Top Robot News This Month

Amanda McMaster, the company's CFO, will take over as interim CEO until Boston Dynamics' board of directors finds its next leader. The post Boston Dynamics CEO Robert Playter steps down appeared first on The Robot Report.

Meet the recipients of the 2026 IEEE Medals—the organization’s highest-level honors. Presented on behalf of the IEEE Board of Directors, these medals recognize innovators whose work has shaped modern technology across disciplines including AI, education, and semiconductors. The medals will be presented at the IEEE Honors Ceremony in April in New York City. View the full list of 2026 recipients on the IEEE Awards website, and follow IEEE Awards on LinkedIn for news and updates. IEEE MEDAL OF HONOR Sponsor: IEEE Jensen Huang Nvidia Santa Clara, Calif. “For leadership in the development of graphics processing units and their application to scientific computing and artificial intelligence.” IEEE FRANCES E. ALLEN MEDAL Sponsor: IBM Luis von Ahn Duolingo Pittsburgh “For contributions to the advancement of societal improvement and education through innovative technology.” IEEE ALEXANDER GRAHAM BELL MEDAL Sponsor: Nokia Bell Labs Scott J. Shenker University of California, Berkeley “For contributions to Internet architecture, network resource allocation, and software-defined networking.” IEEE JAGADISH CHANDRA BOSE MEDAL IN WIRELESS COMMUNICATIONS Sponsor: Mani L. Bhaumik Co-recipients: Erik Dahlman Stefan Parkvall Johan Sköld Ericsson Stockholm “For contributions to and leadership in the research, development, and standardization of cellular wireless communications.” IEEE MILDRED DRESSELHAUS MEDAL Sponsor: Google Karen Ann Panetta Tufts University Medford, Mass. “For contributions to computer vision and simulation algorithms, and for leadership in developing programs to promote STEM careers.” IEEE EDISON MEDAL Sponsor: The Edison Medal Fund Eric Swanson PIXCEL Inc. MIT “For pioneering contributions to biomedical imaging, terrestrial optical communications and networking, and inter-satellite optical links.” IEEE MEDAL FOR ENVIRONMENTAL AND SAFETY TECHNOLOGIES Sponsor: Toyota Motor Corp. Wei-Jen Lee University of Texas at Arlington “For contributions to advancing electrical...

Few robotics technologies have been actually deployed in high-mix applications. Let's explore the reasons behind this slow transition. The post 11 reasons robots struggle to scale in high-mix manufacturing appeared first on The Robot Report.

AutoPallet debuts magnetic, ceiling-mounted AMRs at Manifest 2026, providing high-density palletizing through a suspended swarm architecture. The post Flipping the script: How ‘upside-down’ AutoPallet robots solve palletizing density appeared first on The Robot Report.

Artemis, depending on whom you ask, is NASA’s bid to reclaim its heritage, to resume the business of human exploration, to take astronauts to the moon and beyond and win what’s been billed by some U.S. politicians as the “new space race” with China. Artemis II, the project’s first circumlunar test mission with a crew, is now preparing for launch, perhaps in March. If it succeeds, and if NASA can deliver on an 18 December executive order from the Trump administration, Artemis astronauts will land near the moon’s south pole by 2028 and start building a lunar outpost by 2030, steps to “ensuring American space superiority.” But there are influential voices in the American space community who warn that unless things change quickly, the race has already been lost. “We cannot control what China is doing,” said Michael Griffin in congressional testimony in December of last year. He was NASA administrator from 2005 to 2009, when the agency began assembling the hardware for what is now Artemis. “We can only control what we are doing. Of those efforts, I am forced to say that mediocrity would be an improvement.” RELATED: NASA’s Rivalry/Not-Rivalry With China’s Space Agency Takes Off “Look at the architecture that we have developed to land American astronauts on the moon,” said JIm Bridenstine in his own testimony in September. He was NASA administrator from 2018 to 2021, when Artemis was named as America’s new lunar venture. “It is extraordinarily complex.” Other NASA veterans have expressed the same worry. They say that Artemis, with its much-delayed Space Launch System rocket, Orion crew capsule, and—most critically—two competing, unproven lunar landers, is hobbled by its history of convoluted, meandering decision-making. They say it needs better organization, perhaps even a new landing ship, even at this late date. Meanwhile, the Chinese space program claims it’s on track to a lunar landing by its stated goal of 2030. In Western eyes, China does not have superior technol...

The hunt is on for anything that can surmount AI’s perennial memory wall–even quick models are bogged down by the time and energy needed to carry data between processor and memory. Resistive RAM (RRAM)could circumvent the wall by allowing computation to happen in the memory itself. Unfortunately, most types of this nonvolatile memory are too unstable and unwieldy for that purpose. Fortunately, a potential solution may be at hand. At December’s IEEE International Electron Device Meeting (IEDM), researchers from the University of California, San Diego showed they could run a learning algorithm on an entirely new type of RRAM. “We actually redesigned RRAM, completely rethinking the way it switches,” says Duygu Kuzum, an electrical engineer at the University of California, San Diego, who led the work. RRAM stores data as a level of resistance to the flow of current. The key digital operation in a neural network—multiplying arrays of numbers and then summing the results—can be done in analog simply by running current through an array of RRAM cells, connecting their outputs, and measuring the resulting current. Traditionally, RRAM stores data by creating low-resistance filaments in the higher-resistance surrounds of a dielectric material. Forming these filaments often needs voltages too high for standard CMOS, hindering its integration inside processors. Worse, forming the filaments is a noisy and random process, not ideal for storing data. (Imagine a neural network’s weights randomly drifting. Answers to the same question would change from one day to the next.) Moreover, most filament-based RRAM cells’ noisy nature means they must be isolated from their surrounding circuits, usually with a selector transistor, which makes 3D stacking difficult. Limitations like these mean that traditional RRAM isn’t great for computing. In particular, Kuzum says, it’s difficult to use filamentary RRAM for the sort of parallel matrix operations that are crucial for today’s neural netwo...

One of the key challenges in building robots for household or industrial settings is the need to master the control of high-degree-of-freedom systems such as mobile manipulators. Reinforcement learning has been a promising avenue for acquiring robot control policies, however, scaling to complex systems has proved tricky. In their work SLAC: Simulation-Pretrained Latent Action Space […]