Category robots

The Standard Platform Soccer League in action.

This is the second of our daily digests from RoboCup2024 in Eindhoven, The Netherlands. If you missed the first digest, which gives some background to RoboCup, you can find it here.

Competitions continued across all the leagues today, with participants vying for a place in Sunday’s finals.

The RoboCup@Work league focusses on robots in work-related scenarios, utilizing ideas and concepts from other RoboCup competitions to tackle open research challenges in industrial and service robotics.

I arrived at the arena in time to catch the advanced navigation test. Robots have to autonomously navigate, picking up and placing objects at different work stations. In this advanced test, caution tape is added to the arena floor, which the robots should avoid travelling over. There is also a complex placing element where teams have to put an object that they’ve collected into a slot – get the orientation or placement of the object slightly wrong and the it won’t fall into the slot.

The RoboCup@Work arena just before competition start.

Eight teams are taking part in the league this year. Executive Committee member Asad Norouzi said that there are plans to introduce a sub-league which would provide an entry point for new teams or juniors to get into the league proper.

I caught up with Harrison Burns, Mitchell Torok and Jasper Arnold from Team MiRobot. They are based at the University of New South Wales and are attending RoboCup for the first time.

Team MiRobot from UNSW.

The team actually only started six months ago, so final preparations have been a bit stressful. However, the experience has been great fun, and the competition has gone well so far. Like most teams, they’ve had to make many refinements as the competition has progressed, leading to some late nights.

One notable feature of the team’s robot is the bespoke, in-house-designed grasping mechanism on the end of the arm. The team note that “it has good flexible jaws, so when it grabs round objects it actually pulls the object directly into it. Because it uses a linear motion, compared to a lot of other rotating jaws, it has a lot better reliability for picking up objects”.

Here is some footage from the task, featuring Team bi-t-bots and Team Singapore.

Team b-it-bots take on the RoboCup@Work advanced navigation test, picking up a drill bit #RoboCup2024 pic.twitter.com/QfijZpxaOK

— AIhub (@aihuborg) July 20, 2024

Team Singapore placing an object in the RoboCup@Work advanced navigation test pic.twitter.com/SvgLBOaVo7

— AIhub (@aihuborg) July 20, 2024

In the Middle Size Soccer league (MSL), teams of five fully autonomous robots play with a regular size FIFA ball. Teams are free to design their own hardware but all sensors have to be on-board and there is a maximum size and weight limit of 40kg for the robots. The research focus is on mechatronics design, control and multi-agent cooperation at plan and perception levels. Nine teams are competing this year.

Action from the Middle Size League at #RoboCup2024.

Falcons vs Robot Club Toulon pic.twitter.com/GHcNLOx2nV

— AIhub (@aihuborg) July 20, 2024

I spoke to António Ribeiro, who is a member of the technical committee and part of Team LAR@MSL from the University of Minho, Portugal. The team started in 1998, but António and most of his colleagues on the current team have only been involved in the MSL since September 2022. The robots have evolved as the competition has progressed, and further improvements are in progress. Refinements so far have included communication, the detection system, and the control system. They are pleased with the improvements from the previous RoboCup. “Last year we had a lot of hardware issues, but this year the hardware seems pretty stable. We also changed our coding architecture and it is now much easier and faster for us to develop code because we can all work on the code at the same time on different modules”.

António cited versatility and cost-effective solutions as strengths of the team. “Our robot is actually very cheap compared to other teams. We use a lot of old chassis, and our solutions always go to the lowest cost possible. Some teams have multiple thousand dollar robots, but, for example, our vision system is around $70-80. It works pretty well – we need to improve the way we handle it, but it seems stable”.

Team LAR@MSL

The RoboCup@Home league aims to develop service and assistive robot technology with high relevance for future personal domestic applications. A set of benchmark tests is used to evaluate the robots’ abilities and performance in a realistic non-standardized home environment setting. These tests include helping to prepare breakfast, clearing the table, and storing groceries.

