In robotic teleoperation, it is crucial to be able to dynamically adjust interactions with the environment. Drawing inspiration from human behavior during interactions, Variable Impedance Control (VIC) has been widely adopted to enhance robotic flexibility and adaptability. However, maintaining the passivity of such control systems remains a critical safety concern. This paper introduces an optimization-based framework for passive variable impedance control in bilateral teleoperation, combining the advantages of Passivity Filters (PFs), Time-Domain Passivity (TDP) control, and Passive-Set-Position-Modulation (PSPM). The method solves an optimization problem aimed at dissipating the energy that could lead to a lack of passivity. The proposed method is assessed through experiments, illustrating its ability to keep the teleoperation system passive and safe under a variable impedance profile.

In application domains such as surgical robotics, fully autonomous control remains a long-term ambition and the systems are mostly teleoperated. In this article, the presence of an operator in-the-loop is exploited to perform the online registration of an initially inaccurate haptic guidance and the calibration of robot kinematic models using operator’s intention instead of relying on exteroceptive sensors. This is used to improve online haptic guidance in the context of shared control, or to progress toward automatic task completion after an initial learning phase. The method presented in this article is based on an optimization in the task space to minimize the errors between the executed and desired trajectories, both estimated from models. This approach is particularly relevant when the execution of a planned task would suffer from errors that exteroceptive measurements could not fully correct, because of sensor inaccuracy or unavailability. A user study realized for a drawing task is detailed to illustrate that initially inaccurate task registration and robot models can be corrected from user inputs only. The results show that the proposed algorithm can learn the correct models, which in turns significantly improves the quality of the haptic guidance and decreases path deviations during the teleoperated task.

Haptic guidance can improve accuracy and dexterity during the teleoperation of a robot, but only if the model of the task used to provide the assistance is accurate. In medical robotics, the registration of a task from pre-operative planning from medical images to the robot’s task-space can be erroneous. Additionally, the deformability of the environment can require online correction of a planned task. Therefore, we propose a method to update the geometry and the registration of a pathfollowing task online. This model is simultaneously used to physically guide the user during the teleoperation. Experimental results obtained on a haptic interface show the validity of the approach for a simulated 2D task.