Online Motion Generation for Mirroring Human Arm Motion

Motion planning in robotics is a very large field of research. Many different approaches have been developed to create smooth trajectories for robot movement. For example there are optimization algorithms, which optimize kinematic or dynamic properties of a trajectory. Furthermore, nonlinear programming methods like e.g. optimal control, or polynomial based methods are widely used for trajectory generation. Most of these techniques are used to calculate a trajectory in advance, or they are limited to create point-to-point motions, where the robot needs to stop when switching to the next target point, especially, when interpolating in rotational space. In this paper, we combine a low-pass filter and spherical linear interpolation to realize a velocity-limited online trajectory generator for robot orientations in quaternion space. We use the developed motion generator for mirroring a human arm motion with a robot, recorded by a low frequency visual tracking. Using the proposed method, we can replicate the motion of the operator’s arm with very little delay and thereby achieve an easy-to-use interface. Furthermore, as we can strictly limit the velocity of the generated motion, the approach can safely be used in human robot collaboration applications

Incremental learning of EMG-based Control commands using Gaussian Processes

Myoelectric control is the process of controlling a prosthesis or an assistive robot by using electrical signals of the muscles. Pattern recognition in myoelectric control is a challenging field, since the underlying distribution of the signal is likely to change during the application. Covariate shifts, including changes of the arm position or different levels of muscular activation, often lead to significant instability of the control signal. This work tries to overcome These challenges by enhancing a myoelectric human machine interface through the use of the sparse Gaussian Process (sGP) approximation Variational Free Energy and by the introduction of a novel adaptive model based on an unsupervised incremental learning approach. The novel adaptive model integrates an interclass and intraclass distance to improve prediction stability under challenging conditions. Furthermore, it demonstrates the successful incorporation of incremental updates which is shown to lead to a significantly increased performance and higher stability of the predictions in an online user study

Electromyography for teleoperated tasks in weightlessness

The cooperation between robots and astronauts will become a core element of future space missions. This is accompanied by the demand for suitable input devices. An interface based on electromyography (EMG) represents a small, light and wearable device to generate a continuous 3D control signal from voluntarily muscle activation of the operator's arm. We analyzed the influence of microgravity on task performance during a 2D task on a screen. Six subjects performed aiming and tracking tasks in parabolic flights. Three different levels of fixation -- fixed feet using foot straps, semi-free by using a foot rail, and free-floating feet -- were tested to investigate how much user fixation is required to operate via the interface. The user study showed that weightlessness affects the usage of the interface only to a small extent. Success rates between 89% and 96% were reached within all conditions during microgravity. A significant effect between 0G and 1G could not be identified for the test series of fixed and semi-free feet, while free-floating feet showed significantly worse results in fine and gross motion times in 0G compared to ground tests (with success rates of 92% for 0G and 99% for 1G). Further adaptation to the altered proprioception may be needed here. Hence, foot rails as already mounted in the ISS would be sufficient to use the interface in weightlessness. Low impact of microgravity, high success rates, and an easy handling of the system, indicates a high potential of an EMG-based interface for teleoperation in space

A New Labeling Approach for Proportional Electromyographic Control

Different control strategies are available for human machine interfaces based on electromyography (EMG) to map voluntary muscle signals to control signals of a remote controlled device. Complex systems such as robots or multi-fingered hands require a natural commanding, which can be realized with proportional and simultaneous control schemes. Machine learning approaches and methods based on regression are often used to realize the desired functionality. Training procedures often include the tracking of visual stimuli on a screen or additional sensors, such as cameras or force sensors, to create labels for decoder calibration. In certain scenarios, where ground truth, such as additional sensor data, can not be measured, e.g., with people suffering from physical disabilities, these methods come with the challenge of generating appropriate labels. We introduce a new approach that uses the EMG-feature stream recorded during a simple training procedure to generate continuous labels. The method avoids synchronization mismatches in the labels and has no need for additional sensor data. Furthermore, we investigated the influence of the transient phase of the muscle contraction when using the new labeling approach. For this purpose, we performed a user study involving 10 subjects performing online 2D goal-reaching and tracking tasks on a screen. In total, five different labeling methods were tested, including three variations of the new approach as well as methods based on binary labels, which served as a baseline. Results of the evaluation showed that the introduced labeling approach in combination with the transient phase leads to a proportional command that is more accurate than using only binary labels. In summary, this work presents a new labeling approach for proportional EMG control without the need of a complex training procedure or additional sensors

Elastic Elements in a Wrist Prosthesis for Drumming Reduce Muscular Effort, but Increase Imprecision and Perceived Stress

Recently, progress has been made in the development of mechanical joints with variable intrinsic stiffness, opening up the search for application areas of such variable-stiffness joints. By varying the stiffness of its joints, the resonant frequency of a system can be tuned to perform cyclical tasks most energy-efficiently, making the variable-stiffness joint a candidate element for an advanced prosthetic device specifically designed for the cyclical task of drumming. A prerequisite for a successful variable-stiffness drumming prosthesis is the ability of human drummers to profitably employ different stiffness levels for playing different beats. In this pilot study, 29 able-bodied subjects (20 drumming novices and 9 experts) wear a cuff on the forearm, to which a drumstick is connected using changeable adapters, consisting of several leaf springs with different stiffness and one maximally stiff connection element. The subjects are asked to play simple regular drum beats at different frequencies, one of which is the resonant frequency of the adapter-drumstick system. The subject's performance of each drumming task is rated in terms of accuracy and precision, and the effort is measured using questionnaires for the perceived stress as well as electromyography (EMG) for the muscular activity. The experiments show that using springs instead of the stiff connection leads to lower muscular activity, indicating that humans are able to use the energy-storing capabilities of the springs, or that muscular activity is reduced due to the lower mass of the springs. However, the perceived stress is increased and the novices' performance lowered, possibly due to a higher cerebral load for controlling the elastic system. The hypothesis that "matching the resonant frequency of the spring-drumstick system to the desired frequency leads to better performance and lower effort" is not confirmed. Possible explanations are discussed. In conclusion, a series-elastic element appears to lower the muscular effort of drumming, while a stiff connection appears to minimize the mental load and has a positive effect on the performance of drumming novices

CATs: Task Planning for Shared Control of Assistive Robots with Variable Autonomy

Abstract: From robotic space assistance to healthcare robotics, there is increasing interest in robots that offer adaptable levels of autonomy. In this paper, we propose an action representation and planning framework that is able to generate plans that can be executed with both shared control and supervised autonomy, even switching between them during task execution. The action representation -- Constraint Action Templates (CATs) -- combine the advantages of Action Templates (Leidner, 2019) and Shared Control Templates (Quere, 2020). We demonstrate that CATs enable our planning framework to generate goal-directed plans for variations of a typical task of daily living, and that users can execute them on the wheelchair-robot EDAN in shared control or in autonomous mode

Feasibility Checks for Safe Shared Control with Variable Autonomy in Assistive Robotics

In robotics, especially on systