Author name cluster

Wojciech Matusik

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

42 papers

2 author rows

ICRA Conference 2025 Conference Paper

Adaptive Walker: User Intention and Terrain Aware Intelligent Walker with High-Resolution Tactile and IMU Sensor

Yunho Choi
Seokhyun Hwang
JaeYoung Moon
Hosu Lee
Dohyeon Yeo
Minwoo Seong
Yiyue Luo
SeungJun Kim 0001

In this paper, we present an adaptive walker system designed to address limitations in current intelligent walker technologies. While recent advancements have been made in this field, existing systems often struggle to seamlessly interpret user intent for speed control and lack adaptability across diverse scenarios and terrain. Our proposed solution incorporates high-resolution tactile sensors, deep learning algorithms, IMU sensors, and linear motors to dynamically adjust to the user's intentions and terrain changes. The system is capable of predicting the user's desired speed with an error margin of only 20. 99%, relying solely on tactile input from hand and arm contact points. Additionally, it maintains the walker's horizontal stability with an error of less than 1 degree by adjusting leg lengths in response to variations in ground angle. This adaptive walker enhances user safety and comfort, particularly for individuals with reduced strength or cognitive abilities, and offers reliable assistance on uneven terrain such as uphill and downhill paths.

ICRA Conference 2025 Conference Paper

AI-Enhanced Automatic Design of Efficient Underwater Gliders

Peter Yichen Chen
Pingchuan Ma 0002
Niklas Hagemann
John W. Romanishin
Wei Wang 0078
Daniela Rus
Wojciech Matusik

The development of novel autonomous underwater gliders has been hindered by limited shape diversity, primarily due to the reliance on traditional design tools that depend heavily on manual trial and error. Building an automated design framework is challenging due to the complexities of representing glider shapes and the high computational costs associated with modeling complex solid-fluid interactions. In this work, we introduce an AI-enhanced automated computational framework designed to overcome these limitations by enabling the creation of underwater robots with non-trivial hull shapes. Our approach involves an algorithm that cooptimizes both shape and control signals, utilizing a reducedorder geometry representation and a differentiable neural-network-based fluid surrogate model. This end-to-end design workflow facilitates rapid iteration and evaluation of hydrodynamic performance, leading to the discovery of optimal and complex hull shapes across various control settings. We validate our method through wind tunnel experiments and swimming pool gliding tests, demonstrating that our computationally designed gliders surpass manually designed counterparts in terms of energy efficiency. By addressing challenges in efficient shape representation and neural fluid surrogate models, our work paves the way for the development of highly efficient underwater gliders, with implications for long-range ocean exploration and environmental monitoring.

ICML Conference 2025 Conference Paper

Directed Graph Grammars for Sequence-based Learning

Michael Sun
Orion Foo
Gang Liu
Wojciech Matusik
Jie Chen 0007

Directed acyclic graphs (DAGs) are a class of graphs commonly used in practice, with examples that include electronic circuits, Bayesian networks, and neural architectures. While many effective encoders exist for DAGs, it remains challenging to decode them in a principled manner, because the nodes of a DAG can have many different topological orders. In this work, we propose a grammar-based approach to constructing a principled, compact and equivalent sequential representation of a DAG. Specifically, we view a graph as derivations over an unambiguous grammar, where the DAG corresponds to a unique sequence of production rules. Equivalently, the procedure to construct such a description can be viewed as a lossless compression of the data. Such a representation has many uses, including building a generative model for graph generation, learning a latent space for property prediction, and leveraging the sequence representational continuity for Bayesian Optimization over structured data.

NeurIPS Conference 2025 Conference Paper

FlashBias: Fast Computation of Attention with Bias

Haixu Wu
Minghao Guo
Yuezhou Ma
Yuanxu Sun
Jianmin Wang
Wojciech Matusik
Mingsheng Long

Attention with bias, which extends standard attention by introducing prior knowledge as an additive bias matrix to the query-key scores, has been widely deployed in vision, language, protein-folding and other advanced scientific models, underscoring its status as a key evolution of this foundational module. However, introducing bias terms creates a severe efficiency bottleneck in attention computation. It disrupts the tightly fused memory-compute pipeline that underlies the speed of accelerators like FlashAttention, thereby stripping away most of their performance gains and leaving biased attention computationally expensive. Surprisingly, despite its common usage, targeted efficiency optimization for attention with bias remains absent, which seriously hinders its application in complex tasks. Diving into the computation of FlashAttention, we prove that its optimal efficiency is determined by the rank of the attention weight matrix. Inspired by this theoretical result, this paper presents FlashBias based on the low-rank compressed sensing theory, which can provide fast-exact computation for many widely used attention biases and a fast-accurate approximation for biases in general formalizations. FlashBias can fully take advantage of the extremely optimized matrix multiplication operation in modern GPUs, achieving 1. 5$\times$ speedup for Pairformer in AlphaFold 3, and over 2$\times$ speedup for attention with bias in vision and language models without loss of accuracy. Code is available at this repository: https: //github. com/thuml/FlashBias.

ICML Conference 2025 Conference Paper

Foundation Molecular Grammar: Multi-Modal Foundation Models Induce Interpretable Molecular Graph Languages

Michael Sun
Weize Yuan
Gang Liu
Wojciech Matusik
Jie Chen 0007

Recent data-efficient molecular generation approaches exploit graph grammars to introduce interpretability into the generative models. However, grammar learning therein relies on expert annotation or unreliable heuristics for algorithmic inference. We propose Foundation Molecular Grammar (FMG), which leverages multi-modal foundation models (MMFMs) to induce an interpretable molecular language. By exploiting the chemical knowledge of an MMFM, FMG renders molecules as images, describes them as text, and aligns information across modalities using prompt learning. FMG can be used as a drop-in replacement for the prior grammar learning approaches in molecular generation and property prediction. We show that FMG not only excels in synthesizability, diversity, and data efficiency but also offers built-in chemical interpretability for automated molecular discovery workflows. Code is available at https: //github. com/shiningsunnyday/induction.

