论文标题
学习合规性适应性操纵
Learning Compliance Adaptation in Contact-Rich Manipulation
论文作者
论文摘要
合规机器人行为对于实现富含接触的操作任务至关重要。在此类任务中，重要的是要确保在正常任务执行过程中具有高刚度和力跟踪准确性，以及快速适应和投诉行为，以应对异常情况和变化。在本文中，我们提出了一种新的方法，用于学习接触量任务所需的力谱的预测模型。这样的模型允许检测意外情况并促进更好的自适应控制。该方法结合了基于双向门控复发单元（BI-GRU）和自适应力/阻抗控制器的异常检测。我们在人形机器人的模拟和现实世界实验中评估了这种方法。结果表明，该方法允许同时进行所需运动和力谱的高跟踪精度，以及由于物理人类相互作用而导致的强迫扰动的适应性。
Compliant robot behavior is crucial for the realization of contact-rich manipulation tasks. In such tasks, it is important to ensure a high stiffness and force tracking accuracy during normal task execution as well as rapid adaptation and complaint behavior to react to abnormal situations and changes. In this paper, we propose a novel approach for learning predictive models of force profiles required for contact-rich tasks. Such models allow detecting unexpected situations and facilitates better adaptive control. The approach combines an anomaly detection based on Bidirectional Gated Recurrent Units (Bi-GRU) and an adaptive force/impedance controller. We evaluated the approach in simulated and real world experiments on a humanoid robot.The results show that the approach allow simultaneous high tracking accuracy of desired motions and force profile as well as the adaptation to force perturbations due to physical human interaction.
