223 citations · 792 across the 23 of their papers we have counts for
23 papers
Multi-Task Reinforcement Learning for Quadrotors
Jiaxu Xing, Ismail Geles, Yunlong Song +2
Reinforcement learning (RL) has shown great effectiveness in quadrotor control, enabling specialized policies to develop even human-champion-level performance in single-task scenar…
Residual Policy Learning for Perceptive Quadruped Control Using Differentiable Simulation
Jing Yuan Luo, Yunlong Song, Victor Klemm +3
First-order Policy Gradient (FoPG) algorithms such as Backpropagation through Time and Analytical Policy Gradients leverage local simulation physics to accelerate policy search, si…
Learning Quadrotor Control From Visual Features Using Differentiable Simulation
Johannes Heeg, Yunlong Song, Davide Scaramuzza
The sample inefficiency of reinforcement learning (RL) remains a significant challenge in robotics. RL requires large-scale simulation and can still cause long training times, slow…
Agile Robotics: Optimal Control, Reinforcement Learning, and Differentiable Simulation
Yunlong Song, Davide Scaramuzza
Control systems are at the core of every real-world robot. They are deployed in an ever-increasing number of applications, ranging from autonomous racing and search-and-rescue miss…
Learning Quadruped Locomotion Using Differentiable Simulation
Yunlong Song, Sangbae Kim, Davide Scaramuzza
This work explores the potential of using differentiable simulation for learning quadruped locomotion. Differentiable simulation promises fast convergence and stable training by co…
Flymation: Interactive Animation for Flying Robots
Yunlong Song, Davide Scaramuzza
Trajectory visualization and animation play critical roles in robotics research. However, existing data visualization and animation tools often lack flexibility, scalability, and v…
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…
Contrastive Learning for Enhancing Robust Scene Transfer in Vision-based Agile Flight
Jiaxu Xing, Leonard Bauersfeld, Yunlong Song +2
Scene transfer for vision-based mobile robotics applications is a highly relevant and challenging problem. The utility of a robot greatly depends on its ability to perform a task i…
Contrastive Initial State Buffer for Reinforcement Learning
Nico Messikommer, Yunlong Song, Davide Scaramuzza
In Reinforcement Learning, the trade-off between exploration and exploitation poses a complex challenge for achieving efficient learning from limited samples. While recent works ha…
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…
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…
Learning Deep Sensorimotor Policies for Vision-based Autonomous Drone Racing
Jiawei Fu, Yunlong Song, Yan Wu +2
Autonomous drones can operate in remote and unstructured environments, enabling various real-world applications. However, the lack of effective vision-based algorithms has been a s…
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…
Learning Perception-Aware Agile Flight in Cluttered Environments
Yunlong Song, Kexin Shi, Robert Penicka +1
Recently, neural control policies have outperformed existing model-based planning-and-control methods for autonomously navigating quadrotors through cluttered environments in minim…
Autonomous Ground Navigation in Highly Constrained Spaces: Lessons learned from The BARN Challenge at ICRA 2022
Xuesu Xiao, Zifan Xu, Zizhao Wang +14
The BARN (Benchmark Autonomous Robot Navigation) Challenge took place at the 2022 IEEE International Conference on Robotics and Automation (ICRA 2022) in Philadelphia, PA. The aim…
Learning Minimum-Time Flight in Cluttered Environments
Robert Penicka, Yunlong Song, Elia Kaufmann +1
We tackle the problem of minimum-time flight for a quadrotor through a sequence of waypoints in the presence of obstacles while exploiting the full quadrotor dynamics. Early works…
Policy Search for Model Predictive Control with Application to Agile Drone Flight
Yunlong Song, Davide Scaramuzza
Policy Search and Model Predictive Control~(MPC) are two different paradigms for robot control: policy search has the strength of automatically learning complex policies using expe…
Autonomous Overtaking in Gran Turismo Sport Using Curriculum Reinforcement Learning
Yunlong Song, HaoChih Lin, Elia Kaufmann +2
Professional race-car drivers can execute extreme overtaking maneuvers. However, existing algorithms for autonomous overtaking either rely on simplified assumptions about the vehic…
Autonomous Drone Racing with Deep Reinforcement Learning
Yunlong Song, Mats Steinweg, Elia Kaufmann +1
In many robotic tasks, such as autonomous drone racing, the goal is to travel through a set of waypoints as fast as possible. A key challenge for this task is planning the time-opt…
Flightmare: A Flexible Quadrotor Simulator
Yunlong Song, Selim Naji, Elia Kaufmann +2
State-of-the-art quadrotor simulators have a rigid and highly-specialized structure: either are they really fast, physically accurate, or photo-realistic. In this work, we propose…
Super-Human Performance in Gran Turismo Sport Using Deep Reinforcement Learning
Florian Fuchs, Yunlong Song, Elia Kaufmann +2
Autonomous car racing is a major challenge in robotics. It raises fundamental problems for classical approaches such as planning minimum-time trajectories under uncertain dynamics…
Learning High-Level Policies for Model Predictive Control
Yunlong Song, Davide Scaramuzza
The combination of policy search and deep neural networks holds the promise of automating a variety of decision-making tasks. Model Predictive Control (MPC) provides robust solutio…