ICRA Conference 2025 Conference Paper

Learning Object Properties Using Robot Proprioception via Differentiable Robot-Object Interaction

Peter Yichen Chen
Chao Liu 0021
Pingchuan Ma 0002
John Eastman
Daniela Rus
Dylan Randle
Yuri Ivanov
Wojciech Matusik

Differentiable simulation has become a powerful tool for system identification. While prior work has focused on identifying robot properties using robot-specific data or object properties using object-specific data, our approach calibrates object properties by using information from the robot, without relying on data from the object itself. Specifically, we utilize robot joint encoder information, which is commonly available in standard robotic systems. Our key observation is that by analyzing the robot's reactions to manipulated objects, we can infer properties of those objects, such as inertia and softness. Leveraging this insight, we develop differentiable simulations of robot-object interactions to inversely identify the properties of the manipulated objects. Our approach relies solely on proprioception – the robot's internal sensing capabilities – and does not require external measurement tools or vision-based tracking systems. This general method is applicable to any articulated robot and requires only joint position information. We demonstrate the effectiveness of our method on a low-cost robotic platform, achieving accurate mass and elastic modulus estimations of manipulated objects with just a few seconds of computation on a laptop.

ICLR Conference 2025 Conference Paper

Multimodal Large Language Models for Inverse Molecular Design with Retrosynthetic Planning

Gang Liu
Michael Sun
Wojciech Matusik
Meng Jiang
Jie Chen 0007

While large language models (LLMs) have integrated images, adapting them to graphs remains challenging, limiting their applications in materials and drug design. This difficulty stems from the need for coherent autoregressive generation across texts and graphs. To address this, we introduce Llamole, the first multimodal LLM capable of interleaved text and graph generation, enabling molecular inverse design with retrosynthetic planning. Llamole integrates a base LLM with the Graph Diffusion Transformer and Graph Neural Networks for multi-conditional molecular generation and reaction inference within texts, while the LLM, with enhanced molecular understanding, flexibly controls activation among the different graph modules. Additionally, Llamole integrates A* search with LLM-based cost functions for efficient retrosynthetic planning. We create benchmarking datasets and conduct extensive experiments to evaluate Llamole against in-context learning and supervised fine-tuning. Llamole significantly outperforms 14 adapted LLMs across 12 metrics for controllable molecular design and retrosynthetic planning. Code and model at https://github.com/liugangcode/Llamole.

NeurIPS Conference 2025 Conference Paper

Post Hoc Regression Refinement via Pairwise Rankings

Kevin Tirta Wijaya
Michael Sun
Minghao Guo
Hans-peter Seidel
Wojciech Matusik
Vahid Babaei

Accurate prediction of continuous properties is essential to many scientific and engineering tasks. Although deep-learning regressors excel with abundant labels, their accuracy deteriorates in data-scarce regimes. We introduce RankRefine, a model-agnostic, plug-and-play post-hoc refinement technique that injects expert knowledge through pairwise rankings. Given a query item and a small reference set with known properties, RankRefine combines the base regressor’s output with a rank-based estimate via inverse-variance weighting, requiring no retraining. In molecular property prediction task, RankRefine achieves up to 10\% relative reduction in mean absolute error using only 20 pairwise comparisons obtained through a general-purpose large language model (LLM) with no finetuning. As rankings provided by human experts or general-purpose LLMs are sufficient for improving regression across diverse domains, RankRefine offers practicality and broad applicability, especially in low-data settings.

ICLR Conference 2025 Conference Paper

Procedural Synthesis of Synthesizable Molecules

Michael Sun
Alston Lo
Minghao Guo
Jie Chen 0007
Connor W. Coley
Wojciech Matusik

Designing synthetically accessible molecules and recommending analogs to unsynthesizable molecules are important problems for accelerating molecular discovery. We reconceptualize both problems using ideas from program synthesis. Drawing inspiration from syntax-guided synthesis approaches, we decouple the syntactic skeleton from the semantics of a synthetic tree to create a bilevel framework for reasoning about the combinatorial space of synthesis pathways. Given a molecule we aim to generate analogs for, we iteratively refine its skeletal characteristics via Markov Chain Monte Carlo simulations over the space of syntactic skeletons. Given a black-box oracle to optimize, we formulate a joint design space over syntactic templates and molecular descriptors and introduce evolutionary algorithms that optimize both syntactic and semantic dimensions synergistically. Our key insight is that once the syntactic skeleton is set, we can amortize over the search complexity of deriving the program's semantics by training policies to fully utilize the fixed horizon Markov Decision Process imposed by the syntactic template. We demonstrate performance advantages of our bilevel framework for synthesizable analog generation and synthesizable molecule design. Notably, our approach offers the user explicit control over the resources required to perform synthesis and biases the design space towards simpler solutions, making it particularly promising for autonomous synthesis platforms. Supporting code is at https://github.com/shiningsunnyday/SynthesisNet.

ICLR Conference 2025 Conference Paper

TetSphere Splatting: Representing High-Quality Geometry with Lagrangian Volumetric Meshes

Minghao Guo
Bohan Wang
Kaiming He
Wojciech Matusik

We introduce TetSphere Splatting, a Lagrangian geometry representation designed for high-quality 3D shape modeling. TetSphere splatting leverages an underused yet powerful geometric primitive -- volumetric tetrahedral meshes. It represents 3D shapes by deforming a collection of tetrahedral spheres, with geometric regularizations and constraints that effectively resolve common mesh issues such as irregular triangles, non-manifoldness, and floating artifacts. Experimental results on multi-view and single-view reconstruction highlight TetSphere splatting's superior mesh quality while maintaining competitive reconstruction accuracy compared to state-of-the-art methods. Additionally, TetSphere splatting demonstrates versatility by seamlessly integrating into generative modeling tasks, such as image-to-3D and text-to-3D generation.

