Robots that interact with humans or perform delicate manipulation tasks must exhibit compliance. However, most commercial manipulators are rigid and suffer from significant friction, limiting end-effector tracking accuracy in torque-controlled modes. To address this, we present a real-time, open-source impedance controller that smoothly interpolates between joint-space and task-space compliance. This hybrid approach ensures safe interaction and precise task execution, such as sub-centimetre pin insertions. We deploy our controller on Frank, a dual-arm platform with two Kinova Gen3 arms, and compensate for modelled friction dynamics using a model-free observer. The system is real-time capable and integrates with standard ROS tools like MoveIt!. It also supports high-frequency trajectory streaming, enabling closed-loop execution of trajectories generated by learning-based methods, optimal control, or teleoperation. Our results demonstrate robust tracking and compliant behaviour even under high-friction conditions. The complete system is available open-source at https://github.com/applied-ai-lab/compliant_controllers.

Abstract--Force Sensing and Force Control are essential to many industrial applications. Typically, a 6-axis Force/Torque (F/T) sensor is mounted between the robot's wrist and the endeffector in order to measure the forces and torques exerted by the environment onto the robot (the external wrench). Although a typical 6-axis F/T sensor can provide highly accurate measurements, it is expensive and vulnerable to drift and external impacts. Existing methods aiming at estimating the external wrench using only the robot's internal signals are limited in scope: for example, wrench estimation accuracy was mostly validated in free-space motions and simple contacts as opposed to tasks like assembly that require high-precision force control. Our result opens the possibility of equipping the existing 2.7 million industrial robots with Force Sensing and Force Control Model-free methods, based, e.g., on Neural Networks, have In general, to our knowledge, no method - whether modelbased Force Sensing and Force Control are essential to many or model-free - has been shown to accurately and industrial applications, from contact-based inspection to assembly, reliably estimate the external wrench in both free-space and incontact sanding, deburring, and polishing [1]-[3]. This requirement is crucial for achieving nontrivial a 6-axis Force/Torque (F/T) sensor is mounted between the tasks like tight assembly and hand-guiding, alternating robot's wrist and the end-effector in order to measure the between free-space and in-contact robot motions. These tasks forces and torques exerted by the environment onto the robot have yet to be demonstrated in existing works and are, more (the external wrench). Consequently, there and argue that the above requirement can be satisfied has been a significant research effort aimed at estimating the if particular attention is devoted to the structure of the training external wrench using only the robot's internal signals, such dataset. In particular, we highlight the importance of collecting as joint position, joint velocity, or motor current readings.