Presentation of the most effective paper award on the RoboCup 2025 symposium.
An necessary facet of autonomous soccer-playing robots issues correct detection of the ball. That is the main target of labor by Can Lin, Daniele Affinita, Marco Zimmatore, Daniele Nardi, Domenico Bloisi, and Vincenzo Suriani, which received the most effective paper award on the latest RoboCup symposium. The symposium takes place alongside the annual RoboCup competitors, which this yr was held in Salvador, Brazil. We caught up with a few of the authors to search out out extra concerning the work, how their methodology will be transferred to functions past RoboCup, and their future plans for the competitors.
Might you begin by giving us a quick description of the issue that you simply had been making an attempt to unravel in your paper “Self-supervised Characteristic Extraction for Enhanced Ball Detection on Soccer Robots”?
Daniele Affinita: The principle problem we confronted was that deep studying typically requires a considerable amount of labeled knowledge. This isn’t a serious drawback for widespread duties which have already been studied, as a result of you may normally discover labeled datasets on-line. However when the duty is extremely particular, like in RoboCup, you have to acquire and label the info your self. Which means gathering the info and manually annotating it earlier than you may even begin making use of deep studying. This course of just isn’t scalable and calls for a big human effort.
The concept behind our paper was to scale back this human effort. We approached the issue via self-supervised studying, which goals to study helpful representations of the info. In spite of everything, deep studying is actually about studying latent representations from the out there knowledge.
Might you inform us a bit extra about your self-supervised studying framework and the way you went about creating it?
Daniele: Initially, let me introduce what self-supervised studying is. It’s a method of studying the construction of the info with out gaining access to labels. That is normally performed via what we name pretext duties. These are duties that don’t require specific labels, however as a substitute exploit the construction of the info. For instance, in our case we labored with pictures. You’ll be able to randomly masks some patches and prepare the mannequin to foretell the lacking components. By doing so, the mannequin is pressured to study significant options from the info.
In our paper, we enriched the info through the use of 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 educated on a unique process which is extra basic 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 decent circle across the ball. To information this, we used an exterior pretrained mannequin (YOLO) for object detection, which as a substitute predicts a unfastened bounding field across the ball. We are able to arguably say that the bounding field, a rectangle, is extra basic than a circle. So on this sense, we had been making an attempt to make use of exterior steering that doesn’t remedy precisely the underlying process.
Overview of the info 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 knowledge. The mannequin was additionally extra sturdy underneath completely different lighting circumstances. The difficulty we had with earlier fashions was that they had been tailor-made for particular conditions. However in fact, all of the venues are completely different, the lighting and the brightness are completely different, there may be shadows on the sector. So it’s actually necessary to have a dependable mannequin and we actually observed an important enchancment this yr.
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 we’ve 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 the direction of biped robots as a result of they’re more difficult, they require steadiness and, general it’s more durable to stroll on simply two legs.
Our staff has grown so much throughout latest years. We’ve been following a really constructive development, going from ninth place in 2019 to 3rd place on the German Open in 2025, and we bought 4th place at RoboCup 2025. Our latest success has attracted extra college students to the staff. So it’s sort of a loop – you win extra, you entice 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 perspective, we’ve received three greatest paper awards within the final 5 years, and we’ve been proposing some new developments in the direction of, for instance, the usage of LLMs for coding (as a robotic’s behaviour generator underneath the supervision of a human coach). So we are attempting to maintain the open analysis subject energetic in our staff. We need to win the matches however we additionally need to remedy the analysis issues which are certain along with the competitors.
One of many necessary contributions of our paper is in the direction of the usage of our algorithms exterior RoboCup. For instance, we are attempting to use the ball detector in precision farming. We need to use the identical strategy to detect rounded fruits. That is one thing that’s actually necessary 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 wish our college students, our staff members, to be open minded in the direction of the usage of RoboCup as a place to begin for understanding teamwork and for understanding find out how to take care of strict deadlines. That is one thing that RoboCup may give us. We attempt to have a staff that’s prepared for each sort of problem, not solely inside RoboCup, but additionally different forms of AI functions. Profitable just isn’t all the things 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 exterior of RoboCup.
You stated that you simply’ve beforehand received two different greatest paper awards. What did these papers cowl?
Domenico: So the final two greatest papers had been sort of visionary papers. In a single paper, we wished to provide an perception in find out how to use the spectators to assist the robots rating. For instance, when you cheer louder, the robots are likely to kick the ball. So that is one thing that isn’t really used within the competitors now, however is one thing extra in the direction of the 2050 problem. So we need to think about how will probably be 10 years from now.