ICLR Conference 2025 Conference Paper

VLMaterial: Procedural Material Generation with Large Vision-Language Models

Beichen Li 0005
Rundi Wu
Armando Solar-Lezama
Changxi Zheng
Liang Shi 0003
Bernd Bickel
Wojciech Matusik

Procedural materials, represented as functional node graphs, are ubiquitous in computer graphics for photorealistic material appearance design. They allow users to perform intuitive and precise editing to achieve desired visual appearances. However, creating a procedural material given an input image requires professional knowledge and significant effort. In this work, we leverage the ability to convert procedural materials into standard Python programs and fine-tune a large pre-trained vision-language model (VLM) to generate such programs from input images. To enable effective fine-tuning, we also contribute an open-source procedural material dataset and propose to perform program-level augmentation by prompting another pre-trained large language model (LLM). Through extensive evaluation, we show that our method outperforms previous methods on both synthetic and real-world examples.

ICRA Conference 2024 Conference Paper

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

Yunsheng Tian
Karl D. D. Willis
Bassel Al Omari
Jieliang Luo
Pingchuan Ma 0004
Yichen Li 0004
Farhad Javid
Edward Gu

The automated assembly of complex products requires a system that can automatically plan a physically feasible sequence of actions for assembling many parts together. In this paper, we present ASAP, a physics-based planning approach for automatically generating such a sequence for general-shaped assemblies. ASAP accounts for gravity to design a sequence where each sub-assembly is physically stable with a limited number of parts being held and a support surface. We apply efficient tree search algorithms to reduce the combinatorial complexity of determining such an assembly sequence. The search can be guided by either geometric heuristics or graph neural networks trained on data with simulation labels. Finally, we show the superior performance of ASAP at generating physically realistic assembly sequence plans on a large dataset of hundreds of complex product assemblies. We further demonstrate the applicability of ASAP on both simulation and real-world robotic setups. Project website: asap. csail. mit.edu

ICML Conference 2024 Conference Paper

Boundary Exploration for Bayesian Optimization With Unknown Physical Constraints

Yunsheng Tian
Ane Zuniga
Xinwei Zhang 0001
Johannes P. Dürholt
Payel Das
Jie Chen 0007
Wojciech Matusik
Mina Konakovic-Lukovic

Bayesian optimization has been successfully applied to optimize black-box functions where the number of evaluations is severely limited. However, in many real-world applications, it is hard or impossible to know in advance which designs are feasible due to some physical or system limitations. These issues lead to an even more challenging problem of optimizing an unknown function with unknown constraints. 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. Inspired by this observation, we propose BE-CBO, a new Bayesian optimization method that efficiently explores the boundary between feasible and infeasible designs. To identify the boundary, we learn the constraints with an ensemble of neural networks that outperform the standard Gaussian Processes for capturing complex boundaries. Our method demonstrates superior performance against state-of-the-art methods through comprehensive experiments on synthetic and real-world benchmarks. Code available at: https: //github. com/yunshengtian/BE-CBO

ICLR Conference 2024 Conference Paper

Learning to Jointly Understand Visual and Tactile Signals

Yichen Li 0004
Yilun Du
Chao Liu 0021
Chao Liu 0064
Francis Williams
Michael Foshey
Benjamin Eckart
Jan Kautz

Modeling and analyzing object and shape has been well studied in the past. However, manipulation of these complex tools and articulated objects remains difficult for autonomous agents. Our human hands, however, are dexterous and adaptive. We can easily adapt a manipulation skill on one object to all objects in the class and to other similar classes. Our intuition comes from that there is a close connection between manipulations and topology and articulation of objects. The possible articulation of objects indicates the types of manipulation necessary to operate the object. In this work, we aim to take a manipulation perspective to understand everyday objects and tools. We collect a multi-modal visual-tactile dataset that contains paired full-hand force pressure maps and manipulation videos. We also propose a novel method to learn a cross-modal latent manifold that allow for cross-modal prediction and discovery of latent structure in different data modalities. We conduct extensive experiments to demonstrate the effectiveness of our method.

ICRA Conference 2024 Conference Paper

Liquids Identification and Manipulation via Digitally Fabricated Impedance Sensors

Junyi Zhu 0001
Young Joong Lee
Yiyue Luo
Tianyu Xu 0008
Chao Liu 0021
Daniela Rus
Stefanie Müller 0001
Wojciech Matusik

Despite recent exponential advancements in computer vision and reinforcement learning, it remains challenging for robots to interact with liquids. These challenges are particularly pronounced due to the limitations imposed by opaque containers, transparent liquids, fine-grained splashes, and visual obstructions arising from the robot’s own manipulation activities. Yet, there exists a substantial opportunity for robotics to excel in liquid identification and manipulation, given its potential role in chemical handling in laboratories and various manufacturing sectors such as pharmaceuticals or beverages. In this work, we present a novel approach for liquid class identification and state estimation leveraging electrical impedance sensing. We design and mount a digitally embroidered electrode array to a commercial robot gripper. Coupled with a customized impedance sensing board, we collect data on liquid manipulation with a swept frequency sensing mode and a frequency-specific impedance measuring mode. Our developed learning-based model achieves an accuracy of 93. 33% in classifying 9 different types of liquids (8 liquids + air), and 97. 65% in estimating the liquid state. We investigate the effectiveness of our system with a series of ablation studies. These findings highlight our work as a promising solution for enhancing robotic manipulation in liquid-related tasks.

ICML Conference 2024 Conference Paper

LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discovery

Pingchuan Ma 0002
Tsun-Hsuan Wang
Minghao Guo
Zhiqing Sun
Joshua B. Tenenbaum
Daniela Rus
Chuang Gan 0001
Wojciech Matusik

Large Language Models have recently gained significant attention in scientific discovery for their extensive knowledge and advanced reasoning capabilities. However, they encounter challenges in effectively simulating observational feedback and grounding it with language to propel advancements in physical scientific discovery. Conversely, human scientists undertake scientific discovery by formulating hypotheses, conducting experiments, and revising theories through observational analysis. Inspired by this, we propose to enhance the knowledge-driven, abstract reasoning abilities of LLMs with the computational strength of simulations. We introduce Scientific Generative Agent (SGA), a bilevel optimization framework: LLMs act as knowledgeable and versatile thinkers, proposing scientific hypotheses and reason about discrete components, such as physics equations or molecule structures; meanwhile, simulations function as experimental platforms, providing observational feedback and optimizing via differentiability for continuous parts, such as physical parameters. We conduct extensive experiments to demonstrate our framework’s efficacy in constitutive law discovery and molecular design, unveiling novel solutions that differ from conventional human expectations yet remain coherent upon analysis.