I arrived in time to watch the “stickler for the rules” challenge, where robots have to navigate different rooms and make sure that the people inside (“guests” at a party) are sticking to four rules: 1) there is one forbidden room – if a guest is in there the robot must alert them and ask them to follow it into another room), 2) everyone must have a drink in their hand – if not, the robot directs them to a shelf with drinks, 3) no shoes to be worn in the house, 4) there should be no rubbish left on the floor.

After watching an attempt from the LAR@Home robot, Tiago from the team told me a bit about the robot. “The goal is to develop a robot capable of multi general-purpose tasks in home and healthcare environments.” With the exception of the robotic arm, all of the hardware was built by the team. The robot has two RGBD cameras, two LIDARs, a tray (where the robot can store items that it needs to carry), and two emergency stop buttons that deactivate all moving parts. Four omnidirectional wheels allow the robot to move in any direction at any time. The wheels have independent suspension systems which guarantees that they can all be on the ground at all times, even if there are bumps and cables on the venue floor. There is a tablet that acts as a visual interface, and a microphone and speakers to enable communication between humans and the robot, which is all done via speaking and listening.

Tiago told me that the team have talked to a lot healthcare practitioners to find out the main problems faced by elderly people, and this inspired one of their robot features. “They said that the two main injury sources are from when people are trying to sit down or stand up, and when they are trying to pick something up from the floor. We developed a torso that can pick objects from the floor one metre away from the robot”.

The LAR@Home team.

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You can keep up with the latest news direct from RoboCup here.

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The main soccer arena.

RoboCup is an international scientific initiative with the goal to advance the state of the art of intelligent robots. As part of this initiative, a series of competitions and events are held throughout the year. The main showcase event is an international affair with teams travelling from far and wide to put their machines through their paces.

This year, RoboCup is being held in three arenas in the Genneper Parken, Eindhoven, The Netherlands. The organisers are expecting over 2,000 participants, from 45 different countries, with around 300 teams signed up to take part in the various competitions.

Although RoboCup started out as a football (or soccer) playing competition, other leagues have since been introduced, focussing on robots in industrial, rescue, and home settings. There is even a dedicated league for young roboticists – RoboCupJunior – where participants can take part in either football, rescue, or artistic events.

I am lucky enough to be able to attend this year, and, for the next three days, I’ll be bringing you a daily digest of some of the exciting happenings from Eindhoven.

Today, 19 July, sees the competition in full swing. The main soccer arena, boasting multiple pitches, hosts a number of the different leagues which form RoboCupSoccer.

Some of the pitches in the main soccer arena.

My first port of call was the Standard Platform League, where the round 5 champions cup match between SPQR Team vs rUNSWift was taking place. SPQR ran out winners and advance to round 6. In this league, all teams compete with identical robots (currently the humanoid NAO by Aldebaran). The robots operate fully autonomously, meaning that there is no external control from neither humans nor computers.

Standard platform league. Round 5 champions cup match between SPQR Team vs rUNSWift.

Goal! pic.twitter.com/dMfNDUKNZc

— AIhub (@aihuborg) July 19, 2024

The Humanoid AdultSize league is arguably the most challenging of the leagues, with many constraints placed on the robots to make them as human-like as possible. For example, they must have roughly human-like body proportions, they need to walk on two legs, and they are only allowed to use human-like sensors (up to two cameras to sense the environment). In this AdultSize competition, two robots from each team compete, and the team members walk behind the robots to catch them in case of a fall. Such a mishap could prove costly in terms of potential hardware damage.

Action from the Humanoid AdultSize League.

The RoboCup Rescue Robot League sees teams developing robotic systems with the goal of enabling emergency responders to perform extremely hazardous tasks from safer stand-off distances. During the competition, teams compete in a round-robin, putting their robots through their paces on a number of different challenges. The leading teams following this initial phase progress to the finals on Sunday. The tasks include navigating in complex environments, opening doors, and sensing. Teams may run the machines completely autonomously, or with some assistive control. More points are awarded for completely autonomous operation.

RoboCup Rescue arena from above.

