Abstract-This paper is an initial approach to create a controller for the game TORCS by learning how another controller or humans play the game. We used data obtained from two controllers and from one human player. The first controller is the winner of the WCCI 2008 Simulated Car Racing Competition, and the second one is a hand coded controller that performs a complete lap in all tracks. First, each kind of controller is imitated separately, then a mix of the data is used to create new controllers. The imitation is performed by means of training a feed forward neural network with the data, using the backpropagation algorithm for learning.

No abstract

This paper presents a controller for the 2010 Simulated Car Racing Championship. The idea is not to create the fastest controller but a human-like controller. In order to achieve this, first we have created a process to build a model of the tracks while the car is running and then we used several neural networks which predict the trajectory the car should follow and the target speed. A scripted policy is used for the gear change and to follow the predicted trajectory with the predicted speed. The neural networks are trained with data retrieved from a human player, and are evaluated in a new track. The results shows an acceptable performance of the controller in unknown tracks, more than 20% slower than the human in the same tracks because of the mistakes made when the controller tries to follow the trajectory.

The purpose of this paper is to design a soft robotic neck prototype with two Degrees of Freedom (DOF) and propose a control system based on a fractional order PD controller (FPD). The neck will be able to perform movements of flexion, extension and lateral bending. To achieve these movements, the design is made based on a cable-driven mechanism, with components easy to manufacture in a 3D printer. Simulations are performed to validate the feasibility of the developed parallel robot prototype and the robustness of the proposed control scheme to mass changes at the tip.

No abstract