7 papers
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…
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…
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…
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 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…
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…