3 citations · 11 across the 16 of their papers we have counts for
6 papers · 1 filter
MO-DDN: A Coarse-to-Fine Attribute-based Exploration Agent for Multi-object Demand-driven Navigation
Hongcheng Wang, Peiqi Liu, Wenzhe Cai +3
The process of satisfying daily demands is a fundamental aspect of humans' daily lives. With the advancement of embodied AI, robots are increasingly capable of satisfying human dem…
Canonical Representation and Force-Based Pretraining of 3D Tactile for Dexterous Visuo-Tactile Policy Learning
Tianhao Wu, Jinzhou Li, Jiyao Zhang +2
Tactile sensing plays a vital role in enabling robots to perform fine-grained, contact-rich tasks. However, the high dimensionality of tactile data, due to the large coverage on de…
GFPack++: Attention-Driven Gradient Fields for Optimizing 2D Irregular Packing
Tianyang Xue, Lin Lu, Yang Liu +5
2D irregular packing is a classic combinatorial optimization problem with various applications, such as material utilization and texture atlas generation. Due to its NP-hard nature…
Omni6DPose: A Benchmark and Model for Universal 6D Object Pose Estimation and Tracking
Jiyao Zhang, Weiyao Huang, Bo Peng +5
6D Object Pose Estimation is a crucial yet challenging task in computer vision, suffering from a significant lack of large-scale datasets. This scarcity impedes comprehensive evalu…
SocialGFs: Learning Social Gradient Fields for Multi-Agent Reinforcement Learning
Qian Long, Fangwei Zhong, Mingdong Wu +2
Multi-agent systems (MAS) need to adaptively cope with dynamic environments, changing agent populations, and diverse tasks. However, most of the multi-agent systems cannot easily h…
Dexterous Functional Pre-Grasp Manipulation with Diffusion Policy
Tianhao Wu, Yunchong Gan, Mingdong Wu +4
In real-world scenarios, objects often require repositioning and reorientation before they can be grasped, a process known as pre-grasp manipulation. Learning universal dexterous f…