2 citations · 2 across the 1 of their papers we have counts for
3 papers
HoLoArm: Deformable Arms for Collision-Tolerant Quadrotor Flight
Quang Ngoc Pham, Jonas Eschmann, Yang Zhou +3
The increasing use of drones in human-centric applications highlights the need for designs that can survive collisions and recover rapidly, minimizing risks to both humans and the…
RAPTOR: A Foundation Policy for Quadrotor Control
Jonas Eschmann, Dario Albani, Giuseppe Loianno
Humans are remarkably data-efficient when adapting to new unseen conditions, like driving a new car. In contrast, modern robotic control systems, like neural network policies train…
RLtools: A Fast, Portable Deep Reinforcement Learning Library for Continuous Control
Jonas Eschmann, Dario Albani, Giuseppe Loianno
Deep Reinforcement Learning (RL) can yield capable agents and control policies in several domains but is commonly plagued by prohibitively long training times. Additionally, in the…