NeurIPS Conference 2024 Conference Paper

MeMo: Meaningful, Modular Controllers via Noise Injection

Megan Tjandrasuwita
Jie Xu
Armando Solar-Lezama
Wojciech Matusik

Robots are often built from standardized assemblies, (e. g. arms, legs, or fingers), but each robot must be trained from scratch to control all the actuators of all the parts together. In this paper we demonstrate a new approach that takes a single robot and its controller as input and produces a set of modular controllers for each of these assemblies such that when a new robot is built from the same parts, its control can be quickly learned by reusing the modular controllers. We achieve this with a framework called MeMo which learns (Me)aningful, (Mo)dular controllers. Specifically, we propose a novel modularity objective to learn an appropriate division of labor among the modules. We demonstrate that this objective can be optimized simultaneously with standard behavior cloning loss via noise injection. We benchmark our framework in locomotion and grasping environments on simple to complex robot morphology transfer. We also show that the modules help in task transfer. On both structure and task transfer, MeMo achieves improved training efficiency to graph neural network and Transformer baselines.

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NeurIPS Conference 2024 Conference Paper

NeuralFluid: Nueral Fluidic System Design and Control with Differentiable Simulation

Yifei Li
Yuchen Sun
Pingchuan Ma
Eftychios Sifakis
Tao Du
Bo Zhu
Wojciech Matusik

We present NeuralFluid, a novel framework to explore neural control and design of complex fluidic systems with dynamic solid boundaries. Our system features a fast differentiable Navier-Stokes solver with solid-fluid interface handling, a low-dimensional differentiable parametric geometry representation, a control-shape co-design algorithm, and gym-like simulation environments to facilitate various fluidic control design applications. Additionally, we present a benchmark of design, control, and learning tasks on high-fidelity, high-resolution dynamic fluid environments that pose challenges for existing differentiable fluid simulators. These tasks include designing the control of artificial hearts, identifying robotic end-effector shapes, and controlling a fluid gate. By seamlessly incorporating our differentiable fluid simulator into a learning framework, we demonstrate successful design, control, and learning results that surpass gradient-free solutions in these benchmark tasks.

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NeurIPS Conference 2024 Conference Paper

Physically Compatible 3D Object Modeling from a Single Image

Minghao Guo
Bohan Wang
Pingchuan Ma
Tianyuan Zhang
Crystal E. Owens
Chuang Gan
Joshua B. Tenenbaum
Kaiming He

We present a computational framework that transforms single images into 3D physical objects. The visual geometry of a physical object in an image is determined by three orthogonal attributes: mechanical properties, external forces, and rest-shape geometry. Existing single-view 3D reconstruction methods often overlook this underlying composition, presuming rigidity or neglecting external forces. Consequently, the reconstructed objects fail to withstand real-world physical forces, resulting in instability or undesirable deformation -- diverging from their intended designs as depicted in the image. Our optimization framework addresses this by embedding physical compatibility into the reconstruction process. We explicitly decompose the three physical attributes and link them through static equilibrium, which serves as a hard constraint, ensuring that the optimized physical shapes exhibit desired physical behaviors. Evaluations on a dataset collected from Objaverse demonstrate that our framework consistently enhances the physical realism of 3D models over existing methods. The utility of our framework extends to practical applications in dynamic simulations and 3D printing, where adherence to physical compatibility is paramount.

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ICML Conference 2024 Conference Paper

Representing Molecules as Random Walks Over Interpretable Grammars

Michael Sun
Minghao Guo
Weize Yuan
Veronika Thost
Crystal Elaine Owens
Aristotle Franklin Grosz
Sharvaa Selvan
Katelyn Zhou

Recent research in molecular discovery has primarily been devoted to small, drug-like molecules, leaving many similarly important applications in material design without adequate technology. These applications often rely on more complex molecular structures with fewer examples that are carefully designed using known substructures. We propose a data-efficient and interpretable model for representing and reasoning over such molecules in terms of graph grammars that explicitly describe the hierarchical design space featuring motifs to be the design basis. We present a novel representation in the form of random walks over the design space, which facilitates both molecule generation and property prediction. We demonstrate clear advantages over existing methods in terms of performance, efficiency, and synthesizability of predicted molecules, and we provide detailed insights into the method’s chemical interpretability.

ICML Conference 2023 Conference Paper

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

Minghao Guo
Veronika Thost
Samuel W. Song
Adithya Balachandran
Payel Das
Jie Chen 0007
Wojciech Matusik

The prediction of molecular properties is a crucial task in the field of material and drug discovery. The potential benefits of using deep learning techniques are reflected in the wealth of recent literature. 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. In this work, we propose a data-efficient property predictor by utilizing a learnable hierarchical molecular grammar that can generate molecules from grammar production rules. Such a grammar induces an explicit geometry of the space of molecular graphs, which provides an informative prior on molecular structural similarity. The property prediction is performed using graph neural diffusion over the grammar-induced geometry. On both small and large datasets, our evaluation shows that this approach outperforms a wide spectrum of baselines, including supervised and pre-trained graph neural networks. We include a detailed ablation study and further analysis of our solution, showing its effectiveness in cases with extremely limited data.

ICML Conference 2023 Conference Paper

Learning Neural Constitutive Laws from Motion Observations for Generalizable PDE Dynamics

Pingchuan Ma 0002
Peter Yichen Chen
Bolei Deng
Joshua B. Tenenbaum
Tao Du 0001
Chuang Gan 0001
Wojciech Matusik

We propose a hybrid neural network (NN) and PDE approach for learning generalizable PDE dynamics from motion observations. Many NN approaches learn an end-to-end model that implicitly models both the governing PDE and constitutive models (or material models). Without explicit PDE knowledge, these approaches cannot guarantee physical correctness and have limited generalizability. We argue that the governing PDEs are often well-known and should be explicitly enforced rather than learned. Instead, constitutive models are particularly suitable for learning due to their data-fitting nature. To this end, we introduce a new framework termed "Neural Constitutive Laws" (NCLaw), which utilizes a network architecture that strictly guarantees standard constitutive priors, including rotation equivariance and undeformed state equilibrium. We embed this network inside a differentiable simulation and train the model by minimizing a loss function based on the difference between the simulation and the motion observation. We validate NCLaw on various large-deformation dynamical systems, ranging from solids to fluids. After training on a single motion trajectory, our method generalizes to new geometries, initial/boundary conditions, temporal ranges, and even multi-physics systems. On these extremely out-of-distribution generalization tasks, NCLaw is orders-of-magnitude more accurate than previous NN approaches. Real-world experiments demonstrate our method’s ability to learn constitutive laws from videos.

