Robots that share an environment with humans may communicate their intent using a variety of different channels. Movement is one of these channels and, particularly in manipulation tasks, intent communication via movement is called legibility. It alters a robot's trajectory to make it intent expressive. Here we propose a novel evaluation method that improves the data efficiency of collected experimental data when benchmarking approaches generating such legible behavior. The primary novelty of the proposed method is that it uses trajectories that were generated independently of the framework being tested. This makes evaluation easier, enables N-way comparisons between approaches, and allows easier comparison across papers. We demonstrate the efficiency of the new evaluation method by comparing 10 legibility frameworks in 2 scenarios. The paper, thus, provides readers with (1) a novel approach to investigate and/or benchmark legibility, (2) an overview of existing frameworks, (3) an evaluation of 10 legibility frameworks (from 6 papers), and (4) evidence that viewing angle and trajectory progression matter when users evaluate the legibility of a motion.
翻译:与人类共享环境的机器人可以使用多种不同渠道交流其意图。 移动是这些渠道之一, 特别是操作任务中的转移, 通过移动的意向通信被称为可辨识性。 它改变了机器人的轨迹, 使其具有表达性。 我们在这里提出一种新的评价方法, 提高所收集的实验数据的数据效率, 通过基准方法来产生这种可辨识行为。 提议方法的主要新颖之处在于它使用与正在测试的框架独立产生的轨迹。 这使得评价更加容易, 能够对各种方法进行N- way比较, 便于对各种文件进行比较。 我们通过在两种情景中比较10个可辨识性框架来展示新评价方法的效率。 因此, 该文件为读者提供了(1) 调查和/或基准可辨识性的新办法,(2) 对现有框架的概览,(3) 对10个可辨性框架(来自6份文件)的评价,以及(4) 在用户评估运动的可辨识性时查看角度和轨迹重要性的证据。