We develop an algorithm to control an underactuated unmanned surface veh...
Robots have been increasingly better at doing tasks for humans by learni...
The contribution of this paper is a generalized formulation of correctio...
In this paper we propose a novel distributed model predictive control (D...
Motion planning for autonomous vehicles sharing the road with human driv...
A classical learning setting is one in which a student collects data, or...
Driving heavy-duty vehicles, such as buses and tractor-trailer vehicles,...
We consider the problem of how to learn a step-size policy for the
Limit...
Meta-learning refers to the process of abstracting a learning rule for a...
The contribution of this paper is a framework for training and evaluatio...
Maneuvering an articulated vehicle on narrow road stretches is often a
c...
Time perception is the phenomenological experience of time by an individ...
We address the issue of estimating the topology and dynamics of sparse l...
Kernel and linear regression have been recently explored in the predicti...
Driving in urban environments often presents difficult situations that
r...
We present a trajectory generation framework for control of wheeled vehi...
In this paper we study the estimation of changing trends in time-series ...
This paper concerns model reduction of dynamical systems using the nucle...
In this paper we analyze the asymptotic properties of l1 penalized maxim...
We present an alternating augmented Lagrangian method for convex optimiz...
This paper addresses the problem of segmenting a time-series with respec...