Personalized robot-assisted dressing using user modeling in latent spaces
Personalized robot-assisted dressing using user modeling in latent spaces
复制标题
使用潜在空间中的用户建模进行个性化机器人辅助穿衣
DOI：
10.1109/iros.2017.8206206
复制
发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Y. Demiris
中科院分区：
文献类型：
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作者：
Fan Zhang;Antoine Cully;Y. Demiris
机器人有潜力在残疾人和老年人的日常工作中为他们提供巨大的支持，比如穿衣。最近许多关于机器人穿衣辅助的研究通常将穿衣视为一个轨迹规划问题。然而，在穿戴过程中很少考虑用户的移动，这往往会导致规划的轨迹失败，并可能使用户处于危险之中。考虑用户移动的主要困难是由于机器人、用户和衣服在穿衣过程中造成的严重遮挡，这阻碍了视觉传感器实时准确地检测用户的姿势。在本文中，我们通过引入一种方法来解决这个问题，该方法允许机器人根据用户移动对机器人抓手施加的力自动调整其运动。本文的主要贡献有两个：1)采用分层多任务控制策略自动适应机器人的运动，最小化用户运动引起的用户与机器人之间的作用力；2)基于潜在空间中的高斯过程潜变量模型建立的用户运动限制，并从潜在空间中提取密度信息，在线更新穿衣轨迹。这两个贡献的组合导致了个性化的穿衣辅助，该辅助穿衣可以应对穿衣过程中不可预测的用户移动，同时不断最小化机器人可能施加在用户身上的力。实验结果表明，该方法使Baxter仿人机器人能够为模拟上半身损伤的人类用户提供个性化的穿衣辅助。
Robots have the potential to provide tremendous support to disabled and elderly people in their everyday tasks, such as dressing. Many recent studies on robotic dressing assistance usually view dressing as a trajectory planning problem. However, the user movements during the dressing process are rarely taken into account, which often leads to the failures of the planned trajectory and may put the user at risk. The main difficulty of taking user movements into account is caused by severe occlusions created by the robot, the user, and the clothes during the dressing process, which prevent vision sensors from accurately detecting the postures of the user in real time. In this paper, we address this problem by introducing an approach that allows the robot to automatically adapt its motion according to the force applied on the robot's gripper caused by user movements. There are two main contributions introduced in this paper: 1) the use of a hierarchical multi-task control strategy to automatically adapt the robot motion and minimize the force applied between the user and the robot caused by user movements; 2) the online update of the dressing trajectory based on the user movement limitations modeled with the Gaussian Process Latent Variable Model in a latent space, and the density information extracted from such latent space. The combination of these two contributions leads to a personalized dressing assistance that can cope with unpredicted user movements during the dressing while constantly minimizing the force that the robot may apply on the user. The experimental results demonstrate that the proposed method allows the Baxter humanoid robot to provide personalized dressing assistance for human users with simulated upper-body impairments.