6 accepted papers
We present VISTA (Viewpoint-based Image selection with Semantic Task Awareness), an active exploration method for robots to plan informative trajectories that improve 3D map quality in areas most relevant for task completion. Given an open-vocabulary search instruction (e.g., “find a person”), VISTA
We present VISTA (Viewpoint-based Image selection with Semantic Task Awareness), an active exploration method for robots to plan informative trajectories that improve 3D map quality in areas most relevant for task completion. Given an open-vocabulary search instruction (e.g., "find a person"), VISTA…
We propose a new simulator, training approach, and policy architecture, collectively called SOUS VIDE, for end-to-end visual drone navigation. Our trained policies exhibit zero-shot sim-to-real transfer with robust real-world performance using only onboard perception and computation. Our simulator,
Learning-based models for robot perception are known to suffer from two distinct sources of error: aleatoric and epistemic. Aleatoric uncertainty arises from inherently noisy training data and is easily quantified from residual errors during training. Conversely, epistemic uncertainty arises from a
A largely untapped potential for aerial robots is to capture airborne targets in flight. We present an approach in which a simple dynamic model of a quadrotor/target interaction leads to the design of a gripper and associated velocity sufficiency region with a high probability of capture. A model of
In this work we present a planning and control method for a quadrotor in an autonomous drone race. Our method combines the advantages of both model-based optimal control and model-free deep reinforcement learning. We consider a single drone racing on a track marked by a series of gates; through whic…