36 citations · 61 across the 10 of their papers we have counts for
12 papers
Point-Level Refinable Semantic Mapping for Spatial Understanding
Taimeng Fu, Yi Du, Zitong Zhan +4
Open-vocabulary 3D semantic maps have emerged as a key spatial memory representation for long-horizon robot autonomy, enabling robots to reason about objects, their spatial relatio…
HAVANA: A Hierarchical Video-Action Model for Vision-Language Navigation
Dongzhihan Wang, Yi Du, Chen Wang +2
Vision-Language Navigation (VLN) requires long-horizon instruction-consistent planning and short-horizon local planning in current geometry. Video-action models (VAMs) offer a prom…
GSMem: 3D Gaussian Splatting as Persistent Spatial Memory for Zero-Shot Embodied Exploration and Reasoning
Yiren Lu, Yi Du, Disheng Liu +3
Effective embodied exploration requires agents to accumulate and retain spatial knowledge over time. However, existing scene representations, such as discrete scene graphs or stati…
Vision to Geometry: 3D Spatial Memory for Sequential Embodied MLLM Reasoning and Exploration
Zhongyi Cai, Yi Du, Chen Wang +1
Embodied agents are expected to assist humans by actively exploring unknown environments and reasoning about spatial contexts. When deployed in real life, agents often face sequent…
Vision-Language Memory for Spatial Reasoning
Zuntao Liu, Yi Du, Taimeng Fu +3
Spatial reasoning is a critical capability for intelligent robots, yet current vision-language models (VLMs) still fall short of human-level performance in video-based spatial reas…
Fast Task Planning with Neuro-Symbolic Relaxation
Qiwei Du, Bowen Li, Yi Du +5
Real-world task planning requires long-horizon reasoning over large sets of objects with complex relationships and attributes, leading to a combinatorial explosion for classical sy…
SuperPC: A Single Diffusion Model for Point Cloud Completion, Upsampling, Denoising, and Colorization
Yi Du, Zhipeng Zhao, Shaoshu Su +4
Point cloud (PC) processing tasks-such as completion, upsampling, denoising, and colorization-are crucial in applications like autonomous driving and 3D reconstruction. Despite sub…
AirRoom: Objects Matter in Room Reidentification
Runmao Yao, Yi Du, Zhuoqun Chen +2
Room reidentification (ReID) is a challenging yet essential task with numerous applications in fields such as augmented reality (AR) and homecare robotics. Existing visual place re…
VL-Nav: Neuro-Symbolic Reasoning-based Vision-Language Navigation
Yi Du, Taimeng Fu, Zhipeng Zhao +6
Navigating unseen, large-scale environments based on complex and abstract human instructions remains a formidable challenge for autonomous mobile robots. Addressing this requires r…
Imperative Learning: A Self-supervised Neuro-Symbolic Learning Framework for Robot Autonomy
Chen Wang, Kaiyi Ji, Junyi Geng +16
Data-driven methods such as reinforcement and imitation learning have achieved remarkable success in robot autonomy. However, their data-centric nature still hinders them from gene…
PhysORD: A Neuro-Symbolic Approach for Physics-infused Motion Prediction in Off-road Driving
Zhipeng Zhao, Bowen Li, Yi Du +2
Motion prediction is critical for autonomous off-road driving, however, it presents significantly more challenges than on-road driving because of the complex interaction between th…
PyPose: A Library for Robot Learning with Physics-based Optimization
Chen Wang, Dasong Gao, Kuan Xu +34
Deep learning has had remarkable success in robotic perception, but its data-centric nature suffers when it comes to generalizing to ever-changing environments. By contrast, physic…