Perturbation-based stiffness inference in variable impedance control

Journal Article (2022)


IEEE Robotics and Automation Letters







Doc link


Download the digital copy of the doc pdf document


One of the major challenges in learning from demonstration is to teach the robot a wider set of task features than the plain trajectories to be followed. In this sense, one key parameter is stiffness, i.e., the rigidity that the manipulator should exhibit when performing a task. The required robot stiffness is often not known a priori and varies along the execution of the task, thus its profile needs to be inferred from the demonstrations. In this work, we propose a novel, force-based algorithm for inferring time-varying stiffness profiles, leveraging the relationship between stiffness and tracking error, and involving human-robot interaction. We begin by gathering a set of demonstrations with kinesthetic teaching. Then, the robot executes a perturbed reference, obtained from these demonstrations by means of Gaussian process regression, and the human intervenes if the perturbation makes the manipulator deviate from its expected behaviour. Human intervention is measured and used to infer the desired control stiffness. In the experiments section, we show that our algorithm can be combined with different types of force sensors, and provide suitable processing algorithms. Our approach correctly infers the stiffness profiles from the force and electromyography sensors, their combination permitting also to comply with the physical constraints imposed by the environment. This is demonstrated in three experiments of increasing complexity: a motion in free Cartesian space, a rigid assembly task, and bed-making.


manipulators, robots.

Author keywords

compliant robots, impedance control, learning from demonstration, probabilistic inference

Scientific reference

E. Caldarelli, A. Colomé and C. Torras. Perturbation-based stiffness inference in variable impedance control. IEEE Robotics and Automation Letters, 7(4): 8823-8830, 2022.