What would you do if you walked up to a robot with a human-like head and it smiled at you first? You'd likely smile back and perhaps feel the two of you were genuinely interacting. But how does a robot know how to do this? Or a better question, how does it know to get you to smile back?
Umwelt is a new a system that enables blind and low-vision users to author accessible, interactive charts representing data in three modalities: visualization, textual description, and sonification.
A new study has shown that people modify their behavior to accommodate autonomous delivery robots, and it is this invisible "human work" that allows robots to run smoothly on the streets and needs to be considered when designing their routes.
The integration of moral robots in customer service does more than just streamline operations; it fundamentally alters the relationship between businesses and their customers.
Robots equipped with AI vision systems, such as MIRAI, can perceive and adapt to their surroundings. This allows them to handle variance while performing complex tasks, making it possible to automate operations otherwise considered too complex to solve.
Engineers aim to give robots a bit of common sense when faced with situations that push them off their trained path, so they can self-correct after missteps and carry on with their chores. The team's method connects robot motion data with the common sense knowledge of large language models, or LLMs.
Bushfires can move at astonishing speeds. The land, amount of vegetation, and the weather all have a big impact on how a fire spreads. Staying one step ahead is no easy task, but our bushfire researchers are working on it.
From wiping up spills to serving up food, robots are being taught to carry out increasingly complicated household tasks. Many such home-bot trainees are learning through imitation; they are programmed to copy the motions that a human physically guides them through.
Researchers at McMaster University and Stanford University have invented a new generative artificial intelligence model which can design billions of new antibiotic molecules that are inexpensive and easy to build in the laboratory.
Isaac Robotics Platform Now Provides Developers New Robot Training Simulator, Jetson Thor Robot Computer, Generative AI Foundation Models, and CUDA-Accelerated Perception and Manipulation Libraries
Unmanned aerial vehicles (UAVs), also known as drones, have already proved to be valuable tools for tackling a wide range of real-world problems, ranging from the monitoring of natural environments and agricultural plots to search and rescue missions and the filming of movie scenes from above. So far, most of these problems have been tackled using one drone at a time, rather than teams of multiple autonomous or semi-autonomous UAVs.
Researchers at the Indian Institute of Science (IISc) are using multiple swarms of drones to tackle natural disasters like forest fires. Forest fires are becoming increasingly catastrophic across the world, accelerated by climate change.
Over the past few years, all aspects of the warehousing sector have been automated and this naturally includes the materials handling processes. This automation has increased efficiency across the board as well as improving employee safety and productivity.
Robotic systems have become increasingly sophisticated over the past decades, evolving from rudimental stiff robots to a wide range of soft, humanoid, animal-inspired robots. Legged robots, particularly quadrupeds, have been found to be particularly promising for tackling simple tasks at ground level, such as exploring environments and carrying objects.
As computer vision researchers, we believe that every pixel can tell a story. However, there seems to be a writer’s block settling into the field when it comes to dealing with large images. Large images are no longer rare—the cameras we carry in our pockets and those orbiting our planet snap pictures so big and detailed that they stretch our current best models and hardware to their breaking points when handling them. Generally, we face a quadratic increase in memory usage as a function of image size.
Today, we make one of two sub-optimal choices when handling large images: down-sampling or cropping. These two methods incur significant losses in the amount of information and context present in an image. We take another look at these approaches and introduce $x$T, a new framework to model large images end-to-end on contemporary GPUs while effectively aggregating global context with local details.
Architecture for the $x$T framework.
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