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arXiv 2003.07745cs.AIcs.RO
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学习优化能力感知系统中的自主性

Learning to Optimize Autonomy in Competence-Aware Systems

University of Massachusetts Amherst(马萨诸塞大学阿默斯特分校);Alliance Innovation Lab Silicon Valley(联盟创新实验室硅谷);The University of Texas at Austin(得克萨斯大学奥斯汀分校)

Connor Basich, Justin Svegliato, Kyle Hollins Wray, Stefan Witwicki, Joydeep Biswas, Shlomo Zilberstein

摘要:

作为在偶尔需要依赖人类的领域中部署自治系统的范例,对半自治系统(SAS)的兴趣正在迅速增长。这种模式允许服务机器人或自动驾驶汽车以不同的自主水平运行,并在需要人类判断的情况下提供安全性。我们提出了一个内省的自主性模型,通过经验在线学习和更新,并规定代理在任何给定的情况下可以自主行动的程度。我们定义了一个能力感知系统(CAS),明确地模拟自己的熟练程度在不同的自治水平和可用的人类反馈。CAS学会根据经验调整其自主水平,以最大限度地提高整体效率,并考虑人工协助的成本。我们分析了CAS的收敛特性,并提供了机器人交付和自动驾驶领域的实验结果,证明了该方法的好处。

英文摘要:

Interest in semi-autonomous systems (SAS) is growing rapidly as a paradigm to deploy autonomous systems in domains that require occasional reliance on humans. This paradigm allows service robots or autonomous vehicles to operate at varying levels of autonomy and offer safety in situations that require human judgment. We propose an introspective model of autonomy that is learned and updated online through experience and dictates the extent to which the agent can act autonomously in any given situation. We define a competence-aware system (CAS) that explicitly models its own proficiency at different levels of autonomy and the available human feedback. A CAS learns to adjust its level of autonomy based on experience to maximize overall efficiency, factoring in the cost of human assistance. We analyze the convergence properties of CAS and provide experimental results for robot delivery and autonomous driving domains that demonstrate the benefits of the approach.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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