5 papers
Motion Planning in Compressed Representation Spaces
Lukas Lao Beyer, Sertac Karaman
Deep learning methods have vastly expanded the capabilities of motion planning in robotics applications, as learning priors from large-scale data has been shown to be essential in…
SCREP: Scene Coordinate Regression and Evidential Learning-based Perception-Aware Trajectory Generation
Juyeop Han, Lukas Lao Beyer, Guilherme V. Cavalheiro +1
Autonomous flight in GPS-denied indoor spaces requires trajectories that keep visual-localization error tightly bounded across varied missions. Map-based visual localization method…
Flow Matching with Uncertainty Quantification and Guidance
Juyeop Han, Lukas Lao Beyer, Sertac Karaman
Despite the remarkable success of sampling-based generative models such as flow matching, they can still produce samples of inconsistent or degraded quality. To assess sample relia…
Highly Compressed Tokenizer Can Generate Without Training
L. Lao Beyer, T. Li, X. Chen +2
Commonly used image tokenizers produce a 2D grid of spatially arranged tokens. In contrast, so-called 1D image tokenizers represent images as highly compressed one-dimensional sequ…
Real-Time Sampling-based Online Planning for Drone Interception
Gilhyun Ryou, Lukas Lao Beyer, Sertac Karaman
This paper studies high-speed online planning in dynamic environments. The problem requires finding time-optimal trajectories that conform to system dynamics, meeting computational…