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Due to the Covid-19 pandemic, the RoboCup WorldCup 2021 was held completely remotely. For this competition the Webots simulator (https://cyberbotics.com/) was used, so all teams needed to transfer their robot to the simulation. This paper describes our experiences during this process as well as a genetic learning approach to improve our walk engine to allow a more stable and faster movement in the simulation. Therefore we used a docker setup to scale easily. The resulting movement was one of the outstanding features that finally led to the championship title.
Autonomous humanoid robots need high torque actuators to be able to walk and run. One problem in this context is the heat generated. In this paper we propose to use water evaporation to improve cooling of the motors. Simulations based on thermodynamic calculations as well as measurements on real actuators show that, under the assumption of the load of a soccer game, cooling can be considerably improved with relatively small amounts of water.
In this paper we propose a motion framework forbipedal robots that decouples motion definitions from stabilizingthe robot. This simplifies motion definitions yet allows dynamicmotion adaptations. Two applications, walking and stopping onone leg, demonstrate the power of the framework. We show thatour framework is able to perform walking and stopping on one legeven under extreme conditions and improves walking benchmarkssignificantly in the RoboCup 3D soccer simulation domain.