3 accepted papers
Learning nonlinear dynamical models from data is central to control. Two fundamental challenges exist: (1) how to learn accurate models from limited data, and (2) how to ensure the learned models are suitable for control design of the nominal system. We address both by enforcing a critical \emph{a p…
Physics engines are essential components in simulating complex robotic systems. The accuracy and computational speed of these engines are crucial for reliable real-time simulation. This letter comprehensively evaluates the performance of five common physics engines, i.e., ODE, Bullet, DART, MuJoCo,
The performance of a camera network monitoring a set of targets depends crucially on the configuration of the cameras. In this paper, we investigate the reconfiguration strategy for the parameterized camera network model, with which the sensing qualities of the multiple targets can be optimized glob…