ICML Conference 2023 Conference Paper

Learning Preconditioners for Conjugate Gradient PDE Solvers

Yichen Li 0004
Peter Yichen Chen
Tao Du 0001
Wojciech Matusik

Efficient numerical solvers for partial differential equations empower science and engineering. One commonly employed numerical solver is the preconditioned conjugate gradient (PCG) algorithm, whose performance is largely affected by the preconditioner quality. However, designing high-performing preconditioner with traditional numerical methods is highly non-trivial, often requiring problem-specific knowledge and meticulous matrix operations. We present a new method that leverages learning-based approach to obtain an approximate matrix factorization to the system matrix to be used as a preconditioner in the context of PCG solvers. Our high-level intuition comes from the shared property between preconditioners and network-based PDE solvers that excels at obtaining approximate solutions at a low computational cost. Such observation motivates us to represent preconditioners as graph neural networks (GNNs). In addition, we propose a new loss function that rewrites traditional preconditioner metrics to incorporate inductive bias from PDE data distributions, enabling effective training of high-performing preconditioners. We conduct extensive experiments to demonstrate the efficacy and generalizability of our proposed approach on solving various 2D and 3D linear second-order PDEs.

ICLR Conference 2022 Conference Paper

Accelerated Policy Learning with Parallel Differentiable Simulation

Jie Xu 0028
Viktor Makoviychuk
Yashraj Narang
Fabio Ramos 0001
Wojciech Matusik
Animesh Garg
Miles Macklin

Deep reinforcement learning can generate complex control policies, but requires large amounts of training data to work effectively. Recent work has attempted to address this issue by leveraging differentiable simulators. However, inherent problems such as local minima and exploding/vanishing numerical gradients prevent these methods from being generally applied to control tasks with complex contact-rich dynamics, such as humanoid locomotion in classical RL benchmarks. 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. Our learning algorithm alleviates problems with local minima through a smooth critic function, avoids vanishing/exploding gradients through a truncated learning window, and allows many physical environments to be run in parallel. We evaluate our method on classical RL control tasks, and show substantial improvements in sample efficiency and wall-clock time over state-of-the-art RL and differentiable simulation-based algorithms. In addition, we demonstrate the scalability of our method by applying it to the challenging high-dimensional problem of muscle-actuated locomotion with a large action space, achieving a greater than $17\times$ reduction in training time over the best-performing established RL algorithm. More visual results are provided at: https://short-horizon-actor-critic.github.io/.

NeurIPS Conference 2022 Conference Paper

ActionSense: A Multimodal Dataset and Recording Framework for Human Activities Using Wearable Sensors in a Kitchen Environment

Joseph DelPreto
Chao Liu
Yiyue Luo
Michael Foshey
Yunzhu Li
Antonio Torralba
Wojciech Matusik
Daniela Rus

This paper introduces ActionSense, a multimodal dataset and recording framework with an emphasis on wearable sensing in a kitchen environment. It provides rich, synchronized data streams along with ground truth data to facilitate learning pipelines that could extract insights about how humans interact with the physical world during activities of daily living, and help lead to more capable and collaborative robot assistants. The wearable sensing suite captures motion, force, and attention information; it includes eye tracking with a first-person camera, forearm muscle activity sensors, a body-tracking system using 17 inertial sensors, finger-tracking gloves, and custom tactile sensors on the hands that use a matrix of conductive threads. This is coupled with activity labels and with externally-captured data from multiple RGB cameras, a depth camera, and microphones. The specific tasks recorded in ActionSense are designed to highlight lower-level physical skills and higher-level scene reasoning or action planning. They include simple object manipulations (e. g. , stacking plates), dexterous actions (e. g. , peeling or cutting vegetables), and complex action sequences (e. g. , setting a table or loading a dishwasher). The resulting dataset and underlying experiment framework are available at https: //action-sense. csail. mit. edu. Preliminary networks and analyses explore modality subsets and cross-modal correlations. ActionSense aims to support applications including learning from demonstrations, dexterous robot control, cross-modal predictions, and fine-grained action segmentation. It could also help inform the next generation of smart textiles that may one day unobtrusively send rich data streams to in-home collaborative or autonomous robot assistants.

ICRA Conference 2022 Conference Paper

An Integrated Design Pipeline for Tactile Sensing Robotic Manipulators

Lara Zlokapa
Yiyue Luo
Jie Xu 0028
Michael Foshey
Kui Wu 0003
Pulkit Agrawal 0001
Wojciech Matusik

Traditional robotic manipulator design methods require extensive, time-consuming, and manual trial and error to produce a viable design. During this process, engineers often spend their time redesigning or reshaping components as they discover better topologies for the robotic manipula-tor. Tactile sensors, while useful, often complicate the design due to their bulky form factor. We propose an integrated design pipeline to streamline the design and manufacturing of robotic manipulators with knitted, glove-like tactile sensors. The proposed pipeline allows a designer to assemble a collection of modular, open-source components by applying predefined graph grammar rules. The end result is an intuitive design paradigm that allows the creation of new virtual designs of manipulators in a matter of minutes. Our framework allows the designer to fine-tune the manipulator's shape through cage-based geometry deformation. Finally, the designer can select surfaces for adding tactile sensing. Once the manipulator design is finished, the program will automatically generate 3D printing and knitting files for manufacturing. We demonstrate the utility of this pipeline by creating four custom manipulators tested on real-world tasks: screwing in a wing nut, pouring water from a bottle, picking up an egg, and cutting paper with scissors.

