Scientists created a new way to help computers plan and take actions in complex situations, without needing to train them on specific tasks beforehand. This approach, called the Tree-guided Diffusion Planner, allows computers to explore different options and make better decisions, which could lead to more efficient or effective solutions in fields like robotics or gaming.
Read Full Paper →The researchers developed a new way to help robots figure out what they need to do to move objects around, by understanding cause-and-effect relationships in their actions. This approach could lead to more accurate predictions and successful task completion for robots, which would be useful in real-world applications like helping with household chores.
Read Paper →Scientists compared different ways of controlling a group of robots working together to cover an area. They found that some approaches are better than others, depending on how many robots there are and whether any of them might break down.
Read Paper →Scientists created a computer simulation that models how pedestrians interact with each other on a sidewalk, including situations where they accidentally collide while trying to avoid each other. By studying this phenomenon in a simulated environment, researchers can gain insights into how pedestrians communicate implicitly with each other, which could help develop safe and acceptable behavior for robots navigating among people.
Read Paper →Scientists developed a new method to combine information from multiple cameras, creating a more accurate understanding of the world that's less affected by faulty cameras. This approach allows for faster and more reliable learning, making it easier to use robots in real-world environments.
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