Video Friday is your weekly selection of awesome robotics videos, collected by your friends at IEEE Spectrum robotics. We also post a weekly calendar of upcoming robotics events for the next few months. Please send us your events for inclusion. European Robotics Forum: 25–27 March 2025, STUTTGART, GERMANY RoboSoft 2025: 23–26 April 2025, LAUSANNE, SWITZERLAND ICUAS 2025: 14–17 May 2025, CHARLOTTE, NC ICRA 2025: 19–23 May 2025, ATLANTA, GA London Humanoids Summit: 29–30 May 2025, LONDON IEEE RCAR 2025: 1–6 June 2025, TOYAMA, JAPAN 2025 Energy Drone & Robotics Summit: 16–18 June 2025, HOUSTON, TX RSS 2025: 21–25 June 2025, LOS ANGELES ETH Robotics Summer School: 21–27 June 2025, GENEVA IAS 2025: 30 June–4 July 2025, GENOA, ITALY ICRES 2025: 3–4 July 2025, PORTO, PORTUGAL IEEE World Haptics: 8–11 July 2025, SUWON, KOREA IFAC Symposium on Robotics: 15–18 July 2025, PARIS RoboCup 2025: 15–21 July 2025, BAHIA, BRAZIL Enjoy today’s videos! Every time you see a humanoid demo in a warehouse or factory, ask yourself: Would a “superhumanoid” like this actually be a better answer? [ Dexterity ] The only reason that this is the second video in Video Friday this week, and not the first, is because you’ve almost certainly already seen it. This is a collaboration between the Robotics and AI Institute and Boston Dynamics, and RAI has its own video, which is slightly different: - YouTube [ Boston Dynamics ] via [ RAI ] Well this just looks a little bit like magic. [ University of Pennsylvania Sung Robotics Lab ] After hours of dance battles with professional choreographers (yes, real human dancers!), PM01 now nails every iconic move from Kung Fu Hustle. [ EngineAI ] Sanctuary AI has demonstrated industry-leading sim-to-real transfer of learned dexterous manipulation policies for our unique, high degree-of-freedom, high strength, and high speed hydraulic hands. [ Sanctuary AI ] This video is “introducing BotQ, Figure’s new high-volume manufacturing facility for humanoid robots,” but I just see some injection molding and finishing of a few plastic parts. [ Figure ] DEEP Robotics recently showcased its “One-Touch Navigation” feature, enhancing the intelligent control experience of its robotic dog. This feature offers two modes: map-based point selection and navigation and video-based point navigation, designed for open terrains and confined spaces respectively. By simply typing on a tablet screen or selecting a point in the video feed, the robotic dog can autonomously navigate to the target point, automatically planning its path and intelligently avoiding obstacles, significantly improving traversal efficiency. What’s in the bags, though? [ Deep Robotics ] This hurts my knees to watch, in a few different ways. [ Unitree ] Why the recent obsession with two legs when instead robots could have six? So much cuter! [ Jizai ] via [ RobotStart ] The world must know: who killed Mini-Duck? [ Pollen ] Seven hours of Digit robots at work at ProMat. And there are two more days of these livestreams if you need more! [ Agility ]

In partnership with Google, the Computer History Museum has released the source code to AlexNet, the neural network that in 2012 kickstarted today’s prevailing approach to AI. The source code is available as open source on CHM’s GitHub page. What Is AlexNet? AlexNet is an artificial neural network created to recognize the contents of photographic images. It was developed in 2012 by then University of Toronto graduate students Alex Krizhevsky and Ilya Sutskever and their faculty advisor, Geoffrey Hinton. The Origins of Deep Learning Hinton is regarded as one of the fathers of deep learning, the type of artificial intelligence that uses neural networks and is the foundation of today’s mainstream AI. Simple three-layer neural networks with only one layer of adaptive weights were first built in the late 1950s—most notably by Cornell researcher Frank Rosenblatt—but they were found to have limitations. [This explainer gives more details on how neural networks work.] In particular, researchers needed networks with more than one layer of adaptive weights, but there wasn’t a good way to train them. By the early 1970s, neural networks had been largely rejected by AI researchers. Frank Rosenblatt [left, shown with Charles W. Wightman] developed the first artificial neural network, the perceptron, in 1957.Division of Rare and Manuscript Collections/Cornell University Library In the 1980s, neural network research was revived outside the AI community by cognitive scientists at the University of California San Diego, under the new name of “connectionism.” After finishing his Ph.D. at the University of Edinburgh in 1978, Hinton had become a postdoctoral fellow at UCSD, where he collaborated with David Rumelhart and Ronald Williams. The three rediscovered the backpropagation algorithm for training neural networks, and in 1986 they published two papers showing that it enabled neural networks to learn multiple layers of features for language and vision tasks. Backpropagation, which is foundational to deep learning today, uses the difference between the current output and the desired output of the network to adjust the weights in