gpuFlightmare is a next-generation GPU-accelerated framework designed to enhance the capabilities of Flightmare, a CPU-based physics simulation tool. By transitioning to GPU processing, this project addresses two main limitations of the existing system: the inability to scale simulations to larger, more complex environments and the slow image rendering speeds that hinder efficient policy training for flying robots.
gpuFlightmare is a next-generation GPU-accelerated framework designed to enhance the capabilities of Flightmare, a CPU-based physics simulation tool. By transitioning to GPU processing, this project addresses two main limitations of the existing system: the inability to scale simulations to larger, more complex environments and the slow image rendering speeds that hinder efficient policy training for flying robots.
The goal of gpuFlightmare is to provide a more efficient and effective platform for developing and testing vision-based navigation policies. By improving simulation and rendering speeds, the project will facilitate faster iterations of policy training and validation, making it a valuable tool for researchers and developers in the field of aerial robotics.
The goal of gpuFlightmare is to provide a more efficient and effective platform for developing and testing vision-based navigation policies. By improving simulation and rendering speeds, the project will facilitate faster iterations of policy training and validation, making it a valuable tool for researchers and developers in the field of aerial robotics.
Yunlong Song (song@ifi.uzh.ch), Nico Messikommer ((nmessi@ifi.uzh.ch)
Yunlong Song (song@ifi.uzh.ch), Nico Messikommer ((nmessi@ifi.uzh.ch)