Chao Xu

Planning Oriented 3D Scene Completion via Coupled TUDF Occupancy Representation Learning from Partial Observations

Partial observability remains a fundamental challenge in robotic navigation, where limited sensor coverage and occlusions leave large portions of the environment unobserved. Existing scene completion methods primarily focus on improving incomplete mapping or reconstructing partially observed 3D structures, but rarely i...

A Frequency-guided Sparse Fusion Algorithm for Multispectral Object Detection in Complex Environments

Trajectory-Level Mode Guidance for Controllable Diffusion-Based Multi-Robot Motion Planning

Motion planning often admits multiple feasible solutions, making multimodal generation valuable, particularly for flexible multi-robot coordination. Diffusion models naturally learn such trajectory distributions, yet incorporating coarse and partial trajectory priors without restricting generation remains challenging....

Learning Provable Neural Network Observer for Uncertain Dynamical Systems

A novel two-stage training framework for provably stable neural network observers that decouples the optimization into a point-guided Lyapunov pre-training phase, which rapidly achieves high estimation accuracy and local stability over sampled states and an LMI fine-tuning phase that efficiently satisfies a strict glob...

An efficient graph-transformer operator for learning physical dynamics with manifolds embedding

An efficient graph-transformer operator, named PhysGTO, is shown for learning physical dynamics through explicit manifold embeddings in both physical and latent spaces, showing strong flexibility, scalability, and generalization across diverse physical systems.

DART: Decoded Attention over Recurrent States for Efficient Long-Context Sequence Modeling

DART (Decoded Attention over Recurrent sTates), which retains the chunk state contributions produced by the Mamba-2 chunked scan as chunk state memories, decodes token-conditioned keys and values from these memories, and performs state-memory attention (SMA) over the resulting KV pairs.