Assistive robot arms enable people with disabilities to conduct everyday tasks on their own. These arms are dexterous and high-dimensional; however, the interfaces people must use to control their robots are low-dimensional. Consider teleoperating a 7-DoF robot arm with a 2-DoF joystick. The robot is helping you eat dinner, and currently you want to cut a piece of tofu. Today's robots assume a pre-defined mapping between joystick inputs and robot actions: in one mode the joystick controls the robot's motion in the x-y plane, in another mode the joystick controls the robot's z-yaw motion, and so on. But this mapping misses out on the task you are trying to perform! Ideally, one joystick axis should control how the robot stabs the tofu and the other axis should control different cutting motions. Our insight is that we can achieve intuitive, user-friendly control of assistive robots by embedding the robot's high-dimensional actions into low-dimensional and human-controllable latent actions. We divide this process into three parts. First, we explore models for learning latent actions from offline task demonstrations, and formalize the properties that latent actions should satisfy. Next, we combine learned latent actions with autonomous robot assistance to help the user reach and maintain their high-level goals. Finally, we learn a personalized alignment model between joystick inputs and latent actions. We evaluate our resulting approach in four user studies where non-disabled participants reach marshmallows, cook apple pie, cut tofu, and assemble dessert. We then test our approach with two disabled adults who leverage assistive devices on a daily basis.
翻译:辅助机器人武器让残疾人能够自己执行日常任务。 这些手臂是伸缩和高维的; 但是, 接合器必须用来控制机器人的动作是低维的。 考虑用 2 - DoF 游戏杆远程操作一个 7 - DoF 机器人臂。 机器人正在帮助你吃晚饭, 而目前你想要切除一块豆腐。 今天的机器人假定在游戏棒投入和机器人动作之间有一个预先定义的绘图: 一种模式是, 游戏棒控制机器人在xy 平面上的运动, 另一种模式是, 游戏棍控制机器人的 Z - yaw 运动,等等。 但是这幅绘图会错失你正在尝试完成的任务! 理想的是, 一个游戏杆轴轴轴轴应该控制机器人如何刺杀豆腐, 而其他轴应该控制不同的切除器。 我们的洞穴杆可以将机器人的高维度动作嵌到低维度和人类控制的潜在动作。 我们把这个过程分为三个部分。 我们把这个过程分为一个不透明的轨道 三个部分。 首先, 我们探索最终的游戏模型, 将持续的游戏模型 来学习高深层的动作 行动 。