论文标题
在机器人导航期间，如何减少在保留性能的同时减少计算时间？一种基于模型和无模型学习的神经启发的架构，用于自主转移
How to reduce computation time while sparing performance during robot navigation? A neuro-inspired architecture for autonomous shifting between model-based and model-free learning
论文作者
论文摘要
Taking inspiration from how the brain coordinates multiple learning systems is an appealing strategy to endow robots with more flexibility.预期的优势之一是，当机器人的性能令人满意时，机器人可以自主切换到成本最低的系统。但是，据我们所知，尚无对真实机器人的研究表明，在这种受脑启发的算法维持性能的同时，测得的计算成本降低了。我们提出了导航实验，涉及目标，死胡同和非平稳性的不同长度的路径（即，目标位置和障碍物的幻影发生变化）。我们在学习系统之间提出了一种新颖的仲裁机制，可以明确衡量绩效和成本。我们发现，机器人可以通过在学习系统之间切换以保持高性能来适应环境的变化。此外，当任务稳定时，机器人也自主转移到成本最低的系统，这会导致计算成本的急剧降低，同时保持高性能。总体而言，这些结果说明了使用多个学习系统的兴趣。
Taking inspiration from how the brain coordinates multiple learning systems is an appealing strategy to endow robots with more flexibility. One of the expected advantages would be for robots to autonomously switch to the least costly system when its performance is satisfying. However, to our knowledge no study on a real robot has yet shown that the measured computational cost is reduced while performance is maintained with such brain-inspired algorithms. We present navigation experiments involving paths of different lengths to the goal, dead-end, and non-stationarity (i.e., change in goal location and apparition of obstacles). We present a novel arbitration mechanism between learning systems that explicitly measures performance and cost. We find that the robot can adapt to environment changes by switching between learning systems so as to maintain a high performance. Moreover, when the task is stable, the robot also autonomously shifts to the least costly system, which leads to a drastic reduction in computation cost while keeping a high performance. Overall, these results illustrates the interest of using multiple learning systems.
