Competing in RoboCup@Home and Winning the 2026 Best Paper Award: An Interview with Raphael Memmesheimer
Competing in RoboCup@Home and winning the 2026 best paper award: an interview with Raphael Memmesheimer
Raphael Memmesheimer and his team from NimbRo won the best paper award at RoboCup 2026 for their work on efficient image annotation via semi-supervised object segmentation with label propagation. The method enables robots to quickly learn object models in new environments, reducing manual annotation effort and improving performance in tasks like opening washing machines and interacting with humans. The approach is already being used in RoboCup competitions and is being refined for future challenges.
The paper presents a semi-supervised approach to efficient image annotation for robotic tasks, enabling robots to learn object models in new environments with limited preparation time. The method combines segmentation and label propagation to reduce manual annotation effort, which is critical for real-world deployment in dynamic settings like RoboCup@Home.
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is to make the robot pass the initial inspection. Robots must satisfy certain safety and interactive capabilities and if they fail they are not allowed to take part in the competition. In the competition itself there are five main tasks. One is human-robot interaction. The robots have to open the door for party guests and greet them. Here, the robot has to go to the door, find the handle, align the end-effector to th
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ta. Team NimbRo’s robot picking laundry from the washing machine. How did your team get on in the competition this time? It went well. We had issues, as always during competitions, but we ended up in 3rd place. There was a new “Doing Laundry” task introduced this year. The robot had to carry a laundry basket from a washing machine to a folding table and open the washing machine. It had to detect the washing machine d
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an object with a reference of what could actually be there. That allows us to simplify the labeling approach. Around 60% of the data is automatically labeled based on our approach. That would take a lot of hours by human annotation. Labeled example images from different competition venues. Figure from “Efficient Image Annotation via Semi-Supervised Object Segmentation with Label Propagation” . Have you been able to
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is to make the robot pass the initial inspection. Robots must satisfy certain safety and interactive capabilities and if they fail they are not allowed to take part in the competition. In the competition itself there are five main tasks. One is human-robot interaction. The robots have to open the door for party guests and greet them. Here, the robot has to go to the door, find the handle, align the end-effector to th
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asked to go and fetch a particular drink or snack and hand it to someone. In the past there were people from the audience called into the arena and asked to give a command. It’s interesting to see how good these robots are at interacting with a new person and not someone they are used to receiving commands from. Another interesting task is the restaurant task. Here, the robots are not in the known environment that we
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nce because it’s in the wild and people gather around it to watch it. Then, of course, there’s the new laundry task that I described above. To add to what I said before, this is a very hard task because the robot cannot see into the washing machine. So it has to feel if it has a T-shirt in its hand and take the t-shirt out of the machine. Then the robot has to go to a table and start folding other t-shirts. Team Nimb
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as large language models, open vocabulary approaches and, most recently, agentic systems. But every now and then there’s stagnation because teams are just overwhelmed by the wide amount of different challenges that this competition throws at you. Basically your robot has to do everything. It needs to navigate in an apartment, it needs to be able to interact with humans, it has to manipulate items, and it has to do t
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bring the cutlery to the dishwasher, put a dishwasher tablet into the dishwasher, close the dishwasher, and then prepare the table ready for the next meal. That’s a very manipulation intensive task. There is also a general-purpose service robot task. This task is undefined because the robot has to deal with a random natural language command that can incorporate all elements of the apartment. For example it might be
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e handle, open the door, let the human enter, speak with the guest while holding eye-contact, guide them to the living room, find them a free seat, and introduce this guest to any other guests. This process continues on repeat as long as there are new guests arriving. Team NimbRo’s robot opening the door. Another task is a pick and place task, and this time we had to clear a table after breakfast. So the robot had to
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oor, then find the specific point to grasp the handle to open the machine door. Then it had to pull out t-shirts and fold them. Our robot performed the best in this task. It was able to open the washing machine and start folding the laundry. And this was possible because we used these semi-automatic labeling approaches. What were the other tasks that teams needed to carry out? The first big hurdle at the competition
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