In the context of interaction with unmodelled systems, it becomes imperative for a robot controller to possess the capability to dynamically adjust its actions in real-time, enhancing its resilience in the face of fluctuating environmental conditions. This adaptation process must be performed in a stability-preserving fashion, and resourcefully exploit the knowledge acquired during the interaction process. In this article, we propose a novel control strategy, based on the synergistic usage of state-of-the-art passivity-based control and Deep Reinforcement Learning (DRL). The concept of energy tank is used to provide stability guarantees for the interaction controller with uncertain environments, while an online learning policy allows to properly estimate the requirements of the task and adapt the controller accordingly, thus simultaneously achieving stability and performance. The proposed architecture is successfully validated through simulations and experiments with a collaborative manipulator in a surface polishing task.

A fruitful collaboration is based on the mutual knowledge of each other skills and on the possibility of communicating their own limits and proposing alternatives to adapt the execution of a task to the capabilities of the collaborators. This paper aims at reproducing such a scenario in a human-robot collaboration setting by proposing a novel communication control architecture. Exploiting control barrier functions, the robot is made aware of its (dynamic) skills and limits and, thanks to a local predictor, it is able to assess if it is possible to execute a requested task and, if not, to propose alternative by relaxing some constraints. The controller is interfaced with a communication infrastructure that enables human and robot to set up a bidirectional communication about the task to execute and the human to take an informed decision on the behavior of the robot. A comparative experimental validation is proposed.

During physical human robot collaboration, it is important to be able to implement a time-varying interactive behaviour while ensuring robust stability. Admittance control and passivity theory can be exploited for achieving these objectives. Nevertheless, when the admittance dynamics is time-varying, it can happen that, for ensuring a passive and stable behaviour, some spurious dissipative effects have to be introduced in the admittance dynamics. These effects are perceived by the user and degrade the collaborative performance. In this paper we exploit the task redundancy of the manipulator in order to harvest energy in the null space and to avoid spurious dynamics on the admittance. The proposed architecture is validated by simulations and by experiments onto a collaborative robot.

In robotic applications where the autonomy is shared between the human and the robot, the autonomous behavior of the robotic system is determined considering mainly the task to be executed and the data collected from the environment using, e.g., formal methods and machine learning techniques. Nevertheless, it is important to correctly translate high-level decision into low-level control inputs in order to avoid an unstable behavior due to a naive implementation of the autonomy. In this article, we propose an energy-based architecture for shared autonomy that allows to reproduce as closely as possible the desired behavior, while ensuring a robust stability of the robotic system. The proposed architecture is experimentally validated in two application scenarios: shared control of a multirobot system and variable admittance control in human robot collaboration

In Human-Robot Collaboration, the robot operates in a highly dynamic environment. Thus, it is pivotal to guarantee the robust stability of the system during the interaction but also a high flexibility of the robot behavior in order to ensure safety and reactivity to the variable conditions of the collaborative scenario.In this paper we propose a control architecture capable of maximizing the flexibility of the robot while guaranteeing a stable behavior when physically interacting with the environment. This is achieved by combining an energy tank based variable admittance architecture with control barrier functions. The proposed architecture is experimentally validated on a collaborative robot.