each layer, from the output layer backward to the input layer. University of Toronto. Away from the centers of traditional AI, Hinton’s work and those of his graduate students made Toronto a center of deep learning research over the coming decades. One postdoctoral student of Hinton’s was Yann LeCun, now chief scientist at Meta. While working in Toronto, LeCun showed that when backpropagation was used in “convolutional” neural networks, they became very good at recognizing handwritten numbers. ImageNet and GPUs Despite these advances, neural networks could not consistently outperform other types of machine learning algorithms. They needed two developments from outside of AI to pave the way. The first was the emergence of vastly larger amounts of data for training, made available through the Web. The second was enough computational power to perform this training, in the form of 3D graphics chips, known as GPUs. By 2012, the time was ripe for AlexNet. Fei-Fei Li’s ImageNet image dataset, completed in 2009, was pivotal in training AlexNet. Here, Li [right] talks with Tom Kalil at the Computer History Museum.Douglas Fairbairn/Computer History Museum The data needed to train AlexNet was found in ImageNet, a project started and led by Stanford professor Fei-Fei Li. Beginning in 2006, and against conventional wisdom, Li envisioned a dataset of images covering every noun in the English language. She and her graduate students began collecting images found on the Internet and classifying them using a taxonomy provided by WordNet, a database of words and their relationships to each other. Given the enormity of their task, Li and her collaborators ultimately crowdsourced the task of labeling images to gig workers, using Amazon’s Mechanical Turk platform. competition in 2010 to encourage research teams to improve their image recognition algorithms. But over the next two years, the best systems only made marginal improvements. NVIDIA, cofounded by CEO Jensen Huang, had led the way in the 2000s in making GPUs more generalizable and programmable for applications beyond 3D graphics, especially with the CUDA programming system released in 2007. Both ImageNet and CUDA were, like neural networks themselves, fairly niche developments that were waiting for the right circumstances to shine. In 2012, AlexNet brought together these elements—deep neural networks, big datasets, and GPUs— for the first time, with pathbreaking results. Each of these needed the other. How AlexNet Was Created By the late 2000s, Hinton’s grad students at the University of Toronto were beginning to use GPUs to train neural networks for both image and speech recognition. Their first successes came in speech recognition, but success in image recognition would point to deep learning as a possible general-purpose solution to AI. One student, Ilya Sutskever, believed that the performance of neural networks would scale with the amount of data available, and the arrival of ImageNet provided the opportunity. In 2011, Sutskever convinced fellow grad student Alex Krizhevsky, who had a keen ability to wring maximum performance out of GPUs, to train a convolutional neural network for ImageNet, with Hinton serving as principal investigator. AlexNet used NVIDIA GPUs running CUDA code trained on the ImageNet dataset. NVIDIA CEO Jensen Huang was named a 2024 CHM Fellow for his contributions to computer graphics chips and AI.Douglas Fairbairn/Computer History Museum Krizhevsky had already written CUDA code for a convolutional neural network using NVIDIA GPUs, called cuda-convnet, trained on the much smaller CIFAR-10 image dataset. He extended cuda-convnet with support for multiple GPUs and other features and retrained it on ImageNet. The training was done on a computer with two NVIDIA cards in Krizhevsky’s bedroom at his parents’ house. Over the course of the next year, he constantly tweaked the network’s parameters and retrained it until it achieved performance superior to its competitors. The network would ultimately be named AlexNet, after Krizhevsky. Geoff Hinton summed up the AlexNet project this way: “Ilya thought we should do it, Alex made it work, and I got the Nobel prize.” Krizhevsky, Sutskever, and Hinton wrote a paper on AlexNet that was published in the fall of 2012 and presented by Krizhevsky at a computer vision conference in Florence, Italy, in October. Veteran computer vision researchers weren’t convinced, but LeCun, who was at the meeting, pronounced it a turning point for AI. He was right. Before AlexNet, almost none of the leading computer vision papers used neural nets. After it, almost all of them would. synthesize believable human voices, beat champion Go players, and generate artwork, culminating with the release of ChatGPT in November 2022 by OpenAI, a company cofounded by Sutskever. Releasing the AlexNet Source Code In 2020, I reached out to Krizhevsky to ask about the possibility of allowing CHM to release the AlexNet source code, due to its historical significance. He connected me to Hinton, who was working at Google at the time. Google owned AlexNet, having acquired DNNresearch, the company owned by Hinton, Sutskever, and Krizhevsky. Hinton