The different paper was referred to as “play in every single place”, so you may, for instance, play with various kinds of ball, you may play exterior, you may even play with out a particular aim, you may play utilizing Coca-Cola cans as goalposts. So the robotic has to have a basic strategy that isn’t associated to the particular subject utilized in RoboCup. That is in distinction to different groups which are very particular. We’ve a unique strategy and that is one thing that makes it more durable for us to win the competitors. Nevertheless, we don’t need to win the competitors, we need to obtain this aim of getting, in 2050, this match between the RoboCup winners and the FIFA World Cup winners.
I’m enthusiastic about what you stated about transferring the tactic for ball detection to farming and different functions. Might you say extra about that analysis?
Vincenzo Suriani: Our lab has been concerned in some completely different initiatives regarding farming functions. 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 big effort in bringing information again from RoboCup to different initiatives, and vice versa.
Domenico: Our imaginative and prescient now’s to concentrate on the brand new era 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 functions. The concept is to have a single platform with software program that’s derived from RoboCup code that can be utilized for different functions. You probably have a humanoid robotic that should transfer, you may 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 completely different sort of utility with the identical robotic with kind of the identical code. We need to go in the 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 functions.
Trying particularly at RoboCup, what are your future plans for the staff? There are some massive modifications deliberate for the RoboCup Leagues, so may you additionally say how this may have an effect on your plans?
Domenico: We’ve a really robust staff and a few of the staff members will do a PhD within the coming years. One among our targets was to maintain the scholars contained in the college and the analysis ward, and we had been profitable on this, as a result of now they’re very passionate concerning the RoboCup competitors and about AI basically.
By way of the modifications, 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 determine whether or not to affix 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 the direction of a brand new period of robots. We acquired robots from Booster and we are actually buying one other G1 robotic from Unitree. So we are attempting to have a whole household of recent robots. After which I feel we’ll go in the 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 trade concepts and to know the place we need to go. There will even be a workshop to debate the analysis aspect.
Vincenzo: We’re additionally in dialogue about the most effective measurement of robotic for the competitors. We’re going to have two completely different positions, as a result of robots have gotten cheaper and there are groups which are pushing to maneuver extra rapidly to a much bigger platform. Alternatively, there are groups that need to persist with a smaller platform with a purpose to do analysis on multi brokers. We’ve seen a number of functions for a single robotic however not many functions with a set of robots which are cooperating. And this has been traditionally one of many core components of analysis we did in RoboCup, and likewise exterior 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 elements, and we don’t understand how quick the world will change in two or three years. We are attempting to form the principles and the circumstances to play for subsequent yr, however, due to how rapidly issues are altering, we don’t know what the most effective 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 modifications to different leagues within the close to future too; the small and center sizes will shut in two years in all probability, and the simulation league additionally. Lots will occur within the subsequent 5 years, in all probability greater than over the last 10-15 years. This can be a essential yr as a result of the selections are based mostly on what we will see, what we will spot sooner or later, however we don’t have all the knowledge we’d like, so will probably be difficult.
For instance, the SPL has an enormous, in all probability the most important, group among the many RoboCup leagues. We’ve a number of groups which are grouping by curiosity and so there are groups which are sticking to engaged on this particular drawback with a selected platform and groups which are making an attempt to maneuver to a different platform and one other drawback. So even inside the identical group we’re going to have multiple perspective and hopes for the longer term. At a sure level we’ll attempt to determine what’s the greatest for all of them.
Daniele: I simply need to add that with a purpose to obtain the 2050 problem, in my view, it’s essential to have only one league encompassing all the things. So up up to now, completely different leagues have been specializing in completely 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 a purpose to compete with people, there have to be just one league bringing all these single points collectively. From my perspective, it completely is sensible to maintain merging leagues collectively.
Concerning the authors
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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 Pc Engineering and a Grasp’s diploma in Synthetic Intelligence and Robotics from Sapienza College of Rome. |
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Vincenzo Suriani earned his Ph.D. in Pc 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 Improvement Chief of the Sapienza Soccer Robotic Crew, contributing to main robotic competitions and worldwide initiatives reminiscent of 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. |
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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 obtained his PhD, grasp’s and bachelor’s levels in Pc Engineering from Sapienza College of Rome in 2010, 2006 and 2004, respectively. He’s the writer of greater than 80 peer-reviewed papers printed in worldwide journals and conferences within the subject of synthetic intelligence and robotics, with a concentrate on picture evaluation, multi-robot coordination, visible notion and knowledge fusion. Dr. Bloisi conducts analysis within the subject of melanoma and oral carcinoma prevention via automated 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 taking part within the RoboCup world competitions |
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Can Lin is a grasp pupil in Knowledge Science at Sapienza college of Rome. He holds a bachelor diploma in Pc science and Synthetic intelligence from the identical college. He joined the SPQR staff in September of 2024, specializing in duties associated to laptop imaginative and prescient. |
Lucy Smith
is Managing Editor for AIhub.
