245 citations · 837 across the 21 of their papers we have counts for
22 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…
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…
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…
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…
Accelerating Model-Based Reinforcement Learning with State-Space World Models
Maria Krinner, Elie Aljalbout, Angel Romero +1
Reinforcement learning (RL) is a powerful approach for robot learning. However, model-free RL (MFRL) requires a large number of environment interactions to learn successful control…
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…
Demonstrating Agile Flight from Pixels without State Estimation
Ismail Geles, Leonard Bauersfeld, Angel Romero +2
Quadrotors are among the most agile flying robots. Despite recent advances in learning-based control and computer vision, autonomous drones still rely on explicit state estimation.…
MPCC++: Model Predictive Contouring Control for Time-Optimal Flight with Safety Constraints
Maria Krinner, Angel Romero, Leonard Bauersfeld +3
Quadrotor flight is an extremely challenging problem due to the limited control authority encountered at the limit of handling. Model Predictive Contouring Control (MPCC) has emerg…
Bootstrapping Reinforcement Learning with Imitation for Vision-Based Agile Flight
Jiaxu Xing, Angel Romero, Leonard Bauersfeld +1
Learning visuomotor policies for agile quadrotor flight presents significant difficulties, primarily from inefficient policy exploration caused by high-dimensional visual inputs an…
Reaching the Limit in Autonomous Racing: Optimal Control versus Reinforcement Learning
Yunlong Song, Angel Romero, Matthias Mueller +2
A central question in robotics is how to design a control system for an agile mobile robot. This paper studies this question systematically, focusing on a challenging setting: auto…
Learning to Walk and Fly with Adversarial Motion Priors
Giuseppe L'Erario, Drew Hanover, Angel Romero +5
Robot multimodal locomotion encompasses the ability to transition between walking and flying, representing a significant challenge in robotics. This work presents an approach that…
Agilicious: Open-Source and Open-Hardware Agile Quadrotor for Vision-Based Flight
Philipp Foehn, Elia Kaufmann, Angel Romero +8
Autonomous, agile quadrotor flight raises fundamental challenges for robotics research in terms of perception, planning, learning, and control. A versatile and standardized platfor…
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…
Autonomous Drone Racing: A Survey
Drew Hanover, Antonio Loquercio, Leonard Bauersfeld +6
Over the last decade, the use of autonomous drone systems for surveying, search and rescue, or last-mile delivery has increased exponentially. With the rise of these applications c…
Cracking Double-Blind Review: Authorship Attribution with Deep Learning
Leonard Bauersfeld, Angel Romero, Manasi Muglikar +1
Double-blind peer review is considered a pillar of academic research because it is perceived to ensure a fair, unbiased, and fact-centered scientific discussion. Yet, experienced r…
Weighted Maximum Likelihood for Controller Tuning
Angel Romero, Shreedhar Govil, Gonca Yilmaz +2
Recently, Model Predictive Contouring Control (MPCC) has arisen as the state-of-the-art approach for model-based agile flight. MPCC benefits from great flexibility in trading-off b…
Time-Optimal Online Replanning for Agile Quadrotor Flight
Angel Romero, Robert Penicka, Davide Scaramuzza
In this paper, we tackle the problem of flying a quadrotor using time-optimal control policies that can be replanned online when the environment changes or when encountering unknow…
Perception-Aware Perching on Powerlines with Multirotors
Julio L. Paneque, Jose Ramiro Martínez de Dios, Aníbal Ollero. Drew Hanover +3
Multirotor aerial robots are becoming widely used for the inspection of powerlines. To enable continuous, robust inspection without human intervention, the robots must be able to p…
Time-Optimal Planning for Quadrotor Waypoint Flight
Philipp Foehn, Angel Romero, Davide Scaramuzza
Quadrotors are among the most agile flying robots. However, planning time-optimal trajectories at the actuation limit through multiple waypoints remains an open problem. This is cr…
A Comparative Study of Nonlinear MPC and Differential-Flatness-Based Control for Quadrotor Agile Flight
Sihao Sun, Angel Romero, Philipp Foehn +2
Accurate trajectory tracking control for quadrotors is essential for safe navigation in cluttered environments. However, this is challenging in agile flights due to nonlinear dynam…
Model Predictive Contouring Control for Time-Optimal Quadrotor Flight
Angel Romero, Sihao Sun, Philipp Foehn +1
We tackle the problem of flying time-optimal trajectories through multiple waypoints with quadrotors. State-of-the-art solutions split the problem into a planning task - where a gl…
Nonlinear Control of Quadcopters via Approximate Dynamic Programming
Angel Romero, Paul N. Beuchat, Yvonne R. Stürz +2
While Approximate Dynamic Programming has successfully been used in many applications involving discrete states and inputs such as playing the games of Tetris or chess, it has not…