Some action from the @robocup_org #RoboCup2024 Rescue league, where teams compete in a variety of challenges.

Team Hector Darmstadt in the "Obstacles: pallets with pipes" challenge pic.twitter.com/4Ll75uENjM

— AIhub (@aihuborg) July 19, 2024

KMUTNB navigate rough terrain, including gravel and sand pic.twitter.com/rsI7NliEwd

— AIhub (@aihuborg) July 19, 2024

You can keep up with more RoboCup2024 news here.

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In today’s rapidly evolving AI landscape, strong governance is more critical than ever as organizations strive to harness the power of AI. Drawing on DataRobot’s ten years of experience in enterprise AI, we have dedicated ourselves to building an AI platform with the highest ranking for Governance Use Case among all 18 recognized vendors by Gartner^® in the market and a governance framework that we believe exceeds industry standards.

At DataRobot, we’ve always prioritized establishing a solid AI governance framework that ensures our customers can build, deploy and monitor generative and predictive AI assets with confidence. This framework helps teams maintain the quality and integrity of assets in production which is key in ensuring sustainable value. 

We believe this commitment has led us to be ranked the highest in Governance Use Case by Gartner^®, with an impressive 4.10 out of 5 governance score. In our opinion, this recognition is a testament to our unwavering dedication to upholding the highest standards of integrity, quality, and transparency across all AI operations.

The Growing Need for Strong AI and Data Governance

With the advent of generative AI, the demand for reliable governance has never been stronger or more urgent. As AI continues to become more deeply embedded across all sectors, the potential risks associated with its deployment grow accordingly. 

In 2023 alone, the AI industry saw a 40% increase in reported incidents related to data breaches and model bias, highlighting the urgent need for robust governance frameworks. According to a recent survey by PwC, 85% of AI leaders cite governance as their top concern, emphasizing the importance of trust, confidence, and the security of valuable intellectual property. 

At DataRobot, our AI governance capabilities are specifically designed to address these critical needs. Our platform provides comprehensive tools and protocols to bridge the confidence gap for our customers.

DataRobot enables the rapid and secure deployment of machine learning and generative AI applications into production within an average timeframe of 2 to 4 weeks. This accelerated deployment is facilitated by features such as automated compliance documentation, real-time risk management,  full model transparency, and most importantly strong guards and intervention methods.

This dual focus on governance and speed means that our customers can maintain a competitive advantage in AI without worrying about reputation damage or costly compliance issues. 

Key Governance Features that Set DataRobot Apart

AI has always been a team sport, and generative AI has made AI assets more accessible to a broader set of users, increasing the need for collaboration. Meeting high governance standards across all phases of the AI lifecycle — building, testing, production and management — is a must.

DataRobot Governance Umbrella

The DataRobot Governance Umbrella encapsulates our comprehensive approach to governance standards for both ML and GenAI development and management.

Our AI governance framework is designed to ensure that AI solutions are effective, efficient, and compliant, ultimately ensuring value with AI. It also extends compliance capabilities by ensuring that risk is mitigated across all AI assets throughout the end-to-end AI process:

• Build phase: Data scientists and AI practitioners lay the groundwork for creating robust AI solutions.
• Testing phase: Models undergo rigorous testing to ensure they meet our standards and perform reliably under various conditions.
• Production phase: Models are deployed and managed in a live environment.
• Monitoring and management phase: Oversight and governance tools help teams maintain the compliance, integrity and accuracy of AI solutions for operational excellence.

Our framework safeguards AI models and aligns them with operational and compliance goals. To do so, the DataRobot platform offers six unique features that empower each phase in the framework and make DataRobot stand out:

1. Visibility and Traceability: Full traceability of data, model lineage and versioning ensures that every change is tracked and documented which makes applications safe and useful.
2. Audits and Compliance Documentation: Automated generation of compliance reports and audit trails to meet regulatory requirements and fulfill transparency.
3. Unique LLM Evaluation and Testing: Detect potential risks utilizing both synthetic and real datasets to evaluate your predictive and generative AI models and benchmark performance.
4. CI/CD Testing: The ability to run prototype testing and evaluate ML or generative solutions with quality metrics to rank RAG experiments.
5. Real-time Intervention, Moderation and Alerting: Continuous monitoring with instant notifications and intervention capabilities enabled by guard models and metrics to address issues as they arise.
6. AI Catalog: Easily register, track and version all AI assets, whether they were built on or off the DataRobot AI Platform, all through a secure centralized hub.

