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Action-conditioned video data improves predictability
Long-term video generation and prediction remain challenging tasks in computer vision, particularly in partially observable scenarios where cameras are mounted on moving platforms.
Action-conditioned Deep Visual Prediction with RoAM, a new Indoor Human Motion Dataset for Autonomous Robots
With the increasing adoption of robots across industries, it is crucial to focus on developing advanced algorithms that enable robots to anticipate, comprehend, and plan their actions effectively in collaboration with humans.
Planning Robot Motion using Deep Visual Prediction
In this paper, we introduce a novel framework that can learn to make visual predictions about the motion of a robotic agent from raw video frames.
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