论文标题
从演示中学习
Heterogeneous Learning from Demonstration
论文作者
论文摘要
由于在行业和研究之间产生了预见的，广泛的影响，人类机器人系统能够利用人类及其机器人的优势的发展。我们认为，除非机器人能够以高度的自主权行动，从而减轻了手动任务或远程操作的负担，否则无法发挥这些系统的真正潜力。为了达到这种自治水平，机器人必须能够与其人类伙伴合作，从而在没有明确命令的情况下推断他们的需求。该推论要求机器人能够检测和分类其合作伙伴的异质性。我们提出了一个基于贝叶斯推论的异质示范中学习的框架，并在《星际争霸II》中的现实世界数据集上评估了一套方法。该评估提供了证据，表明我们的贝叶斯方法可以胜过传统方法高达12.8 $％$。
The development of human-robot systems able to leverage the strengths of both humans and their robotic counterparts has been greatly sought after because of the foreseen, broad-ranging impact across industry and research. We believe the true potential of these systems cannot be reached unless the robot is able to act with a high level of autonomy, reducing the burden of manual tasking or teleoperation. To achieve this level of autonomy, robots must be able to work fluidly with its human partners, inferring their needs without explicit commands. This inference requires the robot to be able to detect and classify the heterogeneity of its partners. We propose a framework for learning from heterogeneous demonstration based upon Bayesian inference and evaluate a suite of approaches on a real-world dataset of gameplay from StarCraft II. This evaluation provides evidence that our Bayesian approach can outperform conventional methods by up to 12.8$%$.
