5 papers
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…
Exploring Pose-Guided Imitation Learning for Robotic Precise Insertion
Han Sun, Sheng Liu, Yizhao Wang +5
Imitation learning is promising for robotic manipulation, but \emph{precise insertion} in the real world remains difficult due to contact-rich dynamics, tight clearances, and limit…
Photonic spiking reinforcement learning for intelligent routing
Shuiying Xiang, Yonghang Chen, Ling Zheng +8
Intelligent routing plays a key role in modern communication infrastructure, including data centers, computing networks, and future 6G networks. Although reinforcement learning (RL…
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…
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…