The competition concerns the development of an efficient algorithm to optimize active 3D designs (i.e., simulated agents or robots). The simulation environment is Framsticks, and participants have a Python binding available to the native simulator library, so algorithms should be implemented entirely in Python. Technical details are described on the dedicated competition web page (link below).

The goal of the competition is to propose an algorithm that will discover agents whose center of gravity moves in the desired way in different environments used during optimization. The properties of the desired movement are defined by the fitness function (unknown to participants); examples of such movements are: following a specific path in 3D, swinging, or jumping. The set of parameters that define each environment (such as gravity, water level, terrain, and initial agent rotation) is published, but their values will be set during the evaluation phase. Each submitted algorithm will be tested to optimize agents in 10 different settings (environments and desired movements). These settings will be the same for all participants.

Each submission must contain a short description of the algorithm and a standalone Python source code. The source code can use any freely and publicly available libraries, but participants should take care to describe the way dependencies are supposed to be installed to allow the organizers to run their algorithm. The algorithm will not have access to the Internet.