In this work a control scheme is proposed to enforce dynamic obstacle avoidance constraints to the full body of actively compliant robots. We argue that both compliance and accuracy are necessary to build safe collaborative robotic systems; obstacle avoidance is usually not enough, due to the reliance on perception systems which exhibit delays and errors. Our scheme is able to successfully avoid obstacles, while remaining compliant in the entirety of the executed task. Therefore, in case of unexpected collisions due to perception system errors, the robot remains safe for humans and its environment. Our approach is validated through experiments with simulated and real obstacles utilizing a 7-dof KUKA LBR iiwa robotic manipulator.

This paper presents a teaching by demonstration method for contact tasks with periodic movement on planar surfaces of unknown pose. To learn the motion on the plane, we utilize frequency oscillators with periodic movement primitives and we propose modified adaptation rules along with an extraction method of the task's fundamental frequency by automatically discarding near-zero frequency components. Additionally, we utilize an online estimate of the normal vector to the plane, so that the robot is able to quickly adapt to rotated hinged surfaces such as a window or a door. Using the framework of progressive automation for compliance adaptation, the robot transitions seamlessly and bi-directionally between hand guidance and autonomous operation within few repetitions of the task. While the level of automation increases, a hybrid force/position controller is progressively engaged for the autonomous operation of the robot. Our methodology is verified experimentally in surfaces of different orientation, with the robot being able to adapt to surface orientation perturbations.

Many novel physical assistance devices are beginning to incorporate intelligent robotic systems and mechatronic components. In terms of a human-centered design it is crucial to assess the perceived subjective usability and acceptance of these systems. A questionnaire was thus designed to evaluate novel physically assisting devices in order to support developers in their design decisions as well as users during individualizing of their assistive devices. Two studies (m = 9, n2 = 21), using two different devices, were conducted to analyze objectivity, reliability, and validity. The results show an overall high internal consistency (Cronbach's α > 0.8), which indicates reliability and applicability of the QUEAD. Criterion validity was tested applying correlations with established objective measures for efficiency (time to task completion), effectivity (errors and collisions), and commitment (mean force). Construct validity was applied using a proposed model and correlations to verify convergence. The results show that the QUEAD is able to assess perceived usability and acceptance.

This paper addresses the problem of providing feedback to the operator about the manipulator's performance during human-robot physical interaction. A method is proposed that implements virtual constraints in Cartesian admittance control in order to prevent the operator from guiding the manipulator to low-performance configurations. The constraints are forces expressed in the Cartesian frame, which restrict the translation of the end-effector when the operator guides the robot below a certain performance threshold. These forces are calculated online by numerically approximating the gradient of the performance index with respect to the Cartesian frame attached to the end-effector. An experimental evaluation is conducted involving human-robot interaction with a 7-DOF LWR serial manipulator under Cartesian admittance control, using the kinematic manipulability index of the manipulator as the performance measure for singularity avoidance.

In this paper, the co-activation level of the arm muscles is used as an indication of the end-point stiffness for improving human-robot cooperation. A variable admittance controller is proposed to adjust the virtual damping in real time by measuring the operator's muscle activation by means of surface EMG. An experimental user study is conducted that simulates both high accuracy and fast transition movements, involving human-robot interaction with a 7-DOF LWR serial manipulator. The proposed method is compared to constant admittance and is evaluated in terms of movement accuracy, execution time, and the operator's energy consumption. The results demonstrate that there is a significant reduction of the operator's effort and an improvement of the cooperative motion accuracy.

The emerging field of physical human-robot interaction raises the need to distinguish collisions over intended contacts in order to guarantee safe and seamless interaction. In this paper, a novel contact distinction method is proposed that monitors the externally applied forces/torques and is able to distinguish unexpected collisions from intended contacts during cooperative tasks. The method is based on a frequency domain analysis of the externally applied forces using the Fast Fourier Transform. Moreover, a tuning method is proposed to adjust the thresholds for the detection, according to the desired dynamic behavior of the admittance controller. The collision distinction method is evaluated experimentally in a human-robot cooperation task with multiple subjects using a 7DOF LWR manipulator.

In this paper, a variable admittance controller based on reinforcement learning is proposed for human-robot co-manipulation tasks. Setting as the goal of the reinforcement learning algorithm the minimisation of the jerk throughout a point-to-point movement, the proposed controller can learn the appropriate damping for effective cooperation without any prior knowledge of the target position or other task characteristics. The performance of the proposed variable admittance controller is investigated on a co-manipulation task with a number of subjects using a KUKA LWR robot, demonstrating considerable reduction both in the effort required by the operator and in the completion time of the task.

This paper presents a method for variable admittance control in human-robot cooperation tasks, that combines a human-like decision making process and an adaptation algorithm. A Fuzzy Inference System is designed that relies on the measured velocity and the force applied by the operator to modify on-line the damping of the robot admittance, based on expert knowledge for intuitive cooperation. A Fuzzy Model Reference Learning Controller is used to adapt the Fuzzy Inference System according to the minimum jerk trajectory model. To evaluate the performance of the proposed controller a point-to-point cooperation task is conducted with multiple subjects using a KUKA LWR robot.