Model-Based Task Transfer Learning
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A model-based task transfer learning (MBTTL) method is presented. We consider a constrained nonlinear dynamical system and assume that a dataset of state and input pairs that solve a task T1 is available. Our objective is to find a feasible state-feedback policy for a second task, T1, by using stored data from T2. Our approach applies to tasks T2 which are composed of the same subtasks as T1, but in different order. In this paper we formally introduce the definition of subtask, the MBTTL problem and provide examples of MBTTL in the fields of autonomous cars and manipulators. Then, a computationally efficient approach to solve the MBTTL problem is presented along with proofs of feasibility for constrained linear dynamical systems. Simulation results show the effectiveness of the proposed method.
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