Learning high-speed flight in the wild was published in Science Robotics. The paper presents an end-to-end approach to autonomous high-speed flight through complex natural and human-made environments, with purely onboard sensing and computation. The flight is controlled by a convolutional network that maps noisy sensory observations to collision-free trajectories in a receding-horizon fashion. It is trained exclusively in simulation via privileged learning.
Three papers accepted to NeurIPS 2021
Posted on October 3, 2021
Three papers were accepted to Neural Information Processing Systems (NeurIPS): Geometry Processing with Neural Fields, Differentiable Simulation of Soft Multi-body Systems, and Habitat 2.0: Training Home Assistants to Rearrange their Habitat. Habitat 2.0 was selected for a spotlight at the conference (<3% acceptance rate).