论文标题
从观察系统中学习考虑硬件级可重复使用性
Learning-from-Observation System Considering Hardware-Level Reusability
论文作者
论文摘要
机器人开发人员开发了各种类型的机器人，以满足用户的各种需求。用户的需求与适合用户的机器人有关，可能会有所不同。如果某个开发人员将提供与用户不同的机器人，则必须更改机器人特定的软件。另一方面，机器人软件开发人员希望尽可能多地重复使用他们开发的软件以减少他们的努力。我们建议考虑硬件级可重复使用性的系统设计。为此，我们从学习框架开始。该框架代表机器人 - 不平衡表示中的目标任务，因此可以与各种机器人共享表示的任务描述。执行任务时，有必要将机器人 - 敏捷的描述转换为目标机器人的命令。为了提高可重复性，首先，我们实施了技能库，机器人运动原始功能，仅考虑机器人手，我们认为机器人只是将手移到目标轨迹上的载体。如果我们愿意使用同一只机器人手，则可以重复使用该技能库。其次，我们使用通用的IK求解器快速交换机器人。我们通过将两个任务说明应用于Nextage和Fetch的两个不同的机器人，来验证硬件级可重复使用性。
Robot developers develop various types of robots for satisfying users' various demands. Users' demands are related to their backgrounds and robots suitable for users may vary. If a certain developer would offer a robot that is different from the usual to a user, the robot-specific software has to be changed. On the other hand, robot-software developers would like to reuse their developed software as much as possible to reduce their efforts. We propose the system design considering hardware-level reusability. For this purpose, we begin with the learning-from-observation framework. This framework represents a target task in robot-agnostic representation, and thus the represented task description can be shared with various robots. When executing the task, it is necessary to convert the robot-agnostic description into commands of a target robot. To increase the reusability, first, we implement the skill library, robot motion primitives, only considering a robot hand and we regarded that a robot was just a carrier to move the hand on the target trajectory. The skill library is reusable if we would like to the same robot hand. Second, we employ the generic IK solver to quickly swap a robot. We verify the hardware-level reusability by applying two task descriptions to two different robots, Nextage and Fetch.
