Autonomous cleaning robots for public spaces have potential for addressing current societal challenges, such as labor shortages and cleanliness in public spaces. Other application domains like autonomous driving, bin picking, or search and rescue have shown that benchmarking platforms and approaches in competitive settings can advance their respective research fields, resulting in more applicable systems under real-world conditions. For this paper, we analyzed seven semi-structured, qualitative stakeholder interviews about outdoor cleaning, identified current needs as well as limitations, and considered those results for the development of a benchmarking scenario based on the previous observations.

This paper presents findings from an exploratory needfinding study investigating the research current status and potential participation of the competitions on the robotics community towards four human-centric topics: safety, privacy, explainability, and federated learning. We conducted a survey with 34 participants across three distinguished European robotics consortia, nearly 60% of whom possessed over five years of research experience in robotics. Our qualitative and quantitative analysis revealed that current mainstream robotic researchers prioritize safety and explainability, expressing a greater willingness to invest in further research in these areas. Conversely, our results indicate that privacy and federated learning garner less attention and are perceived to have lower potential. Additionally, the study suggests a lack of enthusiasm within the robotics community for participating in competitions related to these topics. Based on these findings, we recommend targeting other communities, such as the machine learning community, for future competitions related to these four human-centric topics.

This research work aims to develop an analytical approach for optimizing team formation and predicting team performance in a competitive environment based on data on the competitors' skills prior to the team formation. There are several approaches in scientific literature to optimize and predict a team's performance. However, most studies employ fine-grained skill statistics of the individual members or constraints such as teams with a set group of members. Currently, no research tackles the highly constrained domain of the FIRST Robotics Competition. This research effort aims to fill this gap by providing an analytical method for optimizing and predicting team performance in a competitive environment while allowing these constraints and only using metrics on previous team performance, not on each individual member's performance. We apply our method to the drafting process of the FIRST Robotics competition, a domain in which the skills change year-over-year, team members change throughout the season, each match only has a superficial set of statistics, and alliance formation is key to competitive success. First, we develop a method that could extrapolate individual members' performance based on overall team performance. An alliance optimization algorithm is developed to optimize team formation and a deep neural network model is trained to predict the winning team, both using highly post-processed real-world data. Our method is able to successfully extract individual members' metrics from overall team statistics, form competitive teams, and predict the winning team with 84.08% accuracy.

This paper describes the full end-to-end design of our primary scoring agent in an aerial autonomous robotics competition from April 2023. As open-ended robotics competitions become more popular, we wish to begin documenting successful team designs and approaches. The intended audience of this paper is not only any future or potential participant in this particular national Defend The Republic (DTR) competition, but rather anyone thinking about designing their first robot or system to be entered in a competition with clear goals. Future DTR participants can and should either build on the ideas here, or find new alternate strategies that can defeat the most successful design last time. For non-DTR participants but students interested in robotics competitions, identifying the minimum viable system needed to be competitive is still important in helping manage time and prioritizing tasks that are crucial to competition success first.

A total of 13 teams from around the state came to the Washington County Fairgrounds last weekend for a robotics competition, formally called a "First Tech Challenge robotics meet." Such competitions change objectives every year. At this tournament, the games involved strategically stacking cones onto markers around an arena, a task each robot was specifically designed to do. Jim Pitcher, Coach Mentor of Washington 4-H's team, called Eaglebots, said hosting the event was its own team-building exercise. "It's for the love of the sport," he said.

Students from across the South showcased their skills at a robotics competition on Saturday, Aug. 27.

Mount Olive High School hosts its first robotics competition featuring 38 high school teams. Friday's overnight hours should see partly cloudy skies and temperatures are expected to start to significantly cool off. Friday's overnight hours should see partly cloudy skies and temperatures are expected to start to significantly cool off.

In the Hangar at the OC Fair & Event Center in Costa Mesa, high school students watched as robots rushed to fill spaceships with cargo before the next sandstorm arrived. The Orange County regional of the FIRST (For Inspiration and Recognition of Science and Technology) Robotics Competition drew more than 40 teams and their robots. Teams from Marina, Huntington Beach, Ocean View, Edison and Corona del Mar high schools were in attendance Friday. This year's theme is "Destination: Deep Space," inspired by the lunar landing in July 1969. The challenge is to have the robots affix plastic "hatch panels" to the sides of the rockets and cargo ships and fill each with orange balls (the cargo).

Participants prepare their work in a robotics challenge.Ready AI Most industrial robots are preprogrammed or remotely controlled. That's starting to change with the introduction of artificial intelligence (AI). Some robots operate autonomously and learn to improve task efficiency. The same is true about elementary and secondary school robotics competitions--few involve real AI. Usually, the robots are teleoperated to perform a task like picking up objects and putting them in a bucket.

The inaugural "Governor's Cup" competition is happening Saturday at Plymouth State University featuring more than 20 teams from high schools across the state. In addition to one-semester scholarships to the state's public universities and community colleges, scholarships worth $2,000 each will be awarded to five seniors.