553 citations · 783 across the 28 of their papers we have counts for
11 papers · 1 filter
Coupled Distributional Random Expert Distillation for World Model Online Imitation Learning
Shangzhe Li, Zhiao Huang, Hao Su
Imitation Learning (IL) has achieved remarkable success across various domains, including robotics, autonomous driving, and healthcare, by enabling agents to learn complex behavior…
Reward-free World Models for Online Imitation Learning
Shangzhe Li, Zhiao Huang, Hao Su
Imitation learning (IL) enables agents to acquire skills directly from expert demonstrations, providing a compelling alternative to reinforcement learning. However, prior online IL…
DiffVL: Scaling Up Soft Body Manipulation using Vision-Language Driven Differentiable Physics
Zhiao Huang, Feng Chen, Yewen Pu +3
Combining gradient-based trajectory optimization with differentiable physics simulation is an efficient technique for solving soft-body manipulation problems. Using a well-crafted…
Reparameterized Policy Learning for Multimodal Trajectory Optimization
Zhiao Huang, Litian Liang, Zhan Ling +3
We investigate the challenge of parametrizing policies for reinforcement learning (RL) in high-dimensional continuous action spaces. Our objective is to develop a multimodal policy…
Chain-of-Thought Predictive Control
Zhiwei Jia, Vineet Thumuluri, Fangchen Liu +3
We study generalizable policy learning from demonstrations for complex low-level control (e.g., contact-rich object manipulations). We propose a novel hierarchical imitation learni…
Abstract-to-Executable Trajectory Translation for One-Shot Task Generalization
Stone Tao, Xiaochen Li, Tongzhou Mu +3
Training long-horizon robotic policies in complex physical environments is essential for many applications, such as robotic manipulation. However, learning a policy that can genera…
DiffSkill: Skill Abstraction from Differentiable Physics for Deformable Object Manipulations with Tools
Xingyu Lin, Zhiao Huang, Yunzhu Li +3
We consider the problem of sequential robotic manipulation of deformable objects using tools. Previous works have shown that differentiable physics simulators provide gradients to…
ManiSkill: Generalizable Manipulation Skill Benchmark with Large-Scale Demonstrations
Tongzhou Mu, Zhan Ling, Fanbo Xiang +6
Object manipulation from 3D visual inputs poses many challenges on building generalizable perception and policy models. However, 3D assets in existing benchmarks mostly lack the di…
PlasticineLab: A Soft-Body Manipulation Benchmark with Differentiable Physics
Zhiao Huang, Yuanming Hu, Tao Du +4
Simulated virtual environments serve as one of the main driving forces behind developing and evaluating skill learning algorithms. However, existing environments typically only sim…
Towards Scale-Invariant Graph-related Problem Solving by Iterative Homogeneous Graph Neural Networks
Hao Tang, Zhiao Huang, Jiayuan Gu +2
Current graph neural networks (GNNs) lack generalizability with respect to scales (graph sizes, graph diameters, edge weights, etc..) when solving many graph analysis problems. Tak…
Mapping State Space using Landmarks for Universal Goal Reaching
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 Functio…