AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Most current grasp synthesis systems are trained offline and remain fixed during deployment. While this works well when deployment conditions resemble the training data, performance can degrade when robots encounter conditions they have not seen before, such as unfamiliar objects. In this work, we present a continual-l...
A reinforcement-learning-based perceptive control system that operates directly on observations from a head-mounted solid-state lidar, consuming the raw lidar scan through an attention-based encoder with recurrent memory to extract task-relevant geometry from the sparse returns.
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