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Technology Engineering Severed robot hand looks like a futuristic Thing from'The Addams Family' The creepy and kooky robot can type on a keyboard, play a video game, and could help future humanoid robots. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Halloween is just around the corner, which means it's the perfect time to present another undeniably spooky robot . Or, more specifically, an -inspired detached robot hand . Like its gothic sitcom predecessor, this fully articulated appendage can balance on its own fingertips and creep around.

Our articles, videos, and infographics inform our readers about developments in technology, engineering, and science. A public charity, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. Access to Spectrum's Digital Edition is exclusive for IEEE Members Create an account to access more content and features on IEEE Spectrum, including the ability to save articles to read later, download Spectrum Collections, and participate in conversations with readers and editors. For more exclusive content and features, consider Joining IEEE . Join the world's largest professional organization devoted to engineering and applied sciences and get access to all of Spectrum's articles, archives, PDF downloads, and other benefits.

Our articles, videos, and infographics inform our readers about developments in technology, engineering, and science. A public charity, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. Access to Spectrum's Digital Edition is exclusive for IEEE Members Create an account to access more content and features on IEEE Spectrum, including the ability to save articles to read later, download Spectrum Collections, and participate in conversations with readers and editors. For more exclusive content and features, consider Joining IEEE . Join the world's largest professional organization devoted to engineering and applied sciences and get access to all of Spectrum's articles, archives, PDF downloads, and other benefits.

The next time you're scrolling your phone, take a moment to appreciate the feat: The seemingly mundane act is possible thanks to the coordination of 34 muscles, 27 joints, and over 100 tendons and ligaments in your hand. Indeed, our hands are the most nimble parts of our bodies. Mimicking their many nuanced gestures has been a longstanding challenge in robotics and virtual reality. Now, MIT engineers have designed an ultrasound wristband that precisely tracks a wearer's hand movements in real-time. The wristband produces ultrasound images of the wrist's muscles, tendons, and ligaments as the hand moves, and is paired with an artificial intelligence algorithm that continuously translates the images into the corresponding positions of the five fingers and palm.

Race to develop'embodied AI' focuses on creating dextrous hands to transform humanoid robots from gimmicks into useful products Human hands - nimble, nerve-filled appendages that are the most flexible part of the human skeleton - are exceptionally complex. Many tasks that most people can do largely without thinking, from tying a pair of shoelaces to buttoning up a shirt, in fact require a complex set of neurological instructions and precise choreography. In thousands of years of human history, no machine has been able to truly replicate human's greatest tool. But now, as artificial intelligence (AI) races forwards, some companies think they are close to surpassing this final but most difficult hurdle in robotics. Most of them are in China . A new suite of Chinese start-ups are leveraging China's advantages in manufacturing and enthusiasm for what the government calls "embodied AI" to build the fully dextrous robotic hands that are needed to transform humanoid robots from dancing gimmicks into useful products.

Since the first robot-assisted surgery was performed, over 40 years ago, major advances in robotics, computer vision and artificial intelligence have fundamentally changed medicine and healthcare. Innovative new technologies are already aiding skilled medical professionals in diagnosis, surgery, rehabilitation and beyond. But many questions remain: What ethical issues arise as medical tools become increasingly autonomous? How do we regulate technologies that can learn and change over time? And how can we ensure that cutting-edge medical devices are accessible to all?

Learning robot control policies from human videos is a promising direction for scaling up robot learning. However, how to extract action knowledge (or action representations) from videos for policy learning remains a key challenge. Existing action representations such as video frames, pixelflow, and pointcloud flow have inherent limitations such as modeling complexity or loss of information. In this paper, we propose to use object-centric 3D motion field to represent actions for robot learning from human videos, and present a novel framework for extracting this representation from videos for zero-shot control. We introduce two novel components in its implementation.

For robotic manipulation, existing robotics datasets and simulation benchmarks predominantly cater to robot-arm platforms. However, for humanoid robots equipped with dual arms and dexterous hands, simulation tasks and high-quality demonstrations are notably lacking. Bimanual dexterous manipulation is inherently more complex, as it requires coordinated arm movements and hand operations, making autonomous data collection challenging. This paper presents HumanoidGen, an automated task creation and demonstration collection framework that leverages atomic dexterous operations and LLM reasoning to generate relational constraints. Specifically, we provide spatial annotations for both assets and dexterous hands based on the atomic operations, and perform an LLM planner to generate a chain of actionable spatial constraints for arm movements based on object affordances and scenes. To further improve planning ability, we employ a variant of Monte Carlo tree search to enhance LLM reasoning for long-horizon tasks and insufficient annotation. In experiments, we create a novel benchmark with augmented scenarios to evaluate the quality of the collected data. The results show that the performance of the 2D and 3D diffusion policies can scale with the generated dataset.

Handheld grippers are increasingly used to collect human demonstrations due to their ease of deployment and versatility. However, most existing designs lack tactile lation.