Monthly Archives: October 2021

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 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).