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LECO: Learning Direct Control from Optimization for Dynamic Target Tracking

LECO converts online trajectory optimization for dynamic target tracking into offline learning of a direct-control policy, using a differentiable kinematic model to let trajectory-level objectives directly guide control learning. Across three simulated obstacle densities, its root-mean-square linear jerk is 14.1%–31.2% lower than the best comparison method on this metric, it achieves the highest tracking success rate, and its per-cycle computation time is 4.25–5.63 ms.

Part of the event: LECO: Offline-Learned Direct Control Policy for Dynamic Target Tracking

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