Holger Urbanek

Real-time reactive motion generation based on variable attractor dynamics and shaped velocities

This paper describes a novel method for motion generation and reactive collision avoidance. The algorithm performs arbitrary desired velocity profiles in absence of external disturbances and reacts if virtual or physical contact is made in a unified fashion with a clear physically interpretable behavior. The method uses physical analogies for defining attractor dynamics in order to generate smooth paths even in presence of virtual and physical objects. The proposed algorithm can, due to its low complexity, run in the inner most control loop of the robot, which is absolutely crucial for safe Human Robot Interaction. The method is thought as the locally reactive real-time motion generator connecting control, collision detection and reaction, and global path planning.

Robotics of human movements

The construction of robotic systems that can move the way humans do, with respect to agility, stability and precision, is a necessary prerequisite for the successful integration of robotic systems in human environments. We explain human-centered views on robotics, based on the three basic ingredients (1) actuation; (2) sensing; and (3) control, and formulate detailed examples thereof.

Learning from demonstration: repetitive movements for autonomous service robotics

This paper presents a method for learning and generating rhythmic movement patterns based on a simple central oscillator. It can be used to generate cyclic movements for a robot system which has to solve complex tasks. The system is laid out in such a way that multiple motion dimensions, or degrees of freedom of the robot, are represented independent of each other; therefore, an extension to higher-dimensional problems is easily possible. Guiding the robot by holding its end-effector, the user teaches simple movement primitives forming the basis for a more complex task. Each movement primitive is represented in the system using an oscillator combined with a learned nonlinear mapping. These primitives are then optimally combined to a complete solution to the posed problem. Said optimality is obtained using simulated annealing with the A* global search algorithm. Our approach is demonstrated on the problem of wiping a table, but can be used for many typical problems in service and household robotics.

Relation between object properties and EMG during reaching to grasp

In order to stably grasp an object with an artificial hand, a priori knowledge of the objects properties is a major advantage, especially to ensure subsequent manipulation of the object held by the hand. This is also true for hand prostheses: pre-shaping of the hand while approaching the object, similar to able-bodied, allows the wearer for a much faster and more intuitive way of handling and grasping an object. For hand prostheses, it would be advantageous to obtain this information about object properties from a surface electromyography (sEMG) signal, which is already present and used to control the active prosthetic hand. We describe experiments in which human subjects grasp different objects at different positions while their muscular activity is recorded through eight sEMG electrodes placed on the forearm. Results show that sEMG data, gathered before the hand is in contact with the object, can be used to obtain relevant information on object properties such as size and weight.

The Grasp Perturbator: Calibrating human grasp stiffness during a graded force task

In this paper we present a novel and simple handheld device for measuring in vivo human grasp impedance. The measurement method is based on a static identification method and intrinsic impedance is identified inbetween 25 ms. Using this device it is possbile to develop continuous grasp impedance measurement methods as it is an active research topic in physiology as well as in robotics, especially since nowadays (bio-inspired) robotics can be impedance-controlled. Potential applications of human impedance estimation range from impedance-controlled telesurgery to limb prosthetics and rehabilitation robotics. We validate the device through a physiological experiment in which the device is used to show a linear relationship between finger stiffness and grip force.

Model-free robot anomaly detection

Safety is one of the key issues in the use of robots, especially when human-robot interaction is targeted. Although unforeseen environment situations, such as collisions or unexpected user interaction, can be handled with specially tailored control algorithms, hard- or software failures typically lead to situations where too large torques are controlled, which cause an emergency state: hitting an end stop, exceeding a torque, and so on-which often halts the robot when it is too late. No sufficiently fast and reliable methods exist which can early detect faults in the abundance of sensor and controller data. This is especially difficult since, in most cases, no anomaly data are available. In this paper we introduce a new robot anomaly detection system (RADS) which can cope with abundant data in which no or very little anomaly information is present.

iEMG: Imaging electromyography

Advanced data analysis and visualization methodologies have played an important role in making surface electromyography both a valuable diagnostic methodology of neuromuscular disorders and a robust brain-machine interface, usable as a simple interface for prosthesis control, arm movement analysis, stiffness control, gait analysis, etc. But for diagnostic purposes, as well as for interfaces where the activation of single muscles is of interest, surface EMG suffers from severe crosstalk between deep and superficial muscle activation, making the reliable detection of the source of the signal, as well as reliable quantification of deeper muscle activation, prohibitively difficult. To address these issues we present a novel approach for processing surface electromyographic data. Our approach enables the reconstruction of 3D muscular activity location, making the depth of muscular activity directly visible. This is even possible when deep muscles are overlaid with superficial muscles, such as seen in the human forearm. The method, which we call imaging EMG (iEMG), is based on using the crosstalk between a sufficiently large number of surface electromyographic electrodes to reconstruct the 3D generating electrical potential distribution within a given area. Our results are validated by in vivo measurements of iEMG and ultrasound on the human forearm.