The RoboCup@Home league aims to develop service and assistive robot technology with high relevance for future personal domestic applications. It is the largest international annual competition for autonomous service robots and is part of the RoboCup initiative.
The Alle@Home team qualified for the RoboCup@Home competition, which is the largest competition for service robots. The team will be participating in the Open Platform League.
We made it! We received the news that our team qualified for the RoboCup@Home competition.
“This is a huge success since our team is brand new, and we were really amazed that we were able to get such a good qualification result", said Dr. Karinne Ramirez one of the team leaders.
In 2017, the competition took place in Nagoya/Japan (July 25 - 30, 2017).
The focus of this competition is on challenging scientific problems such as human-robot interaction, mobile manipulation, and cognition. The student team needs to design, implement and test perception, learning, and robot control algorithms for these challenges. The overall goal is to provide students with enough practical background to participate in the international annual competition of the RoboCup@Home league.
The code implemented for the demonstration shown for the qualification video can be found in the following repository:
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Karinne Ramirez-Amaro, Michael Beetz, Gordon Cheng: Transferring Skills to Humanoid Robots by Extracting Semantic Representations from Observations of Human Activities. Artificial Intelligence Journal, 2015.
Karinne Ramirez-Amaro , Michael Beetz and Gordon Cheng: Understanding the intention of human activities through semantic perception: observation, understanding and execution on a humanoid robot. Advanced Robotics 29 (5), 2015, 345-362.
E. Dean, K. Ramirez-Amaro, F. Bergner, I. Dianov, P. Lanillos, and G. Cheng: Robotic technologies for fast deployment of industrial robot systems. IEEE Industrial Electronics Conference (IEEE IECON2016), 2016.
llya Dianov, Karinne Ramirez-Amaro, Pablo Lanillos, Emmanuel Dean-Leon, Florian Bergner and Gordon Cheng: Extracting general task structures to accelerate the learning of new tasks. IEEE-RAS International Conference on Humanoid Robots 2016.