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Competitions in Robotics

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  • 28 Sep 2022 9:40 AM | Anonymous

    Meet the latest group of tech start-ups that are in this year’s Smart Port Challenge.

    Meet PIER71™'s latest cohort of tech start-ups

  • 31 Aug 2022 11:53 AM | Anonymous

    Autonomous underwater robotics is an exciting challenge in engineering, which participants get to experience at SAUVC. The competition is great learning ground for participants to experience the challenges of AUV system engineering and develop skills in the related fields of mechanical, electrical and software engineering.

    More details could be found at SAUVC 2022

  • 19 Jul 2022 3:41 PM | Anonymous

    We are happy to announce the Real-Robot Challenge 2022:

    It is time to test offline reinforcement learning methods in the real world!

    Take part in our NeurIPS competition.

    The task is to use pre-recorded data to learn how to manipulate a cube with the TriFinger robot. You will then be able to run the policy on a real robot at the Max Planck Institute for Intelligent Systems in a remote fashion  (as easy as submitting a compute job).

    Please encourage your colleagues to take part and spread the word.

  • 29 Jun 2022 6:18 PM | Anonymous
    Annual Review Paper Award

    This award is to recognize the influential review paper which has to go through the normal review process and has to be accepted for publication in IJHR. The review paper must perfectly focus on the key issues related to humanoid robotics research.

    IJHR will annually award one winner with the cash prize of USD 2000.

    The selection procedure is as follows:

    1. Any member in the advisory board is welcome to contribute review paper.
    2. Any associate editor is welcome to contribute review paper.
    3. Any prominent scientist with strong expertise in humanoid robotics research is welcome to contribute review paper.
    4. Any received submission will go through the normal review process.
    5. In addition, any such accepted submission must be endorsed by all the three Editors-in-Chief.
    6. The fully endorsed and accepted submission will receive such award on annual basis.
    Most Cited Paper Awards

    This award is to recognize those published papers which have stimulated humanoid robotics research due to their visibilities or inspirations that have triggered much more follow-up research works.

    IJHR will annually award three winners with the cash prize of USD 200 each.

    The selection procedure is as follows:

    1. Among all the papers published within the last three years, identity the not-awarded paper with the highest number of citations to receive one award.
    2. Among all the papers published within the last six years, identity the not-awarded paper with the highest number of citations to receive one award.
    3. Among all the papers published within the last nine years, identity the not-awarded paper with the highest number of citations.
    4. Award these three papers with the prizes.
    Outstanding Reviewer Awards

    This award is to recognize those reviewers who have taken the heavy review works and have helped the authors to improve their manuscripts which have finally been accepted for publication in IJHR.

    IJHR will annually award three winners with the cash prize of USD 200 each.

    The selection procedure is as follows:

    1. Identity the published papers in the last year.
    2. Identify the reviewers who have reviewed these published papers.
    3. Identify three not-awarded reviewers with the outstanding efforts.
    4. Award these three reviewers with the prizes.
    Excellent Editorial Service Awards

    This award is to recognize those associate editors who have demonstrated outstanding services and efficiencies devoted to the handling of the reviews of assigned manuscripts which have been submitted to IJHR.

    IJHR will annually award three winners with the cash prize of USD 200 each.

    The selection procedure is as follows:

    1. Count the number of papers handled by each associate editor who is still on duty.
    2. Identify the top three.
    3. Award these three associate editors with the prizes.

    More details could be found at:

    IJHR Awards for Authors and Reviewers (

  • 11 May 2022 7:10 AM | Anonymous

    NTU’s team SINGABOAT has participated in the VRX (Virtual RobotX) competition 2022 organized by RoboNation ( NTU’s team consists of three student members (Tanmay Samak, Lee Chern Peng, Samak Chinmay Vilas) who are under the supervision of Associate Professor Xie Ming. All of them are from MAE. The VRX competition is an international challenge with 19 international teams in this year. The VRX Competition 2022 uses the VRX simulation environment and takes place in three phases. The final score achieved by NTU’s team makes it to be the winner of the third prize with the cash of US$1000. In addition, NTU's team has also received the awards such as Team with Remarkable Video Submission, Team with Biggest Leap in Ranking from Phase 2 to Phase 3, Team being Phenomenal Open-Source Contributor, and Team with Fee Waiver for RoboBoat Competition 2023. For comparison, NUS’s team for this year’s VRX competition 2022 is ranked at 7th place, and NTU's team is just short of one point behind the second prize winner.

    The link below is a video prepared by the NTU's team:

  • 15 Mar 2022 9:08 AM | Anonymous

    Students are invited to participate in Seagate's Lyve Cloud Hackathon 2022to conceive solutions to relieve data storage issues, and to revolutionise the future of data storage. Seagate, global leader in mass storage, is looking for innovations to meet its challenge statements regarding ease of access to data and efficient data management.

