In reality, there is still much to be done for robots to be able to perform manipulation actions with full autonomy. Complicated manipulation tasks, such as cooking, may still require a person to perform some actions that are very risky for a robot to perform. On the other hand, some other actions may be very risky for a human with physical disabilities to perform. Therefore, it is necessary to balance the workload of a robot and a human based on their limitations while minimizing the effort needed from a human in a collaborative robot (cobot) set-up. This paper proposes a new version of our functional object-oriented network (FOON) that integrates weights in its functional units to reflect a robot's chance of successfully executing an action of that functional unit. The paper also presents a task planning algorithm for the weighted FOON to allocate manipulation action load to the robot and human to achieve optimal performance while minimizing human effort. Through a number of experiments, this paper shows several successful cases in which using the proposed weighted FOON and the task planning algorithm allow a robot and a human to successfully complete complicated tasks together with higher success rates than a robot doing them alone.
翻译:在现实中,对于机器人完全自主地进行操纵操作,还需要做很多事情。复杂的操作任务,如烹饪等,可能仍然需要一个人执行一些对机器人来说非常危险的操作。另一方面,对于身体残疾的人来说,其他一些行动可能非常危险。因此,有必要根据机器人和人类的局限性来平衡机器人和人类的工作量,同时尽量减少在协作机器人(cobot)设置中需要人类付出的努力。本文提出了我们功能性物体导向网络(FOON)的新版本,将功能性物体导向网络(FOON)的重量整合在一起,以反映机器人成功执行该功能单位行动的机会。本文还提出了加权的FOON的任务规划算法,将操纵行动负荷分配给机器人和人类,以取得最佳的性能,同时最大限度地减少人类的努力。通过一些实验,本文件展示了一些成功的事例,即使用拟议的加权的FOON和任务规划算法,使机器人和人类能够成功地完成复杂的任务,同时完成的成功率高于仅由机器人完成的机器人完成的成功率。