Robust and Dexterous Dual-Arm Tele-Cooperation Using Adaptable Impedance Control

In recent years, the need for robots to transi- tion from isolated industrial tasks to shared environments, including human-robot collaboration and teleoperation, has become increasingly evident. Building on the foundation of Fractal Impedance Control (FIC) introduced in our previ- ous work, this paper presents a novel extension to dual- arm tele-cooperation, leveraging the non-linear stiffness and passivity of FIC to adapt to diverse cooperative scenarios. Unlike traditional impedance controllers, our approach ensures stability without relying on energy tanks, as demonstrated in our prior research. In this paper, we further extend the FIC framework to bimanual operations, allowing for stable and smooth switching between different dynamic tasks without gain tuning. We also introduce a telemanipulation architecture that offers higher transparency and dexterity, addressing the challenges of signal latency and low-bandwidth communication. Through extensive experiments, we validate the robustness of our method and the results confirm the advantages of the FIC approach over traditional impedance controllers, showcasing its potential for applications in planetary exploration and other scenarios requiring dexterous telemanipulation. This paper’s contributions include the seamless integration of FIC into multi- arm systems, the ability to perform robust interactions in highly variable environments, and the provision of a comprehensive comparison with competing approaches, thereby significantly enhancing the robustness and adaptability of robotic systems. I. I￿￿￿￿￿￿￿￿￿￿￿ Robots are valuable resources for adaptable and inter- changeable manufacturing. The advancements in technology brought about by these robots significantly contributed to the enhancement of our overall well-being and led to significant changes in our population. Currently, with the aging popula- tion, we are experiencing a decline in the number of people in the workforce and an escalating need for healthcare services catering to age-related ailments. Robots have the potential to address these demands by facilitating improved healthcare, minimizing work-related injuries, and decreasing risks for operators in hazardous environments [1]–[4]. The successful execution of these tasks requires the presence of control frameworks that can swiftly transition between various tasks within seconds, requiring minimal involvement from a user or operator. This is particularly important because the opera- tor might lack the necessary technical expertise to reprogram This work is supported by EU H2020 project: Enhancing Healthcare with Assistive Robotic Mobile Manipulation (HARMONY, 101017008) and the Alan Turing Institute, UK. Keyhan Kouhkiloui Babarahmati, Mohammadreza Kasaei, Michael Mis- try and Sethu Vijayakumar are with the School of Informatics, University of Edinburgh, UK and Carlo Tiseo is with the School of Engineering and Informatics, University of Sussex, UK. Email: keyhan.kouhkiloui@ed.ac.uk AM xM FFB F I CSHE Sigma-7 Master FICR fv FFB xd Kc FICR Non-Haptic Haptic Panda Panda GUI Grasp DoF Replica Position Signal Force Signal Gr Gr Gr 0 (a) (b) Fig. 1. (a) Haptic: The master moves the replica (Panda, Franka Emika AG) by applying a virtual force ( 5v). The interaction force/torque feedback at the end-effector (퐹FB) is scaled in AM to generate the haptic feedback. The operator can also act on the replica end-effector’s reference pose (Gd) by activating the master device with its grasp joint or via the GUI. Non- Haptic: The replica is controlled by issuing sequences of Gd that act as via points for the trajectory of the replica. (b) Dual-arm Tele-cooperation Setup. the robot’s controller in such situations. Multiple solutions have been proposed to deploy robots in the applications mentioned above that we classify in four cat- egories: high-level algorithms, adaptable controllers, smart sensing, and adaptable mechatronics [5]–[11]. However, it is essential for all these components to work together in order to make robots sufficiently flexible and robust for integration into our daily lives. The high-level algorithms category encompasses various optimization and machine learning methods that are employed to plan and adjust the actions of the robot. Adaptable controllers refer to architec- tures that enable a certain level of adaptation by integrating the action plan with sensor information. Smart sensing methods utilize multiple sensors to accurately estimate and predict the states of both the robot and the environment. Adaptable mechatronics involves technologies that can adapt to the environment, either through passive means such as soft materials and composites, or through active means such as Variable Stiffness Actuators and smart materials. 2024 IEEE International Conference on Robotics and Automation (ICRA 2024) May 13-17, 2024. Yokohama, Japan 979-8-3503-8457-4/24/$31.00 ©2024 IEEE 17337