    Lyve Cloud tackles the issues that plague traditional data storage, such as prohibitive costs and vendor lock-in. It is an enterprise cloud storage that offers cost predictability, world-class security security and always-on-on availability to maximise what businesses can do with data. Seagate wants ideas on how Lyve Cloud can help businesses scale without limits. 

    Participants are invited to develop the following solutions :

    • Connect other public cloud big data software stacks with Lyve Cloud
    • Create a solution to connect Lyve Cloud’s audit logs to other public clouds
    • Build a data migration and movement solution from other Cloud vendors to Lyve Cloud
    • Build a middleware solution on top of Lyve Cloud that serves as media streaming server

    They will have the chance to pitch their innovative concept to decision makers, and be rewarded for their ideas. 

    The top 3 innovators will be rewarded with cash prizes ranging from SGD$1000 to SGD$8000. Innovators finishing #4 to #8 will also be rewarded with 4TB hard drives.

    Interested students can submit their solutions before May 2nd:

  • 6 Nov 2021 6:42 PM | Anonymous

    Group from Technical University Munich wins the Indy Autonomous Challenge, taking home the $1 million Grand Prize. Mike Oitzman, Editor of the Mobile Robot Guide, reviews the first autonomous motorcar event held at the world-famous Indianapolis Motor Speedway.

    TUM Autonomous Motorsport from the Technical University of Munich (TUM) won the Indy Autonomous Challenge (IAC), the first autonomous racecar competition at the Indianapolis Motor Speedway (IMS). TUM competed in a field of 9 teams from 21 universities to win the $1 million grand prize.

    Fastest Lap
    The Rules of the IAC competition required each team to compete in a fastest lap competition that included an obstacle avoidance component. TUM recorded the fastest 2-lap average speed of 135.944 on the famed IMS Oval.

    “Participating in the Indy Autonomous Challenge allowed our team to advance autonomous driving technologies and being able to take first place after two years of hard work acknowledges that we had an outstanding team,” said Alex Wischnewski, team leader of TUM Autonomous Motorsport. “Our next goal is to win a high-speed autonomous head-to-head race.”

    I was fortunate to attend the event live at IMS, which lived up to its reputation as an incredible venue for an event like this. The event started with a Boston Dynamics Spot robot waving the checkered flag to start the competition.

    The TUM Autonomous Motorsport team is made up of race fans who know the traditions of motor sports, including the tradition at IMS of “kissing the bricks” at the finish line.

    Advancing Autonomous Driving
    Autonomous racing presents different challenges from autonomous driving on public streets. History was definitely made at the 2021 IAC event. Key to the challenge was the fact that all of the vehicles were identical in hardware design and build.

    All of the cars shared the exact same chassis, engine and body design. The vehicles were all designed by Dallara and were similar to the Indy Lights vehicles. The race platform for this race was designated the Dallara AV-21. There was no advantage to any team in the physical vehicle design.

    Comparable Technologies

    Likewise, the racecars all shared the same autonomous driving technology, including LiDAR, RADAR, vision cameras, IMS and GPS sensor package. At the heart of the vehicles were top of the line Cisco routers (for routing all of the signals on-board) and an ADLINK Technologies AVA edge AI autonomous racing computer made just for IAC. (Stay tuned for a special race car tour with ADLINK’s CTO Joe Speed)

    The cars all shared a similar software stack built on top of the ROS (Robot Operating System) with Eclipse Cyclone DDS. The most difficult problem to solve for IAC 2021 was to design the racing algorithms that allowed the sensor fusion and input processing to be done in real-time while the car was moving at high speeds. For example, at 135 MPH, the car is moving 66 yards per second or 2,376 inches per second. A lot has to happen in software to grab new data from all of the sensors and compute a new set point to steer the vehicle to the next trajectory setpoint on the racetrack.

    While the cars didn’t need to worry about pedestrians and stop lights, they didn’t have much room for error with the race track’s barrier ever present. Several team’s vehicles crashed into the wall when the race cars became confused from the sensor inputs.

    Time Trials and Obstacles
    The race committee originally hoped that the teams could achieve a multi-car, head-to-head race. However, there just wasn’t enough testing time during the 2021 event to reach this goal. As a result, the race committee changed the rules for the final event on Saturday to be a time trial and obstacle avoidance contest.

    Each team had 2 laps to warm up the engine and tires, and then they were timed for two consecutive laps. The top three teams then went on to the finals, where it was 4 warm up laps and two timed consecutive laps.

