Presentation of the very best paper award on the RoboCup 2025 symposium.
An essential side 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 gained 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 seek out out extra in regards to the work, how their methodology might be transferred to purposes past RoboCup, and their future plans for the competitors.
May you begin by giving us a short description of the issue that you simply had been making an attempt to unravel in your paper “Self-supervised Function 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 information. 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’ll want 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 major human effort.
The thought behind our paper was to scale back this human effort. We approached the issue by means of self-supervised studying, which goals to be taught helpful representations of the info. In any case, deep studying is basically 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 growing it?
Daniele: Initially, let me introduce what self-supervised studying is. It’s a approach of studying the construction of the info with out accessing labels. That is normally achieved 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 info. For instance, in our case we labored with pictures. You possibly can randomly masks some patches and prepare the mannequin to foretell the lacking elements. By doing so, the mannequin is pressured to be taught significant options from the info.
In our paper, we enriched the info by utilizing not solely uncooked pictures but in addition exterior steering. This got here from a bigger mannequin which we seek advice from because the trainer. This mannequin was skilled on a special job which is extra common than the goal job we aimed for. This fashion the bigger mannequin can present steering (an exterior sign) that helps the self-supervision to focus extra on the precise job we care about.
In our case, we needed 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 will arguably say that the bounding field, a rectangle, is extra common than a circle. So on this sense, we had been making an attempt to make use of exterior steering that doesn’t clear up precisely the underlying job.
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 information. The mannequin was additionally extra strong below completely different lighting circumstances. The problem 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 essential to have a dependable mannequin and we actually observed an important enchancment this 12 months.
What’s your crew identify, and will you speak a bit in regards to the competitors and the way it went?
Daniele: So our crew 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 crew began with the four-legged robots. After which the league shifted extra in direction of biped robots as a result of they’re tougher, they require steadiness and, total it’s tougher to stroll on simply two legs.
Our crew has grown lots throughout latest years. We have now been following a really constructive pattern, going from ninth place in 2019 to 3rd place on the German Open in 2025, and we acquired 4th place at RoboCup 2025. Our latest success has attracted extra college students to the crew. 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 crew.
Domenico: I need to add that additionally, from a analysis standpoint, now we have gained three finest paper awards within the final 5 years, and now we have been proposing some new tendencies in direction of, for instance, using LLMs for coding (as a robotic’s behaviour generator below the supervision of a human coach). So we try to maintain the open analysis subject lively in our crew. We need to win the matches however we additionally need to clear up the analysis issues which can be certain along with the competitors.
One of many essential contributions of our paper is in direction of using our algorithms exterior RoboCup. For instance, we try 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 essential 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 a giant deal for us. We wish our college students, our crew members, to be open minded in direction of using RoboCup as a place to begin for understanding teamwork and for understanding learn how to take care of strict deadlines. That is one thing that RoboCup may give us. We attempt to have a crew that’s prepared for each sort of problem, not solely inside RoboCup, but in addition different forms of AI purposes. Profitable just isn’t all the things for us. We’d want to make use of our personal code and never win, than win utilizing code developed by others. This isn’t optimum for attaining first place, however we need to train our college students to be ready for the analysis that’s exterior of RoboCup.
You stated that you simply’ve beforehand gained two different finest paper awards. What did these papers cowl?
Domenico: So the final two finest papers had been type of visionary papers. In a single paper, we needed to provide an perception in learn how to use the spectators to assist the robots rating. For instance, in case you cheer louder, the robots are likely to kick the ball. So that is one thing that’s not really 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 referred to as “play all over the place”, so you may, for instance, play with various kinds of ball, you may play exterior, you may even play and not using a particular purpose, you may play utilizing Coca-Cola cans as goalposts. So the robotic has to have a common strategy that’s not associated to the precise subject utilized in RoboCup. That is in distinction to different groups which can be very particular. We have now a special strategy and that is one thing that makes it tougher for us to win the competitors. Nonetheless, we don’t need to win the competitors, we need to obtain this purpose of getting, in 2050, this match between the RoboCup winners and the FIFA World Cup winners.
I’m excited about what you stated about transferring the strategy for ball detection to farming and different purposes. May you say extra about that analysis?
Vincenzo Suriani: Our lab has been concerned in some completely different initiatives regarding farming purposes. The Flourish undertaking ran from 2015 – 2018. Extra just lately, the CANOPIES undertaking 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 have now one other undertaking that’s about detecting and harvesting grapes. There’s a large effort in bringing data again from RoboCup to different initiatives, and vice versa.
Domenico: Our imaginative and prescient now could be to deal with 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 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. In case you have a humanoid robotic that should transfer, you may reuse the identical code from RoboCup as a result of you should utilize 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 totally completely different sort 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 utilize the ends in different fields and in different purposes.
Trying particularly at RoboCup, what are your future plans for the crew? There are some huge adjustments deliberate for the RoboCup Leagues, so might you additionally say how this would possibly have an effect on your plans?
Domenico: We have now a really sturdy crew and a number of the crew members will do a PhD within the coming years. One in all 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 in regards to the RoboCup competitors and about AI usually.
By way of the adjustments, there can 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 affix the brand new merged league, or transfer to adult-sized robots. In the intervening time 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 try to have an entire household of latest robots. After which I feel we are going to go in direction of the league that’s chosen by the opposite groups within the SPL league. However for now we try to arrange an occasion in October in Rome with two different groups to alternate concepts and to know the place we need to go. There will even be a workshop to debate the analysis facet.
Vincenzo: We’re additionally in dialogue about the very best dimension 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 can be pushing to maneuver extra shortly to an even bigger platform. Alternatively, there are groups that need to persist with a smaller platform as a way to do analysis on multi brokers. We have now seen a variety 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 elements of analysis we did in RoboCup, and likewise exterior of RoboCup.
There are many factors of view on which robotic dimension to make use of, as a result of there are a number of components, and we don’t understand how quick the world will change in two or three years. We try to form the foundations 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 can be. And in addition the analysis we’re going to do can be affected by the choice we make on this.
There can 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. So much will occur within the subsequent 5 years, most likely greater than over the last 10-15 years. It is a vital 12 months as a result of the choices are primarily based 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 a giant, most likely the largest, neighborhood among the many RoboCup leagues. We have now a variety of groups which can be grouping by curiosity and so there are groups which can be sticking to engaged on this particular drawback with a particular platform and groups which can be making an attempt to maneuver to a different platform and one other drawback. So even inside the identical neighborhood we’re going to have a couple of standpoint and hopes for the longer term. At a sure level we are going to attempt to determine what’s the finest for all of them.
Daniele: I simply need to add that as a way to obtain the 2050 problem, for my part, 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, as a way to compete with people, there should be just one league bringing all these single facets collectively. From my standpoint, it completely is sensible to maintain merging leagues collectively.
In regards to the authors
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Daniele Affinita is a PhD scholar 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 crew. In 2024, he labored at Sony on area adaptation strategies. He holds a Bachelor’s diploma in Laptop 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 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 Improvement 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 growing 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 Laptop 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 deal with picture evaluation, multi-robot coordination, visible notion and knowledge fusion. Dr. Bloisi conducts analysis within the subject 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 undertaking, unit chief for the PRIN PNRR RETINA undertaking, unit chief for the PRIN 2022 AIDA undertaking. Since 2015, he’s the crew supervisor of the SPQR robotic soccer crew collaborating within the RoboCup world competitions |
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Can Lin is a grasp scholar in Information 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 crew in September of 2024, specializing in duties associated to pc imaginative and prescient. |
Lucy Smith
is Managing Editor for AIhub.