IROS Conference 2022 Conference Paper

Automatic Co-Design of Aerial Robots Using a Graph Grammar

Allan Zhao
Tao Du 0001
Jie Xu 0028
Josie Hughes
Juan Salazar
Pingchuan Ma 0002
Wei Wang 0078
Daniela Rus

Unmanned aerial vehicles (UAVs) have broad applications including disaster response, transportation, photography, and mapping. A significant bottleneck in the development of UAVs is the limited availability of automatic tools for task-specific co-design of a UAV's shape and controller. The development of such tools is particularly challenging as UAVs can take many forms, including fixed-wing planes, radial copters, and hybrid topologies, with each class of topology showing different advantages. In this work, we present a computational design pipeline for UAVs based on a graph grammar that can search across a wide range of topologies. Graphs generated by the grammar encode different topologies and component selections, while continuous parameters encode the dimensions and properties of each component. We further augment the shape representation with deformation cages, which allow expressing a variety of wing shapes. Each UAV design is associated with an LQR controller with tunable continuous parameters. To search over this complex discrete and continuous design space, we develop a hybrid algorithm that combines discrete graph search strategies and gradient-based continuous optimization methods using a differentiable UAV simulator. We evaluate our pipeline on a set of simulated flight tasks requiring dynamic motions, showing that it discovers novel UAV designs that outperform canonical UAVs typically made by engineers.

ICLR Conference 2022 Conference Paper

Data-Efficient Graph Grammar Learning for Molecular Generation

Minghao Guo
Veronika Thost
Beichen Li 0005
Payel Das
Jie Chen 0007
Wojciech Matusik

The problem of molecular generation has received significant attention recently. Existing methods are typically based on deep neural networks and require training on large datasets with tens of thousands of samples. In practice, however, the size of class-specific chemical datasets is usually limited (e.g., dozens of samples) due to labor-intensive experimentation and data collection. Another major challenge is to generate only physically synthesizable molecules. 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. In this work, we propose a data-efficient generative model that can be learned from datasets with orders of magnitude smaller sizes than common benchmarks. At the heart of this method is a learnable graph grammar that generates molecules from a sequence of production rules. Without any human assistance, these production rules are automatically constructed from training data. Furthermore, additional chemical knowledge can be incorporated into the model by further grammar optimization. Our learned graph grammar yields state-of-the-art results on generating high-quality molecules for three monomer datasets that contain only ${\sim}20$ samples each. Our approach also achieves remarkable performance in a challenging polymer generation task with $only$ $117$ training samples and is competitive against existing methods using $81$k data points.

ICML Conference 2022 Conference Paper

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

Elvis Nava
John Z. Zhang
Mike Yan Michelis
Tao Du 0001
Pingchuan Ma 0002
Benjamin F. Grewe
Wojciech Matusik
Robert K. Katzschmann

Aquatic locomotion is a classic fluid-structure interaction (FSI) problem of interest to biologists and engineers. Solving the fully coupled FSI equations for incompressible Navier-Stokes and finite elasticity is computationally expensive. Optimizing robotic swimmer design within such a system generally involves cumbersome, gradient-free procedures on top of the already costly simulation. To address this challenge we present a novel, fully differentiable hybrid approach to FSI that combines a 2D direct numerical simulation for the deformable solid structure of the swimmer and a physics-constrained neural network surrogate to capture hydrodynamic effects of the fluid. 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). For the fluid simulation, we use a U-Net architecture trained with a physics-based loss function to predict the flow field at each time step. The pressure and velocity field outputs from the neural network are sampled around the boundary of our swimmer using an immersed boundary method (IBM) to compute its swimming motion accurately and efficiently. We demonstrate the computational efficiency and differentiability of our hybrid simulator on a 2D carangiform swimmer. Due to differentiability, the simulator can be used for computational design of controls for soft bodies immersed in fluids via direct gradient-based optimization.

ICRA Conference 2022 Conference Paper

Graph Grammar-Based Automatic Design for Heterogeneous Fleets of Underwater Robots

Allan Zhao
Jie Xu 0028
Juan Salazar
Wei Wang 0078
Pingchuan Ma 0002
Daniela Rus
Wojciech Matusik

Autonomous underwater vehicles (AUVs) are spe-cialized robots that are commonly used for seafloor surveying and ocean water sampling. Computational design approaches have emerged to reduce the effort required to design both individual AUVs as well as fleets. As the number and scale of underwater missions increases beyond the capabilities of a single vehicle, fleet level design will become more important. Depending on the mission, the optimal fleet may consist of multiple distinct types of AUVs designed to a variety of specifications. Moreover, the AUVs may differ in both continuous parameters (such as battery capacity) and discrete parameters (such as number and model of thrusters). In this work, we present a computational pipeline for designing these heterogeneous AUV fleets. Using a novel shape design space based on a graph grammar and deformation cages, we can express a variety of AUV architectures with different topologies, component selections, and dimensions. We search this space using a combination of discrete graph search and gradient-based continuous optimization, enabled by a differentiable AUV simulator. Finally, we formulate heterogeneous fleet design as a modified knapsack problem, and solve it using an efficient backtracking-based algorithm. We evaluate our pipeline on a simulated mission with nonuniform design requirements-surveying a section of seafloor with varying depth-and show that the best heterogeneous fleet outperforms the best fleet composed of a single vehicle type.

ICLR Conference 2022 Conference Paper

RISP: Rendering-Invariant State Predictor with Differentiable Simulation and Rendering for Cross-Domain Parameter Estimation

Pingchuan Ma 0002
Tao Du 0001
Joshua B. Tenenbaum
Wojciech Matusik
Chuang Gan 0001

This work considers identifying parameters characterizing a physical system's dynamic motion directly from a video whose rendering configurations are inaccessible. Existing solutions require massive training data or lack generalizability to unknown rendering configurations. We propose a novel approach that marries domain randomization and differentiable rendering gradients to address this problem. Our core idea is to train a rendering-invariant state-prediction (RISP) network that transforms image differences into state differences independent of rendering configurations, e.g., lighting, shadows, or material reflectance. To train this predictor, we formulate a new loss on rendering variances using gradients from differentiable rendering. Moreover, we present an efficient, second-order method to compute the gradients of this loss, allowing it to be integrated seamlessly into modern deep learning frameworks. We evaluate our method in rigid-body and deformable-body simulation environments using four tasks: state estimation, system identification, imitation learning, and visuomotor control. We further demonstrate the efficacy of our approach on a real-world example: inferring the state and action sequences of a quadrotor from a video of its motion sequences. Compared with existing methods, our approach achieves significantly lower reconstruction errors and has better generalizability among unknown rendering configurations.

