  In this paper, we address the problem of system identification and control of
a front-steered vehicle which abides by the Ackermann geometry constraints.
This problem arises naturally for on-road and off-road vehicles that require
reliable system identification and basic feedback controllers for various
applications such as lane keeping and way-point navigation. Traditional system
identification requires expensive equipment and is time consuming. In this work
we explore the use of differentiable physics for system identification and
controller design and make the following contributions: i)We develop a
differentiable physics simulator (DPS) to provide a method for the system
identification of front-steered class of vehicles whose system parameters are
learned using a gradient-based method; ii) We provide results for our
gradient-based method that exhibit better sample efficiency in comparison to
other gradient-free methods; iii) We validate the learned system parameters by
implementing a feedback controller to demonstrate stable lane keeping
performance on a real front-steered vehicle, the F1TENTH; iv) Further, we
provide results exhibiting comparable lane keeping behavior for system
parameters learned using our gradient-based method with lane keeping behavior
of the actual system parameters of the F1TENTH.
