1 citations · 1 across the 11 of their papers we have counts for
11 papers
PhysCoRe: Physics-Corrected Residual World Models for Material-Aware Deformable Dynamics
Haocheng Yin, Shuohan Tao, Yongsheng Chen +1
Predicting how deformable objects evolve under robotic manipulation is a longstanding challenge. Existing approaches typically rely on per-object optimization to fit material param…
WestWorld: A Knowledge-Encoded Scalable Trajectory World Model for Diverse Robotic Systems
Yuchen Wang, Jiangtao Kong, Sizhe Wei +6
Trajectory world models play a crucial role in robotic dynamics learning, planning, and control. While recent works have explored trajectory world models for diverse robotic system…
A Generalizable Physics-guided Causal Model for Trajectory Prediction in Autonomous Driving
Zhenyu Zong, Yuchen Wang, Haohong Lin +2
Trajectory prediction for traffic agents is critical for safe autonomous driving. However, achieving effective zero-shot generalization in previously unseen domains remains a signi…
ShelfGaussian: Shelf-Supervised Open-Vocabulary Gaussian-based 3D Scene Understanding
Lingjun Zhao, Yandong Luo, James Hays +1
We introduce ShelfGaussian, an open-vocabulary multi-modal Gaussian-based 3D scene understanding framework supervised by off-the-shelf vision foundation models (VFMs). Gaussian-bas…
Beyond Topology: A Morphological Symmetry Graph Representation for Locomotion Policy Learning
Sizhe Wei, Xulin Chen, Fengze Xie +3
Reinforcement learning has enabled impressive locomotion skills on articulated robots, but common policy representations remain only weakly aligned with robot physics. Generic netw…
Diffusion-Denoised Hyperspectral Gaussian Splatting
Sunil Kumar Narayanan, Lingjun Zhao, Lu Gan +1
Hyperspectral imaging (HSI) has been widely used in agricultural applications for non-destructive estimation of plant nutrient composition and precise quantification of sample nutr…
GaussianFormer3D: Multi-Modal Gaussian-based Semantic Occupancy Prediction with 3D Deformable Attention
Lingjun Zhao, Sizhe Wei, James Hays +1
3D semantic occupancy prediction is essential for achieving safe, reliable autonomous driving and robotic navigation. Compared to camera-only perception systems, multi-modal pipeli…
Morphological-Symmetry-Equivariant Heterogeneous Graph Neural Network for Robotic Dynamics Learning
Fengze Xie, Sizhe Wei, Yue Song +2
We present a morphological-symmetry-equivariant heterogeneous graph neural network, namely MS-HGNN, for robotic dynamics learning, that integrates robotic kinematic structures and…
FetchBench: A Simulation Benchmark for Robot Fetching
Beining Han, Meenal Parakh, Derek Geng +3
Fetching, which includes approaching, grasping, and retrieving, is a critical challenge for robot manipulation tasks. Existing methods primarily focus on table-top scenarios, which…
A Safety-Critical Framework for UGVs in Complex Environments: A Data-Driven Discrepancy-Aware Approach
Skylar X. Wei, Lu Gan, Joel W. Burdick
This work presents a novel data-driven multi-layered planning and control framework for the safe navigation of a class of unmanned ground vehicles (UGVs) in the presence of unknown…
RGB-X Object Detection via Scene-Specific Fusion Modules
Sri Aditya Deevi, Connor Lee, Lu Gan +3
Multimodal deep sensor fusion has the potential to enable autonomous vehicles to visually understand their surrounding environments in all weather conditions. However, existing dee…