6 papers
SLIP-VLA: Single-Step Latent Imagination for Policy Learning in Vision-Language-Action Models
Tianfu Li, Haoxuan Xu, Wenbo Chen +7
Vision-Language-Action models are increasingly effective for robotic manipulation, yet most predict actions directly from current observations without explicitly modeling future sc…
HCSG: Human-Centric Semantic-Geometric Reasoning for Vision-Language Navigation
Haoxuan Xu, Tianfu Li, Wenbo Chen +7
VLN has achieved remarkable progress by scaling data and model capacity. However, the assumption of a static environment breaks down in real-world indoor scenarios, where robots in…
Mask World Model: Predicting What Matters for Robust Robot Policy Learning
Yunfan Lou, Xiaowei Chi, Xiaojie Zhang +9
World models derived from large-scale video generative pre-training have emerged as a promising paradigm for generalist robot policy learning. However, standard approaches often fo…
GGD-SLAM: Monocular 3DGS SLAM Powered by Generalizable Motion Model for Dynamic Environments
Yi Liu, Haoxuan Xu, Hongbo Duan +5
Visual SLAM algorithms achieve significant improvements through the exploration of 3D Gaussian Splatting (3DGS) representations, particularly in generating high-fidelity dense maps…
PNav: End-to-End Perception, Prediction and Planning for Vision-and-Language Navigation
Tianfu Li, Wenbo Chen, Haoxuan Xu +2
In Vision-and-Language Navigation (VLN), an agent is required to plan a path to the target specified by the language instruction, using its visual observations. Consequently, preva…
Wavelet Policy: Imitation Learning in the Scale Domain with World Prior Memory
Changchuan Yang, Haoxuan Xu, Yuhang Dong +3
Conventional visuomotor imitation learning usually predicts future robot actions directly in the time domain. Such formulations often have limited physical scene awareness and weak…