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Adaptation of mobile robot controllers commonly requires the computation of optimal points of operation. Specifically, for miniature mobile robots with serious computational limitations, that are typical of embedded systems, one of the main challenges is the adaptation of efficient computational methods in order to find solutions of complex optimization problems, which demand large execution times. This drawback compels the design of high-performance parallel optimization algorithms which must run over embedded system platforms. This paper describes how adequate hardware implementations of the Particle Swarm Optimization (PSO) algorithm can be useful for real time adaptation of mobile robot controllers. For achieving this, a new architecture is proposed, which is based on an FPGA implementation of the opposition-based learning (OBL) approach applied to the PSO (for short HPOPSO), and which explores the intrinsic parallelism of this algorithm in order to adjust the weights of a neural robot controller in real time according to desired behaviors. The proposed HPOPSO was applied to the learning-from-demonstration problem in which a teacher performs executions of the desired behavior. Effectiveness of the proposed architecture was demonstrated by numerical simulations and the feasibility of the adaptive behavior of the neural robot controller was confirmed for two obstacle avoidance case studies that were preserved when one or more failures on the distance sensors occur. The HPOPSO, which uses the OBL technique, improves the quality of the solutions in comparison with the standard PSO. Comparisons of the adaptation time between hardware and software approaches have demonstrated the suitability of the FPGA implementation of the proposed HPOPSO for attending specific requirements of embedded system applications.