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In this TDP we describe a new tool created for testing the strategy layer of our soccer playing agents. It is a complete 2D simulator that simulates the games based on the decisions of 22 agents. With this tool, debugging the decision and strategy layer of our agents is much more efficient than before due to various interaction methods and complete control over the simulation.
In the future, the tool could also serve as a measure to run simulations of game series much faster than with the 3D simulator. This way, the impact of different play strategies could be evaluated much faster than before.
In this paper we show that a model-free approach to learn behaviors in joint space can be successfully used to utilize toes of a humanoid robot. Keeping the approach model-free makes it applicable to any kind of humanoid robot, or robot in general. Here we focus on the benefit on robots with toes which is otherwise more difficult to exploit. The task has been to learn different kick behaviors on simulated Nao robots with toes in the RoboCup 3D soccer simulator. As a result, the robot learned to step on its toe for a kick that performs 30% better than learning the same kick without toes.
For the RoboCup Soccer AdultSize League the humanoid robot Sweaty uses a single fully convolutional neural network to detect and localize the ball, opponents and other features on the field of play. This neural network can be trained from scratch in a few hours and is able to perform in real-time within the constraints of computational resources available on the robot. The time it takes to precess an image is approximately 11 ms. Balls and goal posts are recalled in 99 % of all cases (94.5 % for all objects) accompanied by a false detection rate of 1.2 % (5.2 % for all). The object detection and localization helped Sweaty to become finalist for the RoboCup 2017 in Nagoya.
One of the challenges in humanoid robotics is motion control. Interacting with humans requires impedance control algorithms, as well as tackling the problem of the closed kinematic chains which occur when both feet touch the ground. However, pure impedance control for totally autonomous robots is difficult to realize, as this algorithm needs very precise sensors for force and speed of the actuated parts, as well as very high sampling rates for the controller input signals. Both requirements lead to a complex and heavy weight design, which makes up for heavy machines unusable in RoboCup Soccer competitions.
A lightweight motor controller was developed that can be used for admittance and impedance control as well as for model predictive control algorithms to further improve the gait of the robot.
This paper describes the Sweaty II humanoid adult size robot trying to qualify for the RoboCup 2017 adult size humanoid competition. Sweaty came 2nd in RoboCup 2016 adult size league. The paper describes the main characteristics of Sweaty that made this success possible, and improvements that have been made or are planned to be implemented for RoboCup 2017.