7 papers
Data-Driven Spiking Control for Distributed -Nash Equilibrium Seeking
Linqi Wang, Wei Xiao, Yuzhou Wei +3
This paper studies how a feedback law synthesized directly from data can be realized by spiking control while retaining a game-theoretic performance guarantee. We consider distribu…
FlowPilot: Real-Time World-Action Modeling for Agile UAV Navigation
Runqing Wang, Ding Yu, Pengyuan Min +6
We present FlowPilot, a compact world-action model for real-time onboard UAV navigation from depth. Unlike map-then-optimize pipelines that require local reconstruction or end-to-e…
Mixture-of-World Models: Scaling Multi-Task Reinforcement Learning with Modular Latent Dynamics
Boxuan Zhang, Weipu Zhang, Zhaohan Feng +4
A fundamental challenge in multi-task reinforcement learning (MTRL) is achieving sample efficiency in visual domains where tasks exhibit substantial heterogeneity in both observati…
DyMoDreamer: World Modeling with Dynamic Modulation
Boxuan Zhang, Runqing Wang, Wei Xiao +5
A critical bottleneck in deep reinforcement learning (DRL) is sample inefficiency, as training high-performance agents often demands extensive environmental interactions. Model-bas…
SPGrasp: Spatiotemporal Prompt-driven Grasp Synthesis in Dynamic Scenes
Yunpeng Mei, Hongjie Cao, Yinqiu Xia +4
Real-time interactive grasp synthesis for dynamic objects remains challenging as existing methods fail to achieve low-latency inference while maintaining promptability. To bridge t…
Time-optimal Flight in Cluttered Environments via Safe Reinforcement Learning
Wei Xiao, Zhaohan Feng, Ziyu Zhou +3
This paper addresses the problem of guiding a quadrotor through a predefined sequence of waypoints in cluttered environments, aiming to minimize the flight time while avoiding coll…
Learning Hybrid Policies for MPC with Application to Drone Flight in Unknown Dynamic Environments
Zhaohan Feng, Jie Chen, Wei Xiao +3
In recent years, drones have found increased applications in a wide array of real-world tasks. Model predictive control (MPC) has emerged as a practical method for drone flight con…