86 citations · 126 across the 36 of their papers we have counts for
38 papers
GG-Nav: Goal-oriented Grounding Chain-of-Thought Elicits Reasoning in Navigation Foundation Models
Shaohao Zhu, Wensi Huang, Siqi Zhang +6
Emerging strong Vision-Language Models (VLMs) have advanced navigation foundation models. However, these models still have unsatisfactory performance on goal-oriented navigation se…
Optimal Design of Distributed Inexact Gradient Tracking: Exact Worst-case Convergence Rate and Explicit Parameters
Qiuchen Tian, Li Chai, Jinming Xu
This paper addresses the problem of designing distributed optimization algorithms. Different to existing methods that usually give sufficient conditions and conservative convergenc…
A Forward-Inverse Dynamic Game Framework for Enhanced Multi-Agent Trajectory Planning
Tianle Liu, Youcheng Niu, Jing Zeng +2
This paper studies feedback Nash equilibrium (FBNE) seeking for multi-agent trajectory planning in nonlinear dynamical systems with unknown agents' objectives and state-dependent i…
Optimal Parameter Design for DIGing on Minimizing Unweighted Sum of Squares
Qiuchen Tian, Li Chai, Jinming Xu
There is no general method for designing proper parameters to achieve faster convergence in distributed optimization algorithms. In this paper, we consider the distributed inexact…
Exact Worst-case Convergence Rates of Distributed Gradient Tracking Methods
Qiuchen Tian, Li Chai, Jinming Xu
Different from most existing literature in the analysis of distributed optimization algorithms that reports sufficient convergence conditions leading to a conservative convergence…
Decentralized Pose Graph Riemannian Optimization for Object-based Multi-Robot SLAM
Yixian Zhao, Yan Huang, Yang Xu +2
Pose graph optimization (PGO) is a key back-end component for state estimation in networked multi-robot simultaneous localization and mapping (SLAM). In object-based multi-robot SL…
Adaptive Polyak Stepsize with Level-value Adjustment for Distributed Optimization
Chen Ouyang, Yongyang Xiong, Jinming Xu +2
Stepsize selection remains a critical challenge in the practical implementation of distributed optimization. Existing distributed algorithms often rely on restrictive prior knowled…
Heterogeneous Stochastic Momentum ADMM for Distributed Nonconvex Composite Optimization
Yangming Zhang, Yongyang Xiong, Jinming Xu +2
This paper investigates the distributed stochastic nonconvex and nonsmooth composite optimization problem. Existing stochastic typically rely on uniform step size strictly bounded…
DP-CSGP: Differentially Private Stochastic Gradient Push with Compressed Communication
Zehan Zhu, Heng Zhao, Yan Huang +3
In this paper, we propose a Differentially Private Stochastic Gradient Push with Compressed communication (termed DP-CSGP) for decentralized learning over directed graphs. Differen…
Bandwidth-Aware Network Topology Optimization for Decentralized Learning
Yipeng Shen, Zehan Zhu, Yan Huang +3
Network topology is critical for efficient parameter synchronization in distributed learning over networks. However, most existing studies do not account for bandwidth limitations…
VL-LN Bench: Towards Long-horizon Goal-oriented Navigation with Active Dialogs
Wensi Huang, Shaohao Zhu, Meng Wei +7
In most existing embodied navigation tasks, instructions are well-defined and unambiguous, such as instruction following and object searching. Under this idealized setting, agents…
Beyond Scaffold: A Unified Spatio-Temporal Gradient Tracking Method
Yan Huang, Jinming Xu, Jiming Chen +1
In distributed and federated learning algorithms, communication overhead is often reduced by performing multiple local updates between communication rounds. However, due to data he…
Pareto-optimal Trade-offs Between Communication and Computation with Flexible Gradient Tracking
Yan Huang, Jinming Xu, Li Chai +2
This paper addresses distributed stochastic optimization problems under non-i.i.d. data, focusing on the inherent trade-offs between communication and computational efficiency. To…
An Optimistic Gradient Tracking Method for Distributed Minimax Optimization
Yan Huang, Jinming Xu, Jiming Chen +1
This paper studies the distributed minimax optimization problem over networks. To enhance convergence performance, we propose a distributed optimistic gradient tracking method, ter…
CoCoL: A Communication Efficient Decentralized Collaborative Method for Multi-Robot Systems
Jiaxi Huang, Yan Huang, Yixian Zhao +2
Collaborative learning enhances the performance and adaptability of multi-robot systems in complex tasks but faces significant challenges due to high communication overhead and dat…
Dyn-DP: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee
Zehan Zhu, Yan Huang, Xin Wang +2
Most existing decentralized learning methods with differential privacy (DP) guarantee rely on constant gradient clipping bounds and fixed-level DP Gaussian noises for each node thr…
On Random Sampling of Diffused Graph Signals with Sparse Inputs on Vertex Domain
Yingcheng Lai, Li Chai, Jinming Xu
The sampling of graph signals has recently drawn much attention due to the wide applications of graph signal processing. While a lot of efficient methods and interesting results ha…
Multi-robot autonomous 3D reconstruction using Gaussian splatting with Semantic guidance
Jing Zeng, Qi Ye, Tianle Liu +5
Implicit neural representations and 3D Gaussian splatting (3DGS) have shown great potential for scene reconstruction. Recent studies have expanded their applications in autonomous…
Hessian-Free Distributed Bilevel Optimization via Penalization with Time-Scale Separation
Youcheng Niu, Jinming Xu, Ying Sun +2
This paper considers a class of distributed bilevel optimization (DBO) problems with a coupled inner-level subproblem. Existing approaches typically rely on hypergradient estimatio…
Achieving Near-Optimal Convergence for Distributed Minimax Optimization with Adaptive Stepsizes
Yan Huang, Xiang Li, Yipeng Shen +2
In this paper, we show that applying adaptive methods directly to distributed minimax problems can result in non-convergence due to inconsistency in locally computed adaptive steps…
PrivSGP-VR: Differentially Private Variance-Reduced Stochastic Gradient Push with Tight Utility Bounds
Zehan Zhu, Yan Huang, Xin Wang +1
In this paper, we propose a differentially private decentralized learning method (termed PrivSGP-VR) which employs stochastic gradient push with variance reduction and guarantees $…
MAexp: A Generic Platform for RL-based Multi-Agent Exploration
Shaohao Zhu, Jiacheng Zhou, Anjun Chen +3
The sim-to-real gap poses a significant challenge in RL-based multi-agent exploration due to scene quantization and action discretization. Existing platforms suffer from the ineffi…
Safe Hybrid-Action Reinforcement Learning-Based Decision and Control for Discretionary Lane Change
Ruichen Xu, Xiao Liu, Jinming Xu +1
Autonomous lane-change, a key feature of advanced driver-assistance systems, can enhance traffic efficiency and reduce the incidence of accidents. However, safe driving of autonomo…
Distributed Stochastic Bilevel Optimization: Improved Complexity and Heterogeneity Analysis
Youcheng Niu, Jinming Xu, Ying Sun +2
This paper consider solving a class of nonconvex-strongly-convex distributed stochastic bilevel optimization (DSBO) problems with personalized inner-level objectives. Most existing…