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Achieving a dramatic reduction in joint-angle prediction error, the H-PAC hand demonstrates that underactuated systems can achieve high precision without complex retuning for different tasks.
Controller performance in edge clusters can swing dramatically based on workload intensity, with a deep reinforcement-learning approach losing to a simple heuristic by 29 percentage points under heavy load.
AIF-Router achieves stable online learning for adaptive AI service orchestration, even in unpredictable edge environments, showcasing the potential of Active Inference in real-world applications.
