12 papers
PGMT: Perceptive General Motion Tracking for Humanoid Robots
Hongyi Li, Li Peizhuo, Yucheng Tao +9
Humanoid motion trackers can reproduce diverse whole-body motions, but their performance degrades on complex terrain where terrain-agnostic references become physically infeasible.…
SSP-DMGTimeNet: Physics-Constrained Learning for Spatiotemporal Trajectory Prediction of Vehicle Platoons
Yuhang Wang, Kailang Ma, Zirui Li +4
Existing car-following prediction methods mainly optimize trajectory accuracy, while rarely considering whether predicted disturbances propagate realistically along a vehicle plato…
TAGA: Terrain-aware Active Gaze Learning for Generalizable Agile Humanoid Locomotion
Peizhuo Li, Hongyi Li, Mingfeng Fan +13
Agile humanoid locomotion across diverse challenging terrain demands both wide perceptual coverage and precise local geometry understanding. Motivated by the way humans selectively…
Learning to Solve Compositional Geometry Routing Problems
Mingfeng Fan, Jianan Zhou, Jiaqi Cheng +3
We study the Compositional Geometry Routing Problem (CGRP), a unified superclass of traditional routing problems that covers point-only, line-only, area-only, and arbitrary hybrid…
CROSS: A Mixture-of-Experts Reinforcement Learning Framework for Generalizable Large-Scale Traffic Signal Control
Xibei Chen, Yifeng Zhang, Yuxiang Xiao +3
Recent advances in robotics, automation, and artificial intelligence have enabled urban traffic systems to operate with increasing autonomy towards future smart cities, powered in…
LATS: Large Language Model Assisted Teacher-Student Framework for Multi-Agent Reinforcement Learning in Traffic Signal Control
Yifeng Zhang, Peizhuo Li, Tingguang Zhou +2
Adaptive Traffic Signal Control (ATSC) aims to optimize traffic flow and minimize delays by adjusting traffic lights in real time. Recent advances in Multi-agent Reinforcement Lear…
CAMO: A Conditional Neural Solver for the Multi-objective Multiple Traveling Salesman Problem
Fengxiaoxiao Li, Xiao Mao, Mingfeng Fan +4
Robotic systems often require a team of robots to collectively visit multiple targets while optimizing competing objectives, such as total travel cost and makespan. This setting ca…
Fed-GAME: Personalized Federated Learning with Graph Attention Mixture-of-Experts For Time-Series Forecasting
Yi Li, Han Liu, Mingfeng Fan +3
Federated learning (FL) on graphs shows promise for distributed time-series forecasting. Yet, existing methods rely on static topologies and struggle with client heterogeneity. We…
CogDrive: Cognition-Driven Multimodal Prediction-Planning Fusion for Safe Autonomy
Heye Huang, Yibin Yang, Mingfeng Fan +3
Safe autonomous driving in mixed traffic requires a unified understanding of multimodal interactions and dynamic planning under uncertainty. Existing learning based approaches stru…
A Unified Deep Reinforcement Learning Approach for Close Enough Traveling Salesman Problem
Mingfeng Fan, Jiaqi Cheng, Yaoxin Wu +4
In recent years, deep reinforcement learning (DRL) has gained traction for solving the NP-hard traveling salesman problem (TSP). However, limited attention has been given to the cl…
Preference-Driven Multi-Objective Combinatorial Optimization with Conditional Computation
Mingfeng Fan, Jianan Zhou, Yifeng Zhang +3
Recent deep reinforcement learning methods have achieved remarkable success in solving multi-objective combinatorial optimization problems (MOCOPs) by decomposing them into multipl…
Unicorn: A Universal and Collaborative Reinforcement Learning Approach Towards Generalizable Network-Wide Traffic Signal Control
Yifeng Zhang, Yilin Liu, Ping Gong +3
Adaptive traffic signal control (ATSC) is crucial in reducing congestion, maximizing throughput, and improving mobility in rapidly growing urban areas. Recent advancements in param…