Variable impedance control (VIC) is rapidly becoming an important ingredient for robotic manipulation in unstructured and uncertain environments. In such situations, it is often necessary to rapidly adapt to different impedance levels as per the task requirements, and to return to a low baseline impedance for safety requirements. Such a capability is crucial to stabilize interactions in divergent force fields, which commonly arise in a variety of contact and force production tasks and occasionally in non-contact tasks. Conventional methods, such as iterative learning control, often underperform in terms of stabilization and efficacy. While VIC algorithms perform better, typical challenges in such methods include unnecessarily high impedance adaptation in divergent fields, difficulty in distinguishing between error-independent and error-based divergent forces, and reliance on the Jacobian inverse which diminishes performance near singularities. In this paper, we introduce an innovative VIC algorithm that addresses typical VIC challenges. The proposed method employs a Cartesian-space field adaptation avoiding the need for inverting the Jacobian during adaptation, while at the same time providing a theoretical stabilization guarantee. Utilizing the Lyapunov function, the algorithm is shown to drive tracking errors to zero, even in the presence of divergent position and velocity-error fields and error-independent forces. Notably, the system exhibits human-like relearning at a faster pace when exposed to previously learned fields or perturbations, improving learning speeds by up to 47.97%. Performance validation was conducted through simulations on a two-link serial chain manipulator that mimics the human arm, as well as tests on a seven degrees-of-freedom KUKA robot, underscoring the algorithm’s advantages in handling VIC challenges and uncertain conditions.

可变阻抗控制（VIC）正在迅速成为非结构化和不确定环境中机器人操作的重要组成部分。在这种情况下，通常需要根据任务要求快速适应不同的阻抗水平，并返回到低基线阻抗以满足安全要求。这种能力对于稳定不同力场中的相互作用至关重要，这种力场通常出现在各种接触和力产生任务中，偶尔也出现在非接触任务中。传统方法，例如迭代学习控制，在稳定性和有效性方面通常表现不佳。虽然 VIC 算法性能更好，但此类方法的典型挑战包括发散场中不必要的高阻抗适应、难以区分与误差无关的发散力和基于误差的发散力，以及对雅可比逆的依赖，这会降低奇点附近的性能。在本文中，我们介绍了一种创新的 VIC 算法，可解决典型的 VIC 挑战。所提出的方法采用笛卡尔空间场自适应，避免了在自适应期间反转雅可比行列式的需要，同时提供了理论上的稳定性保证。利用李亚普诺夫函数，即使存在不同的位置和速度误差场以及与误差无关的力，该算法也能将跟踪误差降至零。值得注意的是，当暴露于先前学习过的领域或扰动时，该系统以更快的速度表现出类似人类的重新学习，将学习速度提高了高达 47.97%。通过在模仿人臂的二连杆串行链机械手上进行仿真以及在七自由度库卡机器人上进行测试来进行性能验证，强调了该算法在处理 VIC 挑战和不确定条件方面的优势。