Network Latency Could Corrupt Robot Training Data, Researchers Argue

A recent paper introduces K-AoI, a metric that measures how closely a digital twin stays synchronized with the physical scene during remote robot operation. It combines data age with spatial alignment rather than relying on delay alone. The authors argue that remote teleoperation sessions produce recorded demonstrations used to train future autonomous robots, so frames with stale views may be unreliable. The piece also notes that trained human annotators must review such footage frame by frame before it becomes usable training data.