论文标题
混合真实机器人行为重播：系统实施
Mixed-Reality Robot Behavior Replay: A System Implementation
论文作者
论文摘要
随着机器人越来越复杂，他们必须解释自己的行为，以获得信任和接受。但是，仅凭口头解释，可能很难充分传达有关过去行为的信息，尤其是关于因机器人或人类行为而不再存在的物体的信息。人类经常试图身体模仿过去的运动，以伴随口头解释。受这种人类互动的启发，我们描述了该工具纸中机器人过去行为重播的系统的技术实施。具体而言，我们使用行为树来编码和单独的机器人行为，而示意性mongoDB在结构上存储并查询了基础传感器数据和关节控制消息以进行将来的重播。我们的方法概括了不同类型的重播，包括操纵和导航重播，以及视觉（即增强现实（AR））和听觉重播。此外，我们简要总结了用户研究，以进一步提供其有效性和效率的经验证据。示例代码和说明可在https://github.com/umhan35/robot-behavior-replay上找到。
As robots become increasingly complex, they must explain their behaviors to gain trust and acceptance. However, it may be difficult through verbal explanation alone to fully convey information about past behavior, especially regarding objects no longer present due to robots' or humans' actions. Humans often try to physically mimic past movements to accompany verbal explanations. Inspired by this human-human interaction, we describe the technical implementation of a system for past behavior replay for robots in this tool paper. Specifically, we used Behavior Trees to encode and separate robot behaviors, and schemaless MongoDB to structurally store and query the underlying sensor data and joint control messages for future replay. Our approach generalizes to different types of replays, including both manipulation and navigation replay, and visual (i.e., augmented reality (AR)) and auditory replay. Additionally, we briefly summarize a user study to further provide empirical evidence of its effectiveness and efficiency. Sample code and instructions are available on GitHub at https://github.com/umhan35/robot-behavior-replay.
