7 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…
Multi-Agent Reinforcement Learning for the Low-Level Control of a Quadrotor UAV
Beomyeol Yu, Taeyoung Lee
By leveraging the underlying structures of the quadrotor dynamics, we propose multi-agent reinforcement learning frameworks to innovate the low-level control of a quadrotor, where…
Equivariant Reinforcement Learning for Quadrotor UAV
Beomyeol Yu, Taeyoung Lee
This paper presents an equivariant reinforcement learning framework for quadrotor unmanned aerial vehicles. Successful training of reinforcement learning often requires numerous in…