Secure Collaboration Across Teams

From the build to the management phase, we prioritize data privacy, security, and efficiency as our customers like to move fast.

The Workbench in DataRobot provides an integrated environment for developing AI use cases with features for automated artifact registration for code, prompts, experiments and more. This helps accelerate the creation and iteration of useful AI models without sacrificing safety, or limiting collaboration. 

The Registry in DataRobot enables AI practitioners to catalog, version, and govern all AI assets ensuring more control over models. Encryption at rest and the option to bring your own key (BYOK) features ensure that our clients’ information is protected at all times, reinforcing trust and reliability.

Flexibility and Adaptability

The DataRobot AI Platform is one of the most open platforms for AI. We give our users the ultimate control and choice when it comes to their generative AI initiatives. The platform supports custom-built models, third-party APIs, and open source LLMs to prevent vendor lock-in and technical debt, and protect sensitive data.  

This flexibility and full governance behind company firewalls ensure that our customers can adapt their AI initiatives to meet evolving business needs with trusted security. Our platform also offers built-in GPU support to accelerate model training and processing, enabling data scientists to handle complex computations promptly.

At DataRobot, we offer equal governance for both predictive and generative AI, ensuring comprehensive oversight and control across all AI models. Our governance framework provides robust tools and protocols, including full model transparency, real-time risk management, and automated compliance documentation. Whether deploying predictive models or generative AI applications, our platform ensures that all AI assets adhere to the highest standards of security, integrity, and accountability. This balanced approach allows our customers to confidently and efficiently manage all of their AI initiatives, knowing that both predictive and generative models are governed with the same level of rigor and precision.

Recognized by Gartner, Trusted by Leaders 

Our governance framework has garnered praise from industry analysts, underscoring the real-world value and reliability that our platform provides. In addition to this, DataRobot has also been ranked highest in Governance Use Case by Gartner.

As much as this recognition from Gartner means to us, the most impactful feedback is from our customer community. Their testimonials highlight how our robust governance functionalities have positively impacted their AI initiatives, ensuring safe, successful and confident deployments:

Tom Thomas, VP of Data Strategy, Analytics & Business Intelligence at FordDirect

“DataRobot is an indispensable partner helping us maintain our reputation both internally and externally by deploying, monitoring, and governing generative AI responsibly and effectively.”

Arvind Thinagarajan, VP, Data Science & Analytics at Gannett | USA Today Network

“With DataRobot, we’ve already automated multiple steps in the machine learning lifecycle for hundreds of our models. These are models mostly in the realm of predictive AI as of now. This allows us to create efficiencies and saves time for my team of data scientists with steps like data pre-processing, model building, governance of those models, and measuring the performance of those models. We believe that our partnership can extend to the generative AI realm as well.”

At DataRobot, we are committed to enabling our customers to achieve their goals with confidence and excellence. Our Governance, recognized by Gartner and lauded by our customers, in our opinion, underscores our dedication to providing a reliable, transparent, and accountable AI platform. We are proud to be the trusted choice for organizations seeking to leverage AI responsibly and effectively.

Book a product tour and explore how our AI governance and compliance capabilities help you achieve value rapidly and scale AI use cases effectively.

Gartner Critical Capabilities^TM for Data Science and Machine Learning Platforms, Machine Learning (ML) Engineering, Afraz Jaffri, Aura Popa, Peter Krensky, Jim Hare, Tong Zhang, Maryam Hassanlou, Raghvender Bhati, Published June 24, 2024.

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Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

This graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document. The Gartner document is available upon request from DataRobot.

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