Search Results for author:
Found 19 papers, 5 papers with code
QuasiSim: Parameterized Quasi-Physical Simulators for Dexterous Manipulations Transfer
We explore the dexterous manipulation transfer problem by designing simulators.
EGraFFBench: Evaluation of Equivariant Graph Neural Network Force Fields for Atomistic Simulations
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
Learning Neural Constitutive Laws From Motion Observations for Generalizable PDE Dynamics
Many NN approaches learn an end-to-end model that implicitly models both the governing PDE and constitutive models (or material models).
DexDeform: Dexterous Deformable Object Manipulation with Human Demonstrations and Differentiable Physics
Reinforcement learning approaches for dexterous rigid object manipulation would struggle in this setting due to the complexity of physics interaction with deformable objects.
RISP: Rendering-Invariant State Predictor with Differentiable Simulation and Rendering for Cross-Domain Parameter Estimation
To train this predictor, we formulate a new loss on rendering variances using gradients from differentiable rendering.
Contact Points Discovery for Soft-Body Manipulations with Differentiable Physics
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.
Fast Aquatic Swimmer Optimization with Differentiable Projective Dynamics and Neural Network Hydrodynamic Models
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).
Dynamic Visual Reasoning by Learning Differentiable Physics Models from Video and Language
This is achieved by seamlessly integrating three components: a visual perception module, a concept learner, and a differentiable physics engine.
Sim2Real for Soft Robotic Fish via Differentiable Simulation
Accurate simulation of soft mechanisms under dynamic actuation is critical for the design of soft robots.
DiffCloth: Differentiable Cloth Simulation with Dry Frictional Contact
This work presents a differentiable cloth simulator whose additional gradient information facilitates cloth-related applications.
PlasticineLab: A Soft-Body Manipulation Benchmark with Differentiable Physics
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.
DiffAqua: A Differentiable Computational Design Pipeline for Soft Underwater Swimmers with Shape Interpolation
The computational design of soft underwater swimmers is challenging because of the high degrees of freedom in soft-body modeling.
DiffPD: Differentiable Projective Dynamics
Inspired by Projective Dynamics (PD), we present Differentiable Projective Dynamics (DiffPD), an efficient differentiable soft-body simulator based on PD with implicit time integration.
Fusion 360 Gallery: A Dataset and Environment for Programmatic CAD Construction from Human Design Sequences
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.
Fusion 360 Gallery: A Dataset and Environment for Programmatic CAD Reconstruction
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.
Efficient Continuous Pareto Exploration in Multi-Task Learning
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.
Learning-In-The-Loop Optimization: End-To-End Control And Co-Design Of Soft Robots Through Learned Deep Latent Representations
We validate the behavior of our algorithm with visualizations of the learned representation.
D3PG: Deep Differentiable Deterministic Policy Gradients
Over the last decade, two competing control strategies have emerged for solving complex control tasks with high efficacy.
Self-driving scale car trained by Deep reinforcement learning
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.
