A robot can invoke heterogeneous computation resources such as CPUs, cloud GPU servers, or even human computation for achieving a high-level goal. The problem of invoking an appropriate computation model so that it will successfully complete a task while keeping its compute and energy costs within a budget is called a model selection problem. In this paper, we present an optimal solution to the model selection problem with two compute models, the first being fast but less accurate, and the second being slow but more accurate. The main insight behind our solution is that a robot should invoke the slower compute model only when the benefits from the gain in accuracy outweigh the computational costs. We show that such cost-benefit analysis can be performed by leveraging the statistical correlation between the accuracy of fast and slow compute models. We demonstrate the broad applicability of our approach to diverse problems such as perception using neural networks and safe navigation of a simulated Mars rover.

中文翻译：

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机器人任务准确性和计算效率之间的可解释权衡

机器人可以调用异构计算资源，例如 CPU、云 GPU 服务器，甚至人类计算来实现高级目标。调用适当的计算模型使其成功完成任务同时将其计算和能源成本保持在预算内的问题称为模型选择问题。在本文中，我们使用两个计算模型提出了模型选择问题的最佳解决方案，第一个计算模型速度快但精度较低，第二个计算速度慢但精度更高。我们的解决方案背后的主要观点是，只有当精度提高带来的好处超过计算成本时，机器人才应该调用较慢的计算模型。我们表明，可以通过利用快速和慢速计算模型的准确性之间的统计相关性来执行这种成本效益分析。