Self-Adaptive Learning and Model Predictive Control for Tracking Unknown Dynamics with No Regret
We propose a self-adaptive online learning for control method for tracking unknown target dynamics. The target dynamics can exhibit switching behavior, particularly, a mixture of structured, random, and/or adversarial motion. Such challenging target tracking scenarios arise in applications of dynamic mapping, traffic control, and pursuit evasion, where robots need to track, pursue, or avoid collision with moving landmarks, objects, humans, etc., whose dynamics are unknown. Our method simultaneously learns multiple predictors from scratch, via self-supervised, one-shot, and computationally efficient learning, and adaptively selects the best one to match the observed target behavior. The me...
| Authors | Atharva Navsalkar, Hongyu Zhou, Vasileios Tzoumas |
|---|---|
| Primary category | cs.RO |
| Published | 29 July 2026 |
| Last updated at source | 29 July 2026 |
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