11 citations · 21 across the 15 of their papers we have counts for
16 papers
Memory in the Sky: Low-Altitude Question Answering with Multi-Agent Memory Aggregation
Chengyang Li, Yujie Wan, Shuai Wang +6
This paper studies low-altitude question answering (LAQA), in which distributed unmanned aerial vehicle (UAV) memories are aggregated at a ground server to answer questions about o…
Memory-Native Non-Terrestrial Networks for Embodied Intelligence
Chengyang Li, Yikun Wang, Jiahui He +6
Non-terrestrial networks (NTN) provide ubiquitous connectivity for embodied intelligence (EI), enabling robots in the wilderness to leverage cloud resources or report critical info…
IR-SIM: A Lightweight Declarative Simulator for Navigation Learning and Benchmarking
Ruihua Han, Shuai Wang, Chengyang Li +8
Developing navigation policies requires simulation scenarios that support repeatable training and evaluation. Despite the availability of numerous simulators, constructing diverse…
Memory Centric Power Allocation for Multi-Agent Embodied Question Answering
Chengyang Li, Shuai Wang, Kejiang Ye +5
This paper considers multi-agent embodied question answering (MA-EQA), which enables robot teams to answer queries based on their long-horizon observations. In contrast to existing…
Direct Contact-Tolerant Motion Planning With Vision Language Models
He Li, Jian Sun, Chengyang Li +4
Navigation in cluttered environments often requires robots to tolerate contact with movable or deformable objects to maintain efficiency. Existing contact-tolerant motion planning…
Self-Evolutionary Replanning for Failure-Aware Motion Planning
Guoliang Li, Ruihua Han, Chengyang Li +5
Motion planners tuned under nominal conditions can fail when execution conditions change. Existing differentiable adaptation methods provide efficient online updates but remain con…
HPTune: Hierarchical Proactive Tuning for Collision-Free Model Predictive Control
Wei Zuo, Chengyang Li, Yikun Wang +5
Parameter tuning is a powerful approach to enhance adaptability in model predictive control (MPC) motion planners. However, existing methods typically operate in a myopic fashion t…
DPNet: Doppler LiDAR Motion Planning for Highly-Dynamic Environments
Wei Zuo, Zeyi Ren, Chengyang Li +7
Existing motion planning methods often struggle with rapid-motion obstacles due to an insufficient understanding of environmental changes. To address this, we propose integrating m…
Federated Split Learning for Resource-Constrained Robots in Industrial IoT: Framework Comparison, Optimization Strategies, and Future Directions
Wanli Ni, Hui Tian, Shuai Wang +3
Federated split learning (FedSL) has emerged as a promising paradigm for enabling collaborative intelligence in industrial Internet of Things (IoT) systems, particularly in smart f…
Autonomous Driving in Unstructured Environments: How Far Have We Come?
Chen Min, Shubin Si, Xu Wang +14
Research on autonomous driving in unstructured outdoor environments is less advanced than in structured urban settings due to challenges like environmental diversities and scene co…
OmniColor: A Global Camera Pose Optimization Approach of LiDAR-360Camera Fusion for Colorizing Point Clouds
Bonan Liu, Guoyang Zhao, Jianhao Jiao +6
A Colored point cloud, as a simple and efficient 3D representation, has many advantages in various fields, including robotic navigation and scene reconstruction. This representatio…
NeuPAN: Direct Point Robot Navigation with End-to-End Model-based Learning
Ruihua Han, Shuai Wang, Shuaijun Wang +8
Navigating a nonholonomic robot in a cluttered, unknown environment requires accurate perception and precise motion control for real-time collision avoidance. This paper presents N…
Decentralized Planning for Car-Like Robotic Swarm in Cluttered Environments
Changjia Ma, Zhichao Han, Tingrui Zhang +5
Robot swarm is a hot spot in robotic research community. In this paper, we propose a decentralized framework for car-like robotic swarm which is capable of real-time planning in cl…
An Efficient Spatial-Temporal Trajectory Planner for Autonomous Vehicles in Unstructured Environments
Zhichao Han, Yuwei Wu, Tong Li +8
As a core part of autonomous driving systems, motion planning has received extensive attention from academia and industry. However, real-time trajectory planning capable of spatial…
Federated Deep Learning Meets Autonomous Vehicle Perception: Design and Verification
Shuai Wang, Chengyang Li, Derrick Wing Kwan Ng +4
Realizing human-like perception is a challenge in open driving scenarios due to corner cases and visual occlusions. To gather knowledge of rare and occluded instances, federated le…
Adaptive Environment Modeling Based Reinforcement Learning for Collision Avoidance in Complex Scenes
Shuaijun Wang, Rui Gao, Ruihua Han +3
The major challenges of collision avoidance for robot navigation in crowded scenes lie in accurate environment modeling, fast perceptions, and trustworthy motion planning policies.…