This work applies universal adaptive control to control barrier functions to achieve forward invariance of a safe set despite the presence of unmatched parametric uncertainties.
The stable combination of optimal feedback policies with online learning is studied in a new control-theoretic framework for uncertain nonlinear systems.
This work develops a new direct adaptive control framework that extends the certainty equivalence principle to general nonlinear systems with unmatched model uncertainties.
Monocular depth inference has gained tremendous attention from researchers in recent years and remains as a promising replacement for expensive time-of-flight sensors, but issues with scale acquisition and implementation overhead still plague these systems.
The standard approaches that ensure safety by enforcing a "stop" condition in the free-known space can severely limit the speed of the vehicle, especially in situations where much of the world is unknown.
The desire of maintaining computational tractability typically leads to optimization problems that do not include the obstacles (collision checks are done on the solutions) or to formulations that use a convex decomposition of the free space and then impose an ad hoc allocation of each interval of the trajectory in a specific polyhedron.
However, in unknown environments, this approach can lead to erratic or unstable behavior due to the interaction between the global planner, whose solution is changing constantly, and the local planner; a consequence of not capturing higher-order dynamics in the global plan.
This paper presents an approach for filtering sequences of object classification probabilities using online modeling of the noise characteristics of the classifier outputs.