Abstract: A method includes providing a signal to an aerial vehicle, the signal including a first component having a first oscillation frequency and a second component having a second oscillation frequency, and flapping, in response to the signal, a wing of the vehicle, thereby causing the vehicle to rotate. Another method includes providing a first component of a signal to an aerial vehicle during a first time period, the first component having a first oscillation frequency, providing a second component of the signal to the vehicle during a second time period that follows the first time period, the second component having a second oscillation frequency, a first ratio of the first oscillation frequency to the second oscillation frequency being equal to a second ratio of the second time period to the first time period, and flapping, in response to receiving the signal, a wing of the vehicle, thereby moving the vehicle.

Abstract: Systems and methods for visuo-tactile object pose estimation are provided. In one embodiment, a computer implemented method includes receiving image data, depth data, and tactile data about the object in the environment. The computer implemented method also includes generating a visual estimate of the object that includes an object point cloud. The computer implemented method further includes generating a tactile estimate of the object that includes a surface point cloud based on the tactile data. The computer implemented method yet further includes estimating a pose of the object based on the visual estimate and the tactile estimate by fusing the object point cloud and the surface point cloud in a 3D space. The pose is a six-dimensional pose.

Abstract: Pose fusion estimation may be achieved via a first and second set of sensors receiving a first and second set of data, passing the first and second set of data through a graph-based neural network to generate a set of geometric features to be passed through a pose fusion network to generate a first and second pose estimate. A second portion of the pose fusion network may receive the set of geometric features and generate a second set of geometric features and the second pose estimate based on the set of geometric features. A first portion of the pose fusion network may receive the first set of data and the second set of geometric features and generate the first pose estimate based on a fusion of the first set of data and the second set of geometric features.

Abstract: Systems and methods for visuo-tactile object pose estimation are provided. In one embodiment, a computer implemented method includes receiving image data, depth data, and tactile data about the object in the environment. The computer implemented method also includes generating a visual estimate of the object that includes an object point cloud. The computer implemented method further includes generating a tactile estimate of the object that includes a surface point cloud based on the tactile data. The computer implemented method yet further includes estimating a pose of the object based on the visual estimate and the tactile estimate by fusing the object point cloud and the surface point cloud in a 3D space. The pose is a six-dimensional pose.