got the ball rolling by connecting CHM to the right team at Google. CHM worked with the Google team for five years to negotiate the release. The team also helped us identify the specific version of the AlexNet source code to release—there have been many versions of AlexNet over the years. There are other repositories of code called AlexNet on GitHub, but many of these are re-creations based on the famous paper, not the original code. CHM’s GitHub page. This post originally appeared on the blog of the Computer History Museum. Acknowledgments Special thanks to Geoffrey Hinton for providing his quote and reviewing the text, to Cade Metz and Alex Krizhevsky for additional clarifications, and to David Bieber and the rest of the team at Google for their work in securing the source code release. References Fei-Fei Li, The Worlds I See: Curiosity, Exploration, and Discovery at the Dawn of AI. First edition, Flatiron Books, New York, 2023. Cade Metz, Genius Makers: The Mavericks Who Brought AI to Google, Facebook, and the World. First edition, Penguin Random House, New York, 2022.

Technology should benefit humanity. One of the most remarkable examples of technology’s potential to provide enduring benefits is the Itaipu Hydroelectric Dam, a massive binational energy project between Brazil and Paraguay. Built on the Paraná River, which forms part of the border between the two nations, Itaipu transformed a once-contested hydroelectric resource into a shared engine of economic progress. The power plant has held many records. For decades, it was the world’s largest hydroelectric facility; the dam spans the river’s 7.9-kilometer width and reaches a height of 196 meters. Itaipu was also the first hydropower plant to generate more than 100 terawatt hours of electricity in a year. To acknowledge Itaipu’s monumental engineering achievement, on 4 March the dam was recognized as an IEEE Milestone during a ceremony in Hernandarias, Paraguay. The ceremony commemorated the project’s impact on engineering and energy production. Itaipu’s massive scale By the late 1960s, Brazil and Paraguay recognized the Paraná River’s untapped hydroelectric potential, according to the Global Infrastructure Hub. Brazil, which was undergoing rapid industrialization, sought a stable, renewable energy source to reduce its dependence on fossil fuels. Meanwhile, Paraguay, lacking the financial resources to construct a gigawatt-scale hydroelectric facility independently, entered into a treaty with Brazil in 1973. The agreement granted both countries equal ownership of the dam and its power generation. Construction began in 1975 and was completed in 1984, costing US $19.6 billion. The scale of the project was staggering. Engineers excavated 50 million cubic meters of earth and rock, poured 12.3 million cubic meters of concrete, and used enough iron and steel to construct 380 Eiffel Towers. Itaipu was designed for continuous expansion. It initially launched with two 700-megawatt turbine units, providing 1.4 gigawatts of capacity. By 1991, the power plant reached its planned 12.6 GW capacity. In 2006 and 2007, it was expanded to 14 GW with the addition of two more units, for a total of 20. Although China’s 22.5-GW Three Gorges Dam, on the Yangtze River near the city of Yichang, surpassed Itaipu’s capacity in 2012, the South American dam remains one of the world’s most productive hydroelectric facilities. On average, Itaipu generates around 90 terawatt-hours of electricity annually. It set a record by generating 103.1 TWh in 2016 (surpassed in 2020 by Three Gorges’ 111.8-TWh output). To put 100 TWh into perspective, a power plant would need to burn approximately 50 million tonnes of coal to produce the same amount of energy, according to the U.S. Energy Information Administration. By harnessing 62,200 cubic meters of river water per second, Itaipu prevents the release of nearly 100 million tonnes of carbon dioxide each year. During its 40-year lifetime, the dam has generated more than 3,000 TWh of electricity, meeting nearly 90 percent of Paraguay’s energy needs and contributing roughly 10 percent of Brazil’s electricity supply. Itaipu’s legacy endures as a testament to the benefits of international cooperation and sustainable energy and to the power of engineering to shape the future. IEEE recognition for Itaipu The IEEE Milestone commemorative plaque, now displayed in the dam’s visitor center, highlights Itaipu’s role as a world leader in hydroelectric power generation. It reads: “Itaipu power plant construction began in 1975 as a joint Brazil-Paraguay venture. When power generation started in 1984, Itaipu set a world record for the single largest installed hydroelectric capacity (14 GW). For at least three decades, Itaipu produced more electricity annually than any other hydroelectric project. Linking power plants, substations, and transmission lines in both Brazil and Paraguay, Itaipu’s system provided reliable, affordable energy to consumers and industry.” Administered by the IEEE History Center and supported by donors, the Milestone program recognizes outstanding technical developments worldwide. The IEEE Paraguay Section sponsored the nomination.

