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Researchers have developed a humanoid robot prototype, HRP-5P, intended to autonomously perform heavy labor or work in hazardous environments.

Music plays an important role in most people's lives regardless of the genre and in a wide variety of contexts from celebrations and parties to simply providing background while a task is being performed. Until very recently, music was only heard when musicians played it live, the ability to record music displaced that live performance to some degree, and then the invention of electronic musical instruments and digitisation changed our appreciation of music yet again.

The company is expected to dissolve the project later this year and relocate employees involved in development to other departments within Alphabet or help them find jobs elsewhere.

The newest version of MVTec HALCON is here to solve all of your machine vision tasks at utmost speed and robustness! Deep learning functions, like pixel-precise semantic segmentation or object detection, help you to identify and classify objects and flaws more flexibly and easier than ever before – HALCON extracts relevant image information automatically. Try it for free here!

Industry 4.0 represents a giant leap in the evolution of machines — an evolution that has already pioneered leaps in productivity and quality of life over several hundred years. Today, our machines are connected with each other, their physical environments, and to people

Autonomous mobile robots are a simple, efficient and cost-effective way to automate material handling and in-house transportation tasks in nearly any situation where employees would previously have been required to push carts around the facility.

"Please enjoy your meal," says Nepal's first robot waiter, Ginger, as she delivers a plate of steaming dumplings to a table of hungry customers.

By Esther Rolf∗, David Fridovich-Keil∗, and Max Simchowitz

In many tasks in machine learning, it is common to want to answer questions given fixed, pre-collected datasets. In some applications, however, we are not given data a priori; instead, we must collect the data we require to answer the questions of interest.

This situation arises, for example, in environmental contaminant monitoring and census-style surveys. Collecting the data ourselves allows us to focus our attention on just the most relevant sources of information. However, determining which of these sources of information will yield useful measurements can be difficult. Furthermore, when data is collected by a physical agent (e.g. robot, satellite, human, etc.) we must plan our measurements so as to reduce costs associated with the motion of the agent over time. We call this abstract problem embodied adaptive sensing.

We introduce a new approach to the embodied adaptive sensing problem, in which a robot must traverse its environment to identify locations or items of interest. Adaptive sensing encompasses many well-studied problems in robotics, including the rapid identification of accidental contamination leaks and radioactive sources, and finding individuals in search and rescue missions. In such settings, it is often critical to devise a sensing trajectory that returns a correct solution as quickly as possible.

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To celebrate the installation of the concrete façade mullions digitally fabricated using Smart Dynamic Casting in the DFAB House, we have released a new extended video showing the entire process from start to finish.

Smart Dynamic Casting (SDC) is a continuous robotic slip-forming process that enables the prefabrication of material-optimised load-bearing concrete structures using a formwork significantly smaller than the structure produced. Read more about the project on the DFAB House website.

Smart Dynamic Casting is a collaborative research project of Gramazio Kohler Research, ETH Zurich, the Institute for Building Materials, ETH Zurich and the Institute of Structural Engineering, ETH Zurich. As part of the DFAB HOUSE project at the Empa and Eawag NEST research and innovation construction site in Dübendorf Smart Dynamic Casting is used for the automated prefabrication of material-optimised load-bearing concrete mullions.

In the future the country's sewer systems will be inhabited by surveillance robots. Using robots, big data and artificial intelligence (AI), a new Danish research project will save hundreds of millions of kroner on maintaining sewers.

This brief coverage of some of the emerging uses of servo motors and drives emphasizes the improvements made possible as these components become increasingly advanced. Such progress should continue over the foreseeable future...

We identified more than 30 vendors that are currently marketing over 80 cobot models, and we expect this number to grow. More than 80 percent of collaborative robot suppliers generated less than $10m in revenues last year.

We are targeting those industries that require precise, small, high-torque motors and complementary components such as high precision planetary gearboxes, slip rings, sensors and encoders.

Reinforcing both companies’ pioneering roles in collaborative robotics, Universal Robots welcomes more than 20 Rethink staff members, incorporating valuable expertise in engineering, product development, and customer applications.

In this episode, Audrow Nash interviews Alexandros Kogkas, Katie Driggs-Campbell, and Martin Karlsson about the work they presented at the 2018 International Conference on Intelligent Robots and Systems (IROS) in Madrid, Spain.

Alexandros Kogkas is a PhD Candidate at the Imperial College London and he speaks about an eye tracking framework to understand where a person is looking.  This framework can be used to understand a person’s intentions, for example to hand a surgeon the correct tool or helping a person who is paraplegic.   Kogkas discusses how the framework works, possible applications, and his future plans for this framework.

Katie Driggs-Campbell is a Post Doctoral Researcher at Stanford’s Intelligent System Laboratory and is—soon to be—an Assistant Professor at University of Illinois Urbana-Champaign (UIUC). She speaks about making inferences about the world from human actions, specifically in the context of autonomous cars.  In the work she discusses, they use a model of a human driver that they use infer what is happening in the world, for example a human using a crosswalk. Driggs-Campbell talks about how they evaluate this work.

Martin Karlsson is a PhD student at Lund University in Sweden, and he speaks about a haptic interface to mirror robotic arms that requires no force sensing.  He discusses a feedback law that allows a mirroring of forces and his future work to deal with joint friction.

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