7 papers
Bridging Performance and Generalization in Reinforcement Learning for Agile Flight
Jonathan Green, Jiaxu Xing, Nico Messikommer +2
Autonomous drone racing is a fundamentally challenging regime for autonomous aerial robots, requiring time-optimal control while operating under persistent actuation saturation. Wh…
Dream to Fly: Model-Based Reinforcement Learning for Vision-Based Drone Flight
Angel Romero, Ashwin Shenai, Ismail Geles +2
Autonomous drone racing has risen as a challenging robotic benchmark for testing the limits of learning, perception, planning, and control. Expert human pilots are able to fly a dr…
Perception-Aware Time-Optimal Planning for Quadrotor Waypoint Flight
Chao Qin, Jiaxu Xing, Rudolf Reiter +4
Agile quadrotor flight pushes the limits of control, actuation, and onboard perception. While time-optimal trajectory planning has been extensively studied, existing approaches typ…
Learning Acrobatic Flight from Preferences
Colin Merk, Ismail Geles, Jiaxu Xing +3
Preference-based reinforcement learning (PbRL) enables agents to learn control policies without requiring manually designed reward functions, making it well-suited for tasks where…
Actor-Critic Model Predictive Control: Differentiable Optimization meets Reinforcement Learning for Agile Flight
Angel Romero, Elie Aljalbout, Yunlong Song +1
A key open challenge in agile quadrotor flight is how to combine the flexibility and task-level generality of model-free reinforcement learning (RL) with the structure and online r…
The Reality Gap in Robotics: Challenges, Solutions, and Best Practices
Elie Aljalbout, Jiaxu Xing, Angel Romero +9
Machine learning has facilitated significant advancements across various robotics domains, including navigation, locomotion, and manipulation. Many such achievements have been driv…