    Three Qualifiers

    In the end, three teams qualified for the finals. EuroRacing qualified first with an average speed of 131.148 mph; TUM qualified second with an average speed of 129.237 mph; PoliMOVE qualified third with an average speed of 124.450. Kaist did not make the final cutoff, but still ran a speed of 84.355 mph. None of the other five teams were able to make it through their qualifying rounds. Three teams crashed their vehicles, and two teams didn’t make it out of the pits.

    “The IAC would not be possible without the generous support of the Lilly Endowment and the Indiana Economic Development Corporation, which have been committed partners since the beginning,” said Paul Mitchell. “The prize money won by TUM Autonomous Motorsport will go to the Technische Universität München (Technical University of Munich, Germany), to support the university’s efforts to further autonomous technology research and development. We know that the achievements of our IAC teams, alongside some of the best companies in the world, will certainly lead to the acceleration of Indiana’s AI and automation industries well into the future.”

    In addition to thousands of attendees at the IMS, and more than 36,000 viewers on the AWS livestream, the IAC hosted 350 high school STEM students, the next generation of innovators, representing more than 50 urban, rural and suburban school districts across Indiana.

  • 29 Oct 2021 7:03 PM | Anonymous

    The winner was not a driver but an algorithm on Saturday at the Indianapolis Motor Speedway, where the top car clocked an average speed of 218 km/h (135 mph), ushering autonomous vehicles into a new era.

    Setting the record pace over two laps, a team from the Technical University of Munich (TUM) won a $1 million prize in the first Indy Autonomous Challenge, an event dedicated to self-driving cars.

    Their car beat EuroRacing, another European team who fell to a coding mistake by one of their student engineers despite securing the fastest lap time ever recorded for an autonomous car, at 139 miles per hour (223 km/h).

    EuroRacing's Dallara IL-15 had been programmed to run five laps instead of the six scheduled for every competitor and therefore slowed down during its final drive around the oval, bringing down the average speed.

    "I have a bitter taste in my mouth," said Marko Bertogna, professor at the University of Modena and Reggio Emilia in Italy and EuroRacing team head.

    A third European team also had a shot at victory but GPS trackers for PoliMOVE shut down during the race, which made their car "totally blind", according to Sergio Matteo Savaresi, professor at the Polytechnic University of Milan and team manager.

    Each autonomous car relies on sensors, cameras, radar, but above all GPS, without which no controlled motion is possible, to the point that some have two onboard.

    - 'Part of history' -

    The Dallara IL-15, used by every team, resembles a Formula One car but is smaller and comes with a price tag of $230,000. However, the technology on board makes each car worth more than $1 million, according to event organizers.

    Among the tech installed in the vehicles are sensors supplied by industry trailblazer Luminar that can map out surfaces from 250 meters away.

    The TUM team's average speed of 218 km/h "is not far away from what human drivers do" with the same car, said Alexander Wischnewski, a member of the winning team.

    Considering the cool, wet weather in Indianapolis on Saturday, with no proper warm-up time for tires, "I'm really proud of what we showed today," Wischnewski said.

    "Nobody knew that these (self-driving cars) could go so fast in competition," added Stefano dePonti, Dallara USA's CEO, who said he had witnessed "a part of history."

    Bertogna said he believed the autonomous Dallara could reach 280 km/h, "but with these conditions, it was impossible."

    For two years, the nine competing college student teams had been preparing for an event in which all the cars would race at the same time side by side.

    But organizers had a last-minute change of heart and decided to go for a time trial competition instead, with the cars taking turns on the track.

    However, a side session took place a few days earlier on another Indianapolis track, Lucas Oil Raceway, during which TUM, PoliMOVE and EuroRacing all had their Dallaras running simultaneously, and even overtaking each other.

    Talk is now rife about a proper multi-car autonomous race involving the same cars at Las Vegas tech show CES scheduled for early January, but the rematch has yet to be confirmed.

    The commercial autonomous vehicle industry has been following the Indianapolis race closely, with contributions to the event topping $120 million. Many of the teams plan to publish some or all of the algorithms used to run the cars for use in the wider sector.

    Saturday's event was also enjoyed by a handful of fans, whom the organizers capped at a low number.

    Patti Aarons, 59, said she had been visiting the Motor Speedway for more than 50 years but was ecstatic about the self-driving race.

    "It just gets my blood pumping. I love it," Aarons said.

  • 2 Sep 2021 8:47 AM | Anonymous

    We have an exciting event with Ahold Delhaize and PAL Robotics that we thought would be great for your community of robotics experts, especially seeing that you have participated in previous events with PAL Robotics! 

    In the AIRLab Stacking challenge, teams will work on algorithms that focus on smart retail applications, for example, automated product stacking. Algorithms will be coded using a simulation that can later be deployed using the TIAGo robot developed by PAL Robotics. 

    The community could stand to win up to €5000


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  • 14 Jul 2021 6:12 PM | Anonymous

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