A Dynamical System Approach for Adaptive Grasping, Navigation and Co-Manipulation with Humanoid Robots

Nadia Figueroa, Salman Faraji, M. Koptev, A. Billard
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引用次数: 12

Abstract

In this paper, we present an integrated approach that provides compliant control of an iCub humanoid robot and adaptive reaching, grasping, navigating and co-manipulating capabilities. We use state-dependent dynamical systems (DS) to (i) coordinate and drive the robots hands (in both position and orientation) to grasp an object using an intermediate virtual object, and (ii) drive the robot's base while walking/navigating. The use of DS as motion generators allows us to adapt smoothly as the object moves and to re-plan on-line motion of the arms and body to reach the object's new location. The desired motion generated by the DS are used in combination with a whole-body compliant control strategy that absorbs perturbations while walking and offers compliant behaviors for grasping and manipulation tasks. Further, the desired dynamics for the arm and body can be learned from demonstrations. By integrating these components, we achieve unprecedented adaptive behaviors for whole body manipulation. We showcase this in simulations and real-world experiments where iCub robots (i) walk-to-grasp objects, (ii) follow a human (or another iCub) through interaction and (iii) learn to navigate or comanipulate an object from human guided demonstrations; whilst being robust to changing targets and perturbations.
类人机器人自适应抓取、导航与协同操作的动态系统方法
在本文中,我们提出了一种集成的方法,提供了iCub人形机器人的柔性控制和自适应的到达、抓取、导航和协同操作能力。我们使用状态依赖的动力系统(DS)来(i)协调和驱动机器人的手(在位置和方向上)使用中间虚拟物体来抓取物体,以及(ii)在行走/导航时驱动机器人的基座。使用DS作为运动发生器使我们能够顺利地适应物体的移动,并重新规划手臂和身体的在线运动,以达到物体的新位置。由DS产生的期望运动与全身柔性控制策略结合使用,该策略可以吸收行走时的扰动,并为抓取和操作任务提供柔性行为。此外,手臂和身体所需的动力学可以从演示中学习。通过整合这些组件,我们实现了前所未有的全身操作自适应行为。我们在模拟和现实世界的实验中展示了这一点,其中iCub机器人(i)走到抓取对象,(ii)通过交互跟随人类(或另一个iCub), (iii)学习导航或操纵人类引导演示的对象;同时对变化的目标和扰动具有鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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