5 papers
Hardware- and Vision-in-the-Loop Validation of Deep Monocular Pose Estimation for Autonomous Maritime UAV Flight
Maneesha Wickramasuriya, Beomyeol Yu, Jaden Shin +3
Autonomous UAV operations on ships require reliable vision-based relative pose estimation, yet at-sea validation is costly, weather-dependent, and risky. This paper presents a hard…
Reasoning Knowledge-Gap in Drone Planning via LLM-based Active Elicitation
Zeyu Fang, Beomyeol Yu, Cheng Liu +5
Human-AI joint planning in Unmanned Aerial Vehicles (UAVs) typically relies on control handover when facing environmental uncertainties, which is often inefficient and cognitively…
Knowing When to Ask: Resolving Uncertainty in Human-Robot Joint Planning via Explicit Dialogue and Implicit Intent Cues
Zeyu Fang, Yuxin Lin, Cheng Liu +6
Effective human-robot collaboration in open-world environments requires joint planning under uncertainty about the task, the environment, and the human teammate. Communication is t…
Equivariant Reinforcement Learning Frameworks for Quadrotor Low-Level Control
Beomyeol Yu, Taeyoung Lee
Improving sampling efficiency and generalization capability is critical for the successful data-driven control of quadrotor unmanned aerial vehicles (UAVs) that are inherently unst…
Vision-in-the-loop Simulation for Deep Monocular Pose Estimation of UAV in Ocean Environment
Maneesha Wickramasuriya, Beomyeol Yu, Taeyoung Lee +1
This paper proposes a vision-in-the-loop simulation environment for deep monocular pose estimation of a UAV operating in an ocean environment. Recently, a deep neural network with…