Presentation of the very best paper award on the RoboCup 2025 symposium.
An vital facet of autonomous soccer-playing robots issues correct detection of the ball. That is the main focus of labor by Can Lin, Daniele Affinita, Marco Zimmatore, Daniele Nardi, Domenico Bloisi, and Vincenzo Suriani, which received the very best paper award on the latest RoboCup symposium. The symposium takes place alongside the annual RoboCup competitors, which this 12 months was held in Salvador, Brazil. We caught up with a number of the authors to search out out extra concerning the work, how their methodology may be transferred to purposes past RoboCup, and their future plans for the competitors.
May you begin by giving us a quick description of the issue that you simply have been attempting to resolve in your paper “Self-supervised Characteristic Extraction for Enhanced Ball Detection on Soccer Robots”?
Daniele Affinita: The primary problem we confronted was that deep studying usually requires a considerable amount of labeled information. This isn’t a significant downside for widespread duties which have already been studied, as a result of you possibly can often discover labeled datasets on-line. However when the duty is extremely particular, like in RoboCup, you have to accumulate and label the information your self. Meaning gathering the information and manually annotating it earlier than you possibly can even begin making use of deep studying. This course of is just not scalable and calls for a big human effort.
The thought behind our paper was to cut back this human effort. We approached the issue by means of self-supervised studying, which goals to study helpful representations of the information. In spite of everything, deep studying is actually about studying latent representations from the obtainable information.
May you inform us a bit extra about your self-supervised studying framework and the way you went about creating it?
Daniele: To begin with, let me introduce what self-supervised studying is. It’s a manner of studying the construction of the information with out gaining access to labels. That is often completed by means of what we name pretext duties. These are duties that don’t require specific labels, however as an alternative exploit the construction of the information. For instance, in our case we labored with pictures. You possibly can randomly masks some patches and practice the mannequin to foretell the lacking components. By doing so, the mannequin is pressured to study significant options from the information.
In our paper, we enriched the information by utilizing not solely uncooked pictures but additionally exterior steering. This got here from a bigger mannequin which we check with because the trainer. This mannequin was skilled on a unique process which is extra normal than the goal process we aimed for. This manner the bigger mannequin can present steering (an exterior sign) that helps the self-supervision to focus extra on the particular process we care about.
In our case, we wished to foretell a good circle across the ball. To information this, we used an exterior pretrained mannequin (YOLO) for object detection, which as an alternative predicts a free bounding field across the ball. We are able to arguably say that the bounding field, a rectangle, is extra normal than a circle. So on this sense, we have been attempting to make use of exterior steering that doesn’t remedy precisely the underlying process.
Overview of the information preparation pipeline.
Have been you in a position to take a look at this mannequin out at RoboCup 2025?
Daniele: Sure, we deployed it at RoboCup 2025 and confirmed nice enhancements over our earlier benchmark, which was the mannequin we utilized in 2024. Specifically, we observed that the ultimate coaching requires a lot much less information. The mannequin was additionally extra strong below totally different lighting circumstances. The difficulty we had with earlier fashions was that they have been tailor-made for particular conditions. However after all, all of the venues are totally different, the lighting and the brightness are totally different, there may be shadows on the sphere. So it’s actually vital to have a dependable mannequin and we actually observed an amazing enchancment this 12 months.
What’s your staff title, and will you speak a bit concerning the competitors and the way it went?
Daniele: So our staff is SPQR. We’re from Rome, and now we have been competing in RoboCup for a very long time.
Domenico Blois: We began in 1998, so we’re one of many oldest groups in RoboCup.
Daniele: Yeah, I wasn’t even born then! Our staff began with the four-legged robots. After which the league shifted extra in direction of biped robots as a result of they’re more difficult, they require stability and, total it’s more durable to stroll on simply two legs.
Our staff has grown quite a bit throughout latest years. We’ve been following a really constructive pattern, going from ninth place in 2019 to 3rd place on the German Open in 2025, and we received 4th place at RoboCup 2025. Our latest success has attracted extra college students to the staff. So it’s type of a loop – you win extra, you appeal to extra college students, and you’ll work extra on the challenges proposed by RoboCup.
SPQR staff.
Domenico: I need to add that additionally, from a analysis viewpoint, now we have received three finest paper awards within the final 5 years, and now we have been proposing some new developments in direction of, for instance, the usage of LLMs for coding (as a robotic’s behaviour generator below the supervision of a human coach). So we are attempting to maintain the open analysis discipline lively in our staff. We need to win the matches however we additionally need to remedy the analysis issues which can be sure along with the competitors.
