Toward version 1.1
April 22, 2008
We are finishing up version 1.1 of Ikaros that will be released within a few days. The new version mainly contains bug fixes for version 1.0. In addition, a number of new WebUI object have been added.
MiniBot Completed
December 10, 2007
The mobile robot MiniBot has been completed. It was built as part of the EU funded project MindRaces to study learning of anticipatory behaviors.
The robot is equipped with an arm using five digital servos and an active vision head with two degrees of freedom. The robot is controlled by a Mac Mini which was modified to run from a battery. All servos are controlled by the SSC-32 controller though the SSC32 module in Ikaros. The two motors for the wheels are controlled by the Motor Mind B through USB directly from the computer.
The robot uses Ikaros to implement a model of autonomous learning of anticipatory sensory-motor transformation. Using continuous observation of its hand and the behavior of a target object, the robot is able to learn motor behaviors that uses predictions of the motion of the target. With such aniticpatory behaviors, the robot can move its gripper to the anticipated future location of the target object.
Most of the modules used to control the robot will be available as part of release 1.1. of Ikaros.
What is Ikaros?
The Ikaros project started in 2001 and its goal is to develop an open infrastructure for system level modeling of the brain including databases of experimental data, computational models and functional brain data. The system will make heavy use of the emerging standards for Internet based information and will make all information accessible through an open web-based interface. In addition, Ikaros can be used as a control architecture for robots.
The main components of the Ikaros systems are:
- A platform independent simulation kernel
- A set of computational brain models
- A set of I/O modules for interfacing with data files and peripheral such as robots or video cameras
- Tools for building systems of interconnected models
- A plug-in architecture that allows new models to be easily added to the system
- A database with data from learning experiments that can be used for validation of the computational models.