activity
20222025
most citedStructured Deep Neural Network-Based Backstepping Trajectory Tracking Control for Lagrangian Systems

2 citations · 3 across the 6 of their papers we have counts for

collaborators

6 papers

eess.IV2025

Toward Robust Single-Photon Perception for Robots: A Condition-Aware Active Learning Approach

Zili Zhang, Ziting Wen, Yiheng Qiang +7

LiDAR-based perception plays a fundamental role in modern robotic systems for environment understanding and navigation. Single-photon LiDAR (SPL) extends conventional LiDAR by enab…

math.OC2024

A Communication-Efficient Stochastic Gradient Descent Algorithm for Distributed Nonconvex Optimization

Antai Xie, Xinlei Yi, Xiaofan Wang +2

This paper studies distributed nonconvex optimization problems with stochastic gradients for a multi-agent system, in which each agent aims to minimize the sum of all agents' cost…

cs.RO2024★ 2 cited

Structured Deep Neural Network-Based Backstepping Trajectory Tracking Control for Lagrangian Systems

Jiajun Qian, Liang Xu, Xiaoqiang Ren +1

Deep neural networks (DNN) are increasingly being used to learn controllers due to their excellent approximation capabilities. However, their black-box nature poses significant cha…

math.OC2023★ 1 cited

Differentially Private and Communication-Efficient Distributed Nonconvex Optimization Algorithms

Antai Xie, Xinlei Yi, Xiaofan Wang +2

This paper studies the privacy-preserving distributed optimization problem under limited communication, where each agent aims to keep its cost function private while minimizing the…

math.OC2023

Compressed Differentially Private Distributed Optimization with Linear Convergence

Antai Xie, Xinlei Yi, Xiaofan Wang +2

This paper addresses the problem of differentially private distributed optimization under limited communication, where each agent aims to keep their cost function private while min…

cs.RO2022

Learning Bifunctional Push-grasping Synergistic Strategy for Goal-agnostic and Goal-oriented Tasks

Dafa Ren, Shuang Wu, Xiaofan Wang +2

Both goal-agnostic and goal-oriented tasks have practical value for robotic grasping: goal-agnostic tasks target all objects in the workspace, while goal-oriented tasks aim at gras…