IROS Conference 2022 Conference Paper

Sim2Real for Soft Robotic Fish via Differentiable Simulation

John Z. Zhang
Yu Zhang
Pingchuan Ma 0002
Elvis Nava
Tao Du 0001
Philip Arm
Wojciech Matusik
Robert K. Katzschmann

Accurate simulation of soft mechanisms under dynamic actuation is critical for the design of soft robots. We address this gap with our differentiable simulation tool by learning the material parameters of our soft robotic fish. On the example of a soft robotic fish, we demonstrate an experimentally-verified, fast optimization pipeline for learning the material parameters from quasi-static data via differentiable simulation and apply it to the prediction of dynamic performance. Our method identifies physically plausible Young's moduli for various soft silicone elastomers and stiff acetal copolymers used in creation of our three different robotic fish tail designs. We show that our method is compatible with varying internal geometry of the actuators, such as the number of hollow cavities. Our framework allows high fidelity prediction of dynamic behavior for composite bi-morph bending structures in real hardware to millimeter-accuracy and within 3% error normalized to actuator length. We provide a differentiable and robust estimate of the thrust force using a neural network thrust predictor; this estimate allows for accurate modeling of our experimental setup measuring bollard pull. This work presents a prototypical hardware and simulation problem solved using our differentiable framework; the framework can be applied to higher dimensional parameter inference, learning control policies, and computational design due to its differentiable character.

IROS Conference 2021 Conference Paper

Dynamic Modeling of Hand-Object Interactions via Tactile Sensing

Qiang Zhang
Yunzhu Li
Yiyue Luo
Wan Shou
Michael Foshey
Junchi Yan
Joshua B. Tenenbaum
Wojciech Matusik

Tactile sensing is critical for humans to perform everyday tasks. While significant progress has been made in analyzing object grasping from vision, it remains unclear how we can utilize tactile sensing to reason about and model the dynamics of hand-object interactions. In this work, we employ a high-resolution tactile glove to perform four different interactive activities on a diversified set of objects. We propose a framework aiming at predicting the 3d locations of both the hand and the object purely from the touch data by combining a predictive model and a contrastive learning module. This framework can reason about the interaction patterns from the tactile data, hallucinate the changes in the environment, esti-mate the uncertainty of the prediction, and generalize to unseen objects. We also provide detailed ablation studies regarding different system designs as well as visualizations of the predicted trajectories. 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.

NeurIPS Conference 2021 Conference Paper

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

Jagdeep Bhatia
Holly Jackson
Yunsheng Tian
Jie Xu
Wojciech Matusik

Both the design and control of a robot play equally important roles in its task performance. However, while optimal control is well studied in the machine learning and robotics community, less attention is placed on finding the optimal robot design. This is mainly because co-optimizing design and control in robotics is characterized as a challenging problem, and more importantly, a comprehensive evaluation benchmark for co-optimization does not exist. In this paper, we propose Evolution Gym, the first large-scale benchmark for co-optimizing the design and control of soft robots. In our benchmark, each robot is composed of different types of voxels (e. g. , soft, rigid, actuators), resulting in a modular and expressive robot design space. Our benchmark environments span a wide range of tasks, including locomotion on various types of terrains and manipulation. Furthermore, we develop several robot co-evolution algorithms by combining state-of-the-art design optimization methods and deep reinforcement learning techniques. Evaluating the algorithms on our benchmark platform, we observe robots exhibiting increasingly complex behaviors as evolution progresses, with the best evolved designs solving many of our proposed tasks. Additionally, even though robot designs are evolved autonomously from scratch without prior knowledge, they often grow to resemble existing natural creatures while outperforming hand-designed robots. Nevertheless, all tested algorithms fail to find robots that succeed in our hardest environments. This suggests that more advanced algorithms are required to explore the high-dimensional design space and evolve increasingly intelligent robots -- an area of research in which we hope Evolution Gym will accelerate progress. Our website with code, environments, documentation, and tutorials is available at http: //evogym. csail. mit. edu/.

ICRA Conference 2021 Conference Paper

Multi-Objective Graph Heuristic Search for Terrestrial Robot Design

Jie Xu 0028
Andrew Spielberg
Allan Zhao
Daniela Rus
Wojciech Matusik

We present methods for co-designing rigid robots over control and morphology (including discrete topology) over multiple objectives. Previous work has addressed problems in single-objective robot co-design or multi-objective control. However, the joint multi-objective co-design problem is extremely important for generating capable, versatile, algorithmically designed robots. In this work, we present Multi-Objective Graph Heuristic Search, which extends a single-objective graph heuristic search from previous work to enable a highly efficient multi-objective search in a combinatorial design topology space. Core to this approach, we introduce a new universal, multiobjective heuristic function based on graph neural networks that is able to effectively leverage learned information between different task trade-offs. We demonstrate our approach on six combinations of seven terrestrial locomotion and design tasks, including one three-objective example. We compare the captured Pareto fronts across different methods and demonstrate that our multi-objective graph heuristic search quantitatively and qualitatively outperforms other techniques.

