1 citations · 1 across the 11 of their papers we have counts for
11 papers
WaterSplat-SLAM: Photorealistic Monocular SLAM in Underwater Environment
Kangxu Wang, Shaofeng Zou, Chenxing Jiang +4
Underwater monocular SLAM is a challenging problem with applications from autonomous underwater vehicles to marine archaeology. However, existing underwater SLAM methods struggle t…
MG-Grasp: Metric-Scale Geometric 6-DoF Grasping Framework with Sparse RGB Observations
Kangxu Wang, Siang Chen, Chenxing Jiang +3
Single-view RGB-D grasp detection remains a common choice in 6-DoF robotic grasping systems, which typically requires a depth sensor. While RGB-only 6-DoF grasp methods has been st…
Rainbow Delay Compensation: A Multi-Agent Reinforcement Learning Framework for Mitigating Delayed Observation
Songchen Fu, Siang Chen, Shaojing Zhao +3
In real-world multi-agent systems (MASs), observation delays are ubiquitous, preventing agents from making decisions based on the environment's true state. An individual agent's lo…
GAP-RL: Grasps As Points for RL Towards Dynamic Object Grasping
Pengwei Xie, Siang Chen, Qianrun Chen +5
Dynamic grasping of moving objects in complex, continuous motion scenarios remains challenging. Reinforcement Learning (RL) has been applied in various robotic manipulation tasks,…
Efficient End-to-End 6-Dof Grasp Detection Framework for Edge Devices with Hierarchical Heatmaps and Feature Propagation
Kaiqin Yang, Yixiang Dai, Guijin Wang +1
6-DoF grasp detection is critically important for the advancement of intelligent embodied systems, as it provides feasible robot poses for object grasping. Various methods have bee…
Target-Oriented Object Grasping via Multimodal Human Guidance
Pengwei Xie, Siang Chen, Dingchang Hu +3
In the context of human-robot interaction and collaboration scenarios, robotic grasping still encounters numerous challenges. Traditional grasp detection methods generally analyze…
Region-aware Grasp Framework with Normalized Grasp Space for Efficient 6-DoF Grasping
Siang Chen, Pengwei Xie, Wei Tang +3
A series of region-based methods succeed in extracting regional features and enhancing grasp detection quality. However, faced with a cluttered scene with potential collision, the…
Part-Guided 3D RL for Sim2Real Articulated Object Manipulation
Pengwei Xie, Rui Chen, Siang Chen +6
Manipulating unseen articulated objects through visual feedback is a critical but challenging task for real robots. Existing learning-based solutions mainly focus on visual afforda…
Category-Agnostic Pose Estimation for Point Clouds
Bowen Liu, Wei Liu, Siang Chen +2
The goal of object pose estimation is to visually determine the pose of a specific object in the RGB-D input. Unfortunately, when faced with new categories, both instance-based and…
Efficient Heatmap-Guided 6-Dof Grasp Detection in Cluttered Scenes
Siang Chen, Wei Tang, Pengwei Xie +2
Fast and robust object grasping in clutter is a crucial component of robotics. Most current works resort to the whole observed point cloud for 6-Dof grasp generation, ignoring the…
Rethinking 6-Dof Grasp Detection: A Flexible Framework for High-Quality Grasping
Pengwei Xie, Siang Chen, Wei Tang +3
Robotic grasping is a primitive skill for complex tasks and is fundamental to intelligence. For general 6-Dof grasping, most previous methods directly extract scene-level semantic…