in combination with reinforcement learning, our model can be used to discover control policies that outperform state-of-the-art controllers.

reinforcement-learning Reinforcement Learning (RL)

AutoOED: Automated Optimal Experimental Design Platform with Data- and Time-Efficient Multi-Objective Optimization

no code implementations29 Sep 2021 Yunsheng Tian, Mina Konakovic Lukovic, Michael Foshey, Timothy Erps, Beichen Li, Wojciech Matusik

We present AutoOED, an Automated Optimal Experimental Design platform powered by machine learning to accelerate discovering solutions with optimal objective trade-offs.

Bayesian Optimization BIG-bench Machine Learning +1

Dynamic Modeling of Hand-Object Interactions via Tactile Sensing

no code implementations9 Sep 2021 Qiang Zhang, Yunzhu Li, Yiyue Luo, Wan Shou, Michael Foshey, Junchi Yan, Joshua B. Tenenbaum, Wojciech Matusik, Antonio Torralba

This work takes a step on dynamics modeling in hand-object interactions from dense tactile sensing, which opens the door for future applications in activity learning, human-computer interactions, and imitation learning for robotics.

Contrastive Learning Imitation Learning +1

An End-to-End Differentiable Framework for Contact-Aware Robot Design

1 code implementation15 Jul 2021 Jie Xu, Tao Chen, Lara Zlokapa, Michael Foshey, Wojciech Matusik, Shinjiro Sueda, Pulkit Agrawal

Existing methods for co-optimization are limited and fail to explore a rich space of designs.

Intelligent Carpet: Inferring 3D Human Pose From Tactile Signals

no code implementations CVPR 2021 Yiyue Luo, Yunzhu Li, Michael Foshey, Wan Shou, Pratyusha Sharma, Tomas Palacios, Antonio Torralba, Wojciech Matusik

In this work, leveraging such tactile interactions, we propose a 3D human pose estimation approach using the pressure maps recorded by a tactile carpet as input.

3D Human Pose Estimation Multi-Person Pose Estimation

DiffCloth: Differentiable Cloth Simulation with Dry Frictional Contact

1 code implementation9 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.

Polygrammar: Grammar for Digital Polymer Representation and Generation

no code implementations5 May 2021 Minghao Guo, Wan Shou, Liane Makatura, Timothy Erps, Michael Foshey, Wojciech Matusik

Here, we present a parametric, context-sensitive grammar designed specifically for the representation and generation of polymers.

valid

AutoOED: Automated Optimal Experiment Design Platform

1 code implementation13 Apr 2021 Yunsheng Tian, Mina Konaković Luković, Timothy Erps, Michael Foshey, Wojciech Matusik

We present AutoOED, an Optimal Experiment Design platform powered with automated machine learning to accelerate the discovery of optimal solutions.

Bayesian Optimization BIG-bench Machine Learning

DiffAqua: A Differentiable Computational Design Pipeline for Soft Underwater Swimmers with Shape Interpolation

no code implementations2 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.

DiffPD: Differentiable Projective Dynamics

no code implementations15 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.

Friction

Diversity-Guided Multi-Objective Bayesian Optimization With Batch Evaluations

1 code implementation NeurIPS 2020 Mina Konakovic Lukovic, Yunsheng Tian, Wojciech Matusik

To further reduce the evaluation time in the optimization process, testing of several samples in parallel can be deployed.

Fusion 360 Gallery: A Dataset and Environment for Programmatic CAD Construction from Human Design Sequences

1 code implementation5 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.

CAD Reconstruction Program Synthesis

Fusion 360 Gallery: A Dataset and Environment for Programmatic CAD Reconstruction

no code implementations28 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.

CAD Reconstruction

Monocular Reconstruction of Neural Face Reflectance Fields

no code implementations CVPR 2021 Mallikarjun B R., Ayush Tewari, Tae-Hyun Oh, Tim Weyrich, Bernd Bickel, Hans-Peter Seidel, Hanspeter Pfister, Wojciech Matusik, Mohamed Elgharib, Christian Theobalt

The reflectance field of a face describes the reflectance properties responsible for complex lighting effects including diffuse, specular, inter-reflection and self shadowing.

Monocular Reconstruction

Efficient Continuous Pareto Exploration in Multi-Task Learning

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.

BIG-bench Machine Learning Multiobjective Optimization +1

D3PG: Deep Differentiable Deterministic Policy Gradients

no code implementations25 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.

Model Predictive Control

Knitting Skeletons: A Computer-Aided Design Tool for Shaping and Patterning of Knitted Garments

2 code implementations11 Apr 2019 Alexandre Kaspar, Liane Makatura, Wojciech Matusik

This work presents a novel interactive system for simple garment composition and surface patterning.

Human-Computer Interaction

Neural Inverse Knitting: From Images to Manufacturing Instructions

1 code implementation7 Feb 2019 Alexandre Kaspar, Tae-Hyun Oh, Liane Makatura, Petr Kellnhofer, Jacqueline Aslarus, Wojciech Matusik

Motivated by the recent potential of mass customization brought by whole-garment knitting machines, we introduce the new problem of automatic machine instruction generation using a single image of the desired physical product, which we apply to machine knitting.

ChainQueen: A Real-Time Differentiable Physical Simulator for Soft Robotics

no code implementations2 Oct 2018 Yuanming Hu, Jian-Cheng Liu, Andrew Spielberg, Joshua B. Tenenbaum, William T. Freeman, Jiajun Wu, Daniela Rus, Wojciech Matusik

The underlying physical laws of deformable objects are more complex, and the resulting systems have orders of magnitude more degrees of freedom and therefore they are significantly more computationally expensive to simulate.

Motion Planning

Learning to Zoom: a Saliency-Based Sampling Layer for Neural Networks

1 code implementation ECCV 2018 Adrià Recasens, Petr Kellnhofer, Simon Stent, Wojciech Matusik, Antonio Torralba

We introduce a saliency-based distortion layer for convolutional neural networks that helps to improve the spatial sampling of input data for a given task.

Caricature Gaze Estimation +2

A Dataset of Flash and Ambient Illumination Pairs from the Crowd

no code implementations ECCV 2018 Yagiz Aksoy, Changil Kim, Petr Kellnhofer, Sylvain Paris, Mohamed Elgharib, Marc Pollefeys, Wojciech Matusik

We present a dataset of thousands of ambient and flash illumination pairs to enable studying flash photography and other applications that can benefit from having separate illuminations.

On Learning Associations of Faces and Voices

1 code implementation15 May 2018 Changil Kim, Hijung Valentina Shin, Tae-Hyun Oh, Alexandre Kaspar, Mohamed Elgharib, Wojciech Matusik

We computationally model the overlapping information between faces and voices and show that the learned cross-modal representation contains enough information to identify matching faces and voices with performance similar to that of humans.

Speaker Identification

Learning-based Video Motion Magnification

2 code implementations ECCV 2018 Tae-Hyun Oh, Ronnachai Jaroensri, Changil Kim, Mohamed Elgharib, Frédo Durand, William T. Freeman, Wojciech Matusik

We show that the learned filters achieve high-quality results on real videos, with less ringing artifacts and better noise characteristics than previous methods.

Motion Magnification

Eye Tracking for Everyone

2 code implementations CVPR 2016 Kyle Krafka, Aditya Khosla, Petr Kellnhofer, Harini Kannan, Suchendra Bhandarkar, Wojciech Matusik, Antonio Torralba

We believe that we can put the power of eye tracking in everyone's palm by building eye tracking software that works on commodity hardware such as mobile phones and tablets, without the need for additional sensors or devices.

Gaze Estimation

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