NeurIPS Conference 2020 Conference Paper

Diversity-Guided Multi-Objective Bayesian Optimization With Batch Evaluations

Mina Konakovic Lukovic
Yunsheng Tian
Wojciech Matusik

Many science, engineering, and design optimization problems require balancing the trade-offs between several conflicting objectives. The objectives are often black-box functions whose evaluations are time-consuming and costly. Multi-objective Bayesian optimization can be used to automate the process of discovering the set of optimal solutions, called Pareto-optimal, while minimizing the number of performed evaluations. To further reduce the evaluation time in the optimization process, testing of several samples in parallel can be deployed. We propose a novel multi-objective Bayesian optimization algorithm that iteratively selects the best batch of samples to be evaluated in parallel. Our algorithm approximates and analyzes a piecewise-continuous Pareto set representation. This representation allows us to introduce a batch selection strategy that optimizes for both hypervolume improvement and diversity of selected samples in order to efficiently advance promising regions of the Pareto front. Experiments on both synthetic test functions and real-world benchmark problems show that our algorithm predominantly outperforms relevant state-of-the-art methods. Code is available at https: //github. com/yunshengtian/DGEMO.

ICML Conference 2020 Conference Paper

Efficient Continuous Pareto Exploration in Multi-Task Learning

Pingchuan Ma 0002
Tao Du 0001
Wojciech Matusik

Tasks in multi-task learning often correlate, conflict, or even compete with each other. As a result, a single solution that is optimal for all tasks rarely exists. Recent papers introduced the concept of Pareto optimality to this field and directly cast multi-task learning as multi-objective optimization problems, but solutions returned by existing methods are typically finite, sparse, and discrete. 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. We scale up theoretical results in multi-objective optimization to modern machine learning problems by proposing a sample-based sparse linear system, for which standard Hessian-free solvers in machine learning can be applied. We compare our method to the state-of-the-art algorithms and demonstrate its usage of analyzing local Pareto sets on various multi-task classification and regression problems. The experimental results confirm that our algorithm reveals the primary directions in local Pareto sets for trade-off balancing, finds more solutions with different trade-offs efficiently, and scales well to tasks with millions of parameters.

ICML Conference 2020 Conference Paper

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

Jie Xu 0028
Yunsheng Tian
Pingchuan Ma 0002
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). While extensive research in multi-objective reinforcement learning (MORL) has been conducted to tackle such problems, multi-objective optimization for complex continuous robot control is still under-explored. In this work, we propose an efficient evolutionary learning algorithm to find the Pareto set approximation for continuous robot control problems, by extending a state-of-the-art RL algorithm and presenting a novel prediction model to guide the learning process. In addition to efficiently discovering the individual policies on the Pareto front, we construct a continuous set of Pareto-optimal solutions by Pareto analysis and interpolation. Furthermore, we design seven multi-objective RL environments with continuous action space, which is the first benchmark platform to evaluate MORL algorithms on various robot control problems. We test the previous methods on the proposed benchmark problems, and the experiments show that our approach is able to find a much denser and higher-quality set of Pareto policies than the existing algorithms.

ICRA Conference 2019 Conference Paper

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

Yuanming Hu
Jiancheng Liu
Andrew Spielberg
Joshua B. Tenenbaum
William T. Freeman
Jiajun Wu 0001
Daniela Rus
Wojciech Matusik

Physical simulators have been widely used in robot planning and control. Among them, differentiable simulators are particularly favored, as they can be incorporated into gradient-based optimization algorithms that are efficient in solving inverse problems such as optimal control and motion planning. Therefore, rigid body simulators and recently their differentiable variants are studied extensively. Simulating deformable objects is, however, more challenging compared to rigid body dynamics. The underlying physical laws of deformable objects are more complex, and the resulting systems have orders of magnitude more degrees of freedom and there-fore they are significantly more computationally expensive to simulate. Computing gradients with respect to physical design or controller parameters is typically even more computationally challenging. In this paper, we propose a real-time, differentiable hybrid Lagrangian-Eulerian physical simulator for deformable objects, ChainQueen, based on the Moving Least Squares Material Point Method (MLS-MPM). MLS-MPM can simulate deformable objects with collisions and can be seamlessly incorporated into soft robotic systems. We demonstrate that our simulator achieves high precision in both forward simulation and backward gradient computation. We have successfully employed it in a diverse set of inference, control and co-design tasks for soft robotics.

NeurIPS Conference 2019 Conference Paper

Learning-In-The-Loop Optimization: End-To-End Control And Co-Design Of Soft Robots Through Learned Deep Latent Representations

Andrew Spielberg
Allan Zhao
Yuanming Hu
Tao Du
Wojciech Matusik
Daniela Rus

Soft robots have continuum solid bodies that can deform in an infinite number of ways. Controlling soft robots is very challenging as there are no closed form solutions. We present a learning-in-the-loop co-optimization algorithm in which a latent state representation is learned as the robot figures out how to solve the task. Our solution marries hybrid particle-grid-based simulation with deep, variational convolutional autoencoder architectures that can capture salient features of robot dynamics with high efficacy. We demonstrate our dynamics-aware feature learning algorithm on both 2D and 3D soft robots, and show that it is more robust and faster converging than the dynamics-oblivious baseline. We validate the behavior of our algorithm with visualizations of the learned representation.

ICML Conference 2019 Conference Paper

Neural Inverse Knitting: From Images to Manufacturing Instructions

Alexandre Kaspar
Tae Hyun Oh
Liane Makatura
Petr Kellnhofer
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. We propose to tackle this problem by directly learning to synthesize regular machine instructions from real images. We create a cured dataset of real samples with their instruction counterpart and propose to use synthetic images to augment it in a novel way. We theoretically motivate our data mixing framework and show empirical results suggesting that making real images look more synthetic is beneficial in our problem setup.

ICRA Conference 2018 Conference Paper

Robot Assisted Carpentry for Mass Customization

Jeffrey I. Lipton
Adriana Schulz
Andrew Spielberg
Luite Trueba
Wojciech Matusik
Daniela Rus

Despite the ubiquity of carpentered items, the customization of carpentered items remains labor intensive. The generation of laymen editable templates for carpentry is difficult. Current design tools rely heavily on CNC fabrication, limiting applicability. We develop a template based system for carpentry and a robotic fabrication system using mobile robots and standard carpentry tools. Our end-to-end design and fabrication tool democratizes design and fabrication of carpentered items. Our method combines expert knowledge for template design, allows laymen users to customize and verify specific designs, and uses robotics system to fabricate parts. We validate our system using multiple designs to make customizable, verifiable templates and fabrication plans and show an end-to-end example that was designed, manufactured, and assembled using our tools.