A DRL policy selects among base, arm, and whole-body kinematic models for NMPC on a mobile manipulator, improving success rate and reducing per-call computation time in simulation.
Multi-modal model predictive control through batch non-holonomic trajectory optimization: Application to highway driving,
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Re4MPC: Reactive Nonlinear MPC for Multi-model Motion Planning via Deep Reinforcement Learning
A DRL policy selects among base, arm, and whole-body kinematic models for NMPC on a mobile manipulator, improving success rate and reducing per-call computation time in simulation.