论文标题
一种质量多样性方法，可以在共享自主权中自动生成人类机器人互动场景
A Quality Diversity Approach to Automatically Generating Human-Robot Interaction Scenarios in Shared Autonomy
论文作者
论文摘要
人类与机器人之间相互作用的规模和复杂性的增长突出了需要新的计算方法自动评估新型算法和应用的需求。探索在模拟中相互作用的人类和机器人的各种场景可以提高对机器人系统的理解，并避免在现实世界中的潜在昂贵失败。我们将此问题提出为质量多样性（QD）问题，其目标是通过同时探索环境和人类行动来发现各种故障场景。我们专注于共享的自主域，该机器人试图推断人类操作员的目标，并采用QD算法MAP-eLITE，以生成该域中两个已发表算法的方案：通过Hindsight Optimization Optimization Optimization-Optimization-Optimization和Lineal Policy Colity Blending共享自主权。一些生成的方案证实了以前的理论发现，而另一些则令人惊讶，并对最先进的实现有了新的了解。我们的实验表明，MAP-Elites在有效搜索场景空间方面优于蒙特卡洛模拟和基于优化的方法，从而强调了其对人类机器人相互作用算法自动评估的承诺。
The growth of scale and complexity of interactions between humans and robots highlights the need for new computational methods to automatically evaluate novel algorithms and applications. Exploring diverse scenarios of humans and robots interacting in simulation can improve understanding of the robotic system and avoid potentially costly failures in real-world settings. We formulate this problem as a quality diversity (QD) problem, where the goal is to discover diverse failure scenarios by simultaneously exploring both environments and human actions. We focus on the shared autonomy domain, where the robot attempts to infer the goal of a human operator, and adopt the QD algorithm MAP-Elites to generate scenarios for two published algorithms in this domain: shared autonomy via hindsight optimization and linear policy blending. Some of the generated scenarios confirm previous theoretical findings, while others are surprising and bring about a new understanding of state-of-the-art implementations. Our experiments show that MAP-Elites outperforms Monte-Carlo simulation and optimization based methods in effectively searching the scenario space, highlighting its promise for automatic evaluation of algorithms in human-robot interaction.
