In this work, we present a learning from demonstration solution for automating a surgical blunt dissection task. In addition to learning motion trajectories, our goal is to learn variable impedance behaviors that enable the robot to interact safely and compliantly during the task. To that end, we propose a teaching interface using bilateral teleoperation, which allows the natural transfer of human motions and impedance behaviors skills to robots. The demonstrated profiles are captured with Dynamic Movement Primitives and Gaussian Mixture Models, which subsequently provide the robot a reference motion plan, and a stiffness adaptation policy, during physical interaction. Experimental validation on real robot hardware shows the effectiveness of the proposed approach in terms of ensuring successful task execution, as well as safety compared to stiff high-gain control.

In this paper, we present a controller that combines motion generation and control in one loop, to endow robots with reactivity and safety. In particular, we propose a control approach that enables to follow the motion plan of a first order Dynamical System (DS) with a variable stiffness profile, in a closed loop configuration where the controller is always aware of the current robot state. This allows the robot to follow a desired path with an interactive behavior dictated by the desired stiffness. We also present two solutions to enable a robot to follow the desired velocity profile, in a manner similar to trajectory tracking controllers, while maintaining the closed-loop configuration. Additionally, we exploit the concept of energy tanks in order to guarantee the passivity during interactions with the environment, as well as the asymptotic stability in free motion, of our closed-loop system. The developed approach is evaluated extensively in simulation, as well as in real robot experiments, in terms of performance and safety both in free motion and during the execution of physical interaction tasks.Note to Practitioners—The approach presented in this work allows for safe and reactive robot motions, as well as the capacity to shape the robot’s physical behavior during interactions. This becomes crucial for performing contact tasks that might require adaptability or for interactions with humans as in shared control or collaborative tasks. Furthermore, the reactive properties of our controller make it adequate for robots that operate in proximity to humans or in dynamic environments where potential collisions are likely to happen.

Recently, several approaches have attempted to combine motion generation and control in one loop to equip robots with reactive behaviors, that cannot be achieved with traditional time-indexed tracking controllers. These approaches however mainly focused on positions, neglecting the orientation part which can be crucial to many tasks e.g. screwing. In this work, we propose a control algorithm that adapts the robot's rotational motion and impedance in a closed-loop manner. Given a first-order Dynamical System representing an orientation motion plan and a desired rotational stiffness profile, our approach enables the robot to follow the reference motion with an interactive behavior specified by the desired stiffness, while always being aware of the current orientation, represented as a Unit Quaternion (UQ). We rely on the Lie algebra to formulate our algorithm, since unlike positions, UQ feature constraints that should be respected in the devised controller. We validate our proposed approach in multiple robot experiments, showcasing the ability of our controller to follow complex orientation profiles, react safely to perturbations, and fulfill physical interaction tasks.

This article presents a hierarchical passivity-based compliance controller that exploits robot redundancy and aims at achieving an impedance behavior with a time-varying stiffness on all the priority levels. Unfortunately, this gives rise to certain control actions that lead to the loss of the safety-critical passivity feature. To deal with this problem, we employ the concept of virtualenergy tanksthat keep track of the passivity violating energy in the system, ensuring that it remains bounded. This restores the passivity in the system, which guarantees the stable interaction with any passive environment. Furthermore, we augment our controller with an additional safety layer, which ensures that the energy injected through the tank into the system remains below a safe limit, defined based on the maximum kinetic energy allowed in the system. Finally, our approach is validated in terms of performance during task execution and safety both in simulations and on real-robot hardware.

Kinematic redundancy enhances the dexterity and flexibility of robot manipulators. By exploiting the redundant degrees of freedom, auxiliary null space tasks can be carried out in addition to the primary task. Such auxiliary tasks are often formulated in terms of a performance or safety criterion that shall be minimized. If the optimization criterion, however, is defined in global terms, then it is directly affected by the primary task. As a consequence, the task achievement of the auxiliary task may be unnecessarily detrimented by the main task. In addition to modifying the primary task via constraint relaxation, a possible solution for improving the performance of the auxiliary task is to relax the primary task temporarily via time scaling. This gives the null space task more time for achieving its objective. In this paper, we propose several such time scaling schemes and verify their performance for a DLR/KUKA Lightweight Robot with one redundant degree of freedom. Finally, we extend the concept to multiple prioritized tasks and provide a simulation example.