Video Friday is your weekly selection of awesome robotics videos, collected by your friends at IEEE Spectrum robotics. We also post a weekly calendar of upcoming robotics events for the next few months. Please send us your events for inclusion. European Robotics Forum: 25–27 March 2025, STUTTGART, GERMANY RoboSoft 2025: 23–26 April 2025, LAUSANNE, SWITZERLAND ICUAS 2025: 14–17 May 2025, CHARLOTTE, NC ICRA 2025: 19–23 May 2025, ATLANTA, GA London Humanoids Summit: 29–30 May 2025, LONDON IEEE RCAR 2025: 1–6 June 2025, TOYAMA, JAPAN 2025 Energy Drone & Robotics Summit: 16–18 June 2025, HOUSTON, TX RSS 2025: 21–25 June 2025, LOS ANGELES ETH Robotics Summer School: 21–27 June 2025, GENEVA IAS 2025: 30 June–4 July 2025, GENOA, ITALY ICRES 2025: 3–4 July 2025, PORTO, PORTUGAL IEEE World Haptics: 8–11 July 2025, SUWON, KOREA IFAC Symposium on Robotics: 15–18 July 2025, PARIS RoboCup 2025: 15–21 July 2025, BAHIA, BRAZIL Enjoy today’s videos! In 2026, a JAXA spacecraft is heading to the Martian moon Phobos to chuck a little rover at it. [ DLR ] Happy International Women’s Day! UBTECH humanoid robots Walker S1 deliver flowers to incredible women and wish all women a day filled with love, joy and empowerment. [ UBTECH ] TRON 1 demonstrates Multi-Terrain Mobility as a versatile biped mobility platform, empowering innovators to push the boundaries of robotic locomotion, unlocking limitless possibilities in algorithm validation and advanced application development. [ LimX Dynamics ] This is indeed a very fluid running gait, and the flip is also impressive, but I’m wondering what sort of actual value these skills add, you know? Or even what kind of potential value they’re leading up to. [ EngineAI ] Designing trajectories for manipulation through contact is challenging as it requires reasoning of object & robot trajectories as well as complex contact sequences simultaneously. In this paper, we present a novel framework for simultaneously designing trajectories of robots, objects, and contacts efficiently for contact-rich manipulation. [ Paper ] via [ Mitsubishi Electric Research Laboratories ] Thanks, Yuki! Running robot, you say? I’m thinking it might actually be a power walking robot. [ MagicLab ] Wake up, Reachy! [ Pollen ] Robot vacuum docks have gotten large enough that we’re now all supposed to pretend that we’re happy they’ve become pieces of furniture. [ Roborock ] The SeaPerch underwater robot, a “do-it-yourself” maker project, is a popular educational tool for middle and high school students. Developed by MIT Sea Grant, the remotely operated vehicle (ROV) teaches hand fabrication processes, electronics techniques, and STEM concepts, while encouraging exploration of structures, electronics, and underwater dynamics. [ MIT Sea Grant ] I was at this RoboGames match! In 2010! And now I feel old! [ Hardcore Robotics ] Daniel Simu with a detailed breakdown of his circus acrobat partner robot. If you don’t want to watch the whole thing, make sure and check out 3:30. [ Daniel Simu ]