One of many vital contributions of our paper is in direction of the usage of our algorithms outdoors RoboCup. For instance, we are attempting to use the ball detector in precision farming. We need to use the identical method to detect rounded fruits. That is one thing that’s actually vital for us; to exit the context of Robocup and to make use of Robocup instruments for brand spanking new approaches in different fields. So if we lose a match, it’s not an enormous deal for us. We would like our college students, our staff members, to be open minded in direction of the usage of RoboCup as a place to begin for understanding teamwork and for understanding learn how to cope with strict deadlines. That is one thing that RoboCup may give us. We attempt to have a staff that’s prepared for each kind of problem, not solely inside RoboCup, but additionally different sorts of AI purposes. Profitable is just not every thing for us. We’d favor to make use of our personal code and never win, than win utilizing code developed by others. This isn’t optimum for reaching first place, however we need to educate our college students to be ready for the analysis that’s outdoors of RoboCup.
You stated that you simply’ve beforehand received two different finest paper awards. What did these papers cowl?
Domenico: So the final two finest papers have been type of visionary papers. In a single paper, we wished to offer an perception in learn how to use the spectators to assist the robots rating. For instance, for those who cheer louder, the robots are likely to kick the ball. So that is one thing that isn’t truly used within the competitors now, however is one thing extra in direction of the 2050 problem. So we need to think about how it will likely be 10 years from now.
The different paper was known as “play all over the place”, so you possibly can, for instance, play with various kinds of ball, you possibly can play outdoors, you possibly can even play with no particular objective, you possibly can play utilizing Coca-Cola cans as goalposts. So the robotic has to have a normal method that isn’t associated to the particular discipline utilized in RoboCup. That is in distinction to different groups which can be very particular. We’ve a unique method and that is one thing that makes it more durable for us to win the competitors. Nonetheless, we don’t need to win the competitors, we need to obtain this objective of getting, in 2050, this match between the RoboCup winners and the FIFA World Cup winners.
I’m all in favour of what you stated about transferring the tactic for ball detection to farming and different purposes. May you say extra about that analysis?
Vincenzo Suriani: Our lab has been concerned in some totally different tasks referring to farming purposes. The Flourish mission ran from 2015 – 2018. Extra just lately, the CANOPIES mission has focussed on precision agriculture for everlasting crops the place farmworkers can effectively work along with groups of robots to carry out agronomic interventions, like harvesting or pruning.
We’ve one other mission that’s about detecting and harvesting grapes. There’s a large effort in bringing information again from RoboCup to different tasks, and vice versa.
Domenico: Our imaginative and prescient now could be to concentrate on the brand new technology of humanoid robots. We participated in a brand new occasion, the World Humanoid Robotic Video games, held in Beijing in August 2025, as a result of we need to use the platform of RoboCup for different kinds of purposes. The thought is to have a single platform with software program that’s derived from RoboCup code that can be utilized for different purposes. When you’ve got a humanoid robotic that should transfer, you possibly can reuse the identical code from RoboCup as a result of you should use the identical stabilization, the identical imaginative and prescient core, the identical framework (kind of), and you’ll simply change some modules and you’ll have a very totally different kind of utility with the identical robotic with kind of the identical code. We need to go in direction of this concept of reusing code and having RoboCup as a take a look at mattress. It’s a very robust take a look at mattress, however you should use the ends in different fields and in different purposes.
Trying particularly at RoboCup, what are your future plans for the staff? There are some large adjustments deliberate for the RoboCup Leagues, so may you additionally say how this may have an effect on your plans?
Domenico: We’ve a really sturdy staff and a number of the staff members will do a PhD within the coming years. Certainly one of our targets was to maintain the scholars contained in the college and the analysis ward, and we have been profitable on this, as a result of now they’re very passionate concerning the RoboCup competitors and about AI on the whole.
By way of the adjustments, there might be a brand new league inside RoboCup that could be a merger of the usual platform league (SPL) and the humanoid kid-size league. The humanoid adult-size league will stay, so we have to resolve whether or not to hitch the brand new merged league, or transfer to adult-sized robots. For the time being we don’t have too many particulars, however what we all know is that we’ll go in direction of a brand new period of robots. We acquired robots from Booster and we at the moment are buying one other G1 robotic from Unitree. So we are attempting to have an entire household of latest robots. After which I feel we’ll go in direction of the league that’s chosen by the opposite groups within the SPL league. However for now we are attempting to prepare an occasion in October in Rome with two different groups to change concepts and to know the place we need to go. There may even be a workshop to debate the analysis aspect.
