We present our open humanoid robot platform Wolfgang. The described hardware focuses on four aspects. Firstly, the robustness against falls is improved by integrating 3D printed elastic elements. Additionally, a high control loop frequency is achieved by using new custom control electronics. Furthermore, a torsion spring is applied to reduce the torque on the knee joints. Finally, computational power is provided through the combination of different processors. The paper also presents the ROS-based software stack that is used in RoboCup.

Wolfgang-OP: A Robust Humanoid Robot Platform for Research and Competitions

Soccer Field Boundary Detection Using Convolutional Neural Networks

AbstractWe present a dataset specifically designed to be used as a benchmark to compare vision systems in the RoboCup Humanoid Soccer domain. The dataset is composed of a collection of images taken in various real-world locations as well as a collection of simulated images. It enables comparing vision approaches with a meaningful and expressive metric. The contributions of this paper consist of providing a comprehensive and annotated dataset, an overview of the recent approaches to vision in RoboCup, methods to generate vision training data in a simulated environment, and an approach to increase the variety of a dataset by automatically selecting a diverse set of images from a larger pool. Additionally, we provide a baseline of YOLOv4 and YOLOv4-tiny on this dataset.KeywordsComputer visionVision datasetDeep learning 

TORSO-21 Dataset: Typical Objects in RoboCup Soccer 2021

Bipedal Walking on Humanoid Robots Through Parameter Optimization

RoboCup2022 KidSize League Winner CIT Brains: Open Platform Hardware SUSTAINA-OP and Software

We showcase a pipeline to train, evaluate, and deploy deep learning architectures for monocular depth estimation in the RoboCup Soccer Humanoid domain. In contrast to previous approaches, we apply the methods on embedded systems in highly dynamic but heavily constrained environments. The results indicate that our monocular depth estimation pipeline is usable in the RoboCup environment.

Applying Monocular Depth Estimation in RoboCup Soccer

Abstract Robotics researchers have been focusing on developing autonomous and human-like intelligent robots that are able to plan, navigate, manipulate objects, and interact with humans in both static and dynamic environments. These capabilities, however, are usually developed for direct interactions with people in controlled environments, and evaluated primarily in terms of human safety. Consequently, human-robot interaction (HRI) in scenarios with no intervention of technical personnel is under-explored. However, in the future, robots will be deployed in unstructured and unsupervised environments where they will be expected to work unsupervised on tasks which require direct interaction with humans and may not necessarily be collaborative. Developing such robots requires comparing the effectiveness and efficiency of similar design approaches and techniques. Yet, issues regarding the reproducibility of results, comparing different approaches between research groups, and creating challenging milestones to measure performance and development over time make this difficult. Here we discuss the international robotics competition called RoboCup as a benchmark for the progress and open challenges in AI and robotics development. The long term goal of RoboCup is developing a robot soccer team that can win against the world’s best human soccer team by 2050. We selected RoboCup because it requires robots to be able to play with and against humans in unstructured environments, such as uneven fields and natural lighting conditions, and it challenges the known accepted dynamics in HRI. Considering the current state of robotics technology, RoboCup’s goal opens up several open research questions to be addressed by roboticists. In this paper, we (a) summarise the current challenges in robotics by using RoboCup development as an evaluation metric, (b) discuss the state-of-the-art approaches to these challenges and how they currently apply to RoboCup, and (c) present a path for future development in the given areas to meet RoboCup’s goal of having robots play soccer against and with humans by 2050. 

The human in the loop Perspectives and challenges for RoboCup 2050

We are proposing an Open Source ROS vision pipeline for the RoboCup Soccer context. It is written in Python and offers sufficient precision while running with an adequate frame rate on the hardware of kid-sized humanoid robots to allow a fluent course of the game. Fully Convolutional Neural Networks (FCNNs) are used to detect balls while conventional methods are applied to detect robots, obstacles, goalposts, the field boundary, and field markings. The system is evaluated using an integrated evaluator and debug framework. Due to the usage of standardized ROS messages, it can be easily integrated into other teams’ code bases.

An Open Source Vision Pipeline Approach for RoboCup Humanoid Soccer

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Papers

Wolfgang-OP: A Robust Humanoid Robot Platform for Research and Competitions

Applying Monocular Depth Estimation in RoboCup Soccer

The human in the loop Perspectives and challenges for RoboCup 2050

An Open Source Vision Pipeline Approach for RoboCup Humanoid Soccer