Florian Shkurti

TL;DR: A heterogeneous multi-robot system for assisting scientists in environmental monitoring tasks, such as the inspection of marine ecosystems, is described, comprised of a fixed-wing aerial vehicle, an autonomous airboat, and an agile legged underwater robot.

TL;DR: This paper proposes to automate the process using Bayesian optimization to generate adversarialSelf-driving scenarios that expose poorly-engineered or poorly-trained self-driving policies, and increase the risk of collision with simulated pedestrians and vehicles.

TL;DR: This paper theoretically characterize the tradeoff between safety and policy improvement, show that the safety constraints are likely to be satisfied with high probability during training, derive provable convergence guarantees for the approach, and demonstrate the efficacy of the proposed approach on a suite of challenging navigation, manipulation, and locomotion tasks.

TL;DR: The proposed approach combines information from an Inertial Measurement Unit in the form of linear accelerations and angular velocities, depth data from a pressure sensor, and feature tracking from a monocular downward facing camera to estimate the 6DOF pose of the vehicle.

TL;DR: In this paper, a tracking-by-detection (TBD) approach is proposed to mitigate tracking drift in unstructured 3D environments, which interleaves efficient model-based object detection with temporal filtering of image-based bounding box estimation.