Vincenzo: We’re additionally in dialogue about the very best measurement of robotic for the competitors. We’re going to have two totally different positions, as a result of robots have gotten cheaper and there are groups which can be pushing to maneuver extra shortly to a much bigger platform. Then again, there are groups that need to keep on with a smaller platform with the intention to do analysis on multi brokers. We’ve seen plenty of purposes for a single robotic however not many purposes with a set of robots which can be cooperating. And this has been traditionally one of many core components of analysis we did in RoboCup, and likewise outdoors of RoboCup.
There are many factors of view on which robotic measurement to make use of, as a result of there are a number of components, and we don’t know the way quick the world will change in two or three years. We are attempting to form the principles and the circumstances to play for subsequent 12 months, however, due to how shortly issues are altering, we don’t know what the very best choice might be. And in addition the analysis we’re going to do might be affected by the choice we make on this.
There might be some adjustments to different leagues within the close to future too; the small and center sizes will shut in two years most likely, and the simulation league additionally. Loads will occur within the subsequent 5 years, most likely greater than over the past 10-15 years. This can be a essential 12 months as a result of the choices are based mostly on what we will see, what we will spot sooner or later, however we don’t have all the data we want, so it will likely be difficult.
For instance, the SPL has an enormous, most likely the largest, group among the many RoboCup leagues. We’ve plenty of groups which can be grouping by curiosity and so there are groups which can be sticking to engaged on this particular downside with a selected platform and groups which can be attempting to maneuver to a different platform and one other downside. So even inside the identical group we’re going to have multiple viewpoint and hopes for the longer term. At a sure level we’ll attempt to determine what’s the finest for all of them.
Daniele: I simply need to add that with the intention to obtain the 2050 problem, in my view, it’s essential to have only one league encompassing every thing. So up thus far, totally different leagues have been specializing in totally different analysis issues. There have been leagues focusing solely on technique, others focusing solely on the {hardware}, our league focusing primarily on the coordination and dynamic dealing with of the gameplay. However on the finish of the day, with the intention to compete with people, there should be just one league bringing all these single features collectively. From my viewpoint, it completely is smart to maintain merging leagues collectively.
Concerning the authors
 | Daniele Affinita is a PhD pupil in Machine Studying at EPFL, specializing within the intersection of Machine Studying and Robotics. He has over 4 years of expertise competing in RoboCup with the SPQR staff. In 2024, he labored at Sony on area adaptation methods. He holds a Bachelor’s diploma in Laptop Engineering and a Grasp’s diploma in Synthetic Intelligence and Robotics from Sapienza College of Rome. |
 | Vincenzo Suriani earned his Ph.D. in Laptop Engineering in 2024 from Sapienza College of Rome, with a specialization in synthetic intelligence, robotic imaginative and prescient, and multi-agent coordination. Since 2016, he has served as Software program Growth Chief of the Sapienza Soccer Robotic Crew, contributing to main robotic competitions and worldwide initiatives resembling EUROBENCH, SciRoc, and Tech4YOU. He’s presently a Analysis Fellow on the College of Basilicata, the place he focuses on creating clever environments for software program testing automation. His analysis, acknowledged with award-winning papers on the RoboCup Worldwide Symposium (2021, 2023, 2025), facilities on robotic semantic mapping, object recognition, and human–robotic interplay. |
 | Domenico Daniele Bloisi is an affiliate professor of Synthetic Intelligence on the Worldwide College of Rome UNINT. Beforehand, he was affiliate professor on the College of Basilicata, assistant professor on the College of Verona, and assistant professor at Sapienza College of Rome. He acquired his PhD, grasp’s and bachelor’s levels in Laptop Engineering from Sapienza College of Rome in 2010, 2006 and 2004, respectively. He’s the creator of greater than 80 peer-reviewed papers printed in worldwide journals and conferences within the discipline of synthetic intelligence and robotics, with a concentrate on picture evaluation, multi-robot coordination, visible notion and data fusion. Dr. Bloisi conducts analysis within the discipline of melanoma and oral carcinoma prevention by means of computerized medical picture evaluation in collaboration with specialised medical groups in Italy. As well as, Dr. Bloisi is WP3 chief of the EU H2020 SOLARIS mission, unit chief for the PRIN PNRR RETINA mission, unit chief for the PRIN 2022 AIDA mission. Since 2015, he’s the staff supervisor of the SPQR robotic soccer staff collaborating within the RoboCup world competitions |
 | Can Lin is a grasp pupil in Knowledge Science at Sapienza college of Rome. He holds a bachelor diploma in Laptop science and Synthetic intelligence from the identical college. He joined the SPQR staff in September of 2024, specializing in duties associated to pc imaginative and prescient. |
Lucy Smith
is Managing Editor for AIhub.
