A Temporally misaligned training strategy enables policies to learn predictive compensation for latency shifts in dual-frequency systems. It uses stale semantic intent alongside real-time proprioception to maintain high-frequency control despite asynchronous reasoning, crucial for dynamic environments.
This training strategy teaches AI systems, especially for robots, how to act correctly even when their high-level plans are a bit old due to processing delays. It combines these slightly outdated plans with current sensory information to make quick, accurate decisions, preventing errors in fast-moving situations.
Misaligned training, Latency-aware training, Predictive compensation training
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