RoboCup 2016 in Leipzig, Germany RoboCup 2016 was held in Leipzig, Germany. After the extensive changes introduced in 2015, the gameplay didn't change much in 2016. The robots operate stations based on the Modular Production System (MPS) provided by Festo. MPSs are small production machines that process small cylinders. These cylinders represent workpieces and consist of a colored base, zero, or up to three colored rings, and a cap on top. These cylinders embody products within the factory scenario. By the physical manipulation of these products, the material flow and the production sequence of the goods become comprehensible. The products ordered are determined randomly and published dynamically along the game. Therefore, planning and scheduling of the production process driven by the robot fleet are crucial aspects of this scenario alongside the typical problems in robotics like navigation with collision avoidance, self-localization, object detection, or (limited) manipulation. 
Technical Challenge Additionally, we had a Technical Challenge consisting of three tasks:
1) Recognition of a machine without using a marker
2) Running a Simulation Game
3) Open Challenge: Presentation of some special feature
Corssover Challenge
To foster closer cooperation among the RoboCup Logistics League and the RoboCup @Work league, a crossover challenge was introduced in 2016. Within this challenge, an industrial process in a multi-stage production and packaging scenario with human-robot interaction and cross-vendor robot cooperation was emulated.  It clearly distinguishes the task to be performed by the robots of the respective leagues and roughly follows the following steps. A human worker initiates production by requesting a specific product (1) . The request is processed by the @Work referee box (refbox) and immediately communicated to the RCLL refbox (2) . It generates an order and sends it to an RCLL robot for completion (3) . Once production is completed, the product is supplied to a shelf or similar in the shared zone and informs the RCLL refbox, which in turn informs the @Work refbox of the availability of the product (4) . This informs an @Work robot which picks up the object and puts it into a box, that is then delivered to the human worker (5).
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