activity
20202024
most citedReaching the Limit in Autonomous Racing: Optimal Control versus Reinforcement Learning

223 citations · 515 across the 17 of their papers we have counts for

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21 papers · 1 filter

cs.RO2024

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…

cs.RO2024

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…

cs.RO2024★ 10 cited

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…

cs.RO2024

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…

cs.RO2024★ 1 cited

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…

cs.RO2023

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…