Search Results for author: Zhiao Huang

Found 13 papers, 6 papers with code

RoboCraft: Learning to See, Simulate, and Shape Elasto-Plastic Objects with Graph Networks

no code implementations5 May 2022 Haochen Shi, Huazhe Xu, Zhiao Huang, Yunzhu Li, Jiajun Wu

Our learned model-based planning framework is comparable to and sometimes better than human subjects on the tested tasks.

Contact Points Discovery for Soft-Body Manipulations with Differentiable Physics

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.

Learning Multi-Object Dynamics with Compositional Neural Radiance Fields

no code implementations24 Feb 2022 Danny Driess, Zhiao Huang, Yunzhu Li, Russ Tedrake, Marc Toussaint

The latent vectors parameterize individual NeRF models from which the scene can be reconstructed and rendered from novel viewpoints.

ManiSkill: Generalizable Manipulation Skill Benchmark with Large-Scale Demonstrations

3 code implementations30 Jul 2021 Tongzhou Mu, Zhan Ling, Fanbo Xiang, Derek Yang, Xuanlin Li, Stone Tao, Zhiao Huang, Zhiwei Jia, Hao Su

Here we propose SAPIEN Manipulation Skill Benchmark (ManiSkill) to benchmark manipulation skills over diverse objects in a full-physics simulator.

PlasticineLab: A Soft-Body Manipulation Benchmark with Differentiable Physics

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.

Towards Scale-Invariant Graph-related Problem Solving by Iterative Homogeneous GNNs

no code implementations NeurIPS 2020 Hao Tang, Zhiao Huang, Jiayuan Gu, Bao-liang Lu, Hao Su

Current graph neural networks (GNNs) lack generalizability with respect to scales (graph sizes, graph diameters, edge weights, etc..) when solving many graph analysis problems.

Towards Scale-Invariant Graph-related Problem Solving by Iterative Homogeneous Graph Neural Networks

no code implementations26 Oct 2020 Hao Tang, Zhiao Huang, Jiayuan Gu, Bao-liang Lu, Hao Su

Current graph neural networks (GNNs) lack generalizability with respect to scales (graph sizes, graph diameters, edge weights, etc..) when solving many graph analysis problems.

Mapping State Space using Landmarks for Universal Goal Reaching

1 code implementation NeurIPS 2019 Zhiao Huang, Fangchen Liu, Hao Su

An agent that has well understood the environment should be able to apply its skills for any given goals, leading to the fundamental problem of learning the Universal Value Function Approximator (UVFA).

Object-Oriented Dynamics Predictor

1 code implementation NeurIPS 2018 Guangxiang Zhu, Zhiao Huang, Chongjie Zhang

Generalization has been one of the major challenges for learning dynamics models in model-based reinforcement learning.

Model-based Reinforcement Learning

Coarse-to-fine Face Alignment with Multi-Scale Local Patch Regression

no code implementations16 Nov 2015 Zhiao Huang, Erjin Zhou, Zhimin Cao

Facial landmark localization plays an important role in face recognition and analysis applications.

Face Alignment Face Recognition

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