6 papers
Embodiment-conditioned Generalist Control for Multirotor Aerial Robots
Orestis Konstantaropoulos, Welf Rehberg, Mihir Kulkarni +1
We present a generalist position control policy capable of controlling arbitrary multirotor configurations of a certain rotor count (e.g., hexarotors or quadrotors) with a single s…
Efficient Knowledge Transfer for Jump-Starting Control Policy Learning of Multirotors through Physics-Aware Neural Architectures
Welf Rehberg, Mihir Kulkarni, Philipp Weiss +1
Efficiently training control policies for robots is a major challenge that can greatly benefit from utilizing knowledge gained from training similar systems through cross-embodimen…
Isaac Lab: A GPU-Accelerated Simulation Framework for Multi-Modal Robot Learning
NVIDIA, :, Mayank Mittal +104
We present Isaac Lab, the natural successor to Isaac Gym, which extends the paradigm of GPU-native robotics simulation into the era of large-scale multi-modal learning. Isaac Lab c…
Performance-guided Task-specific Optimization for Multirotor Design
Etor Arza, Welf Rehberg, Philipp Weiss +2
This paper introduces a methodology for task-specific design optimization of multirotor Micro Aerial Vehicles. By leveraging reinforcement learning, Bayesian optimization, and cova…
A Neural Network Mode for PX4 on Embedded Flight Controllers
Sindre M. Hegre, Welf Rehberg, Mihir Kulkarni +1
This paper contributes an open-sourced implementation of a neural-network based controller framework within the PX4 stack. We develop a custom module for inference on the microcont…
Aerial Gym Simulator: A Framework for Highly Parallelized Simulation of Aerial Robots
Mihir Kulkarni, Welf Rehberg, Kostas Alexis
This paper contributes the Aerial Gym Simulator, a highly parallelized, modular framework for simulation and rendering of arbitrary multirotor platforms based on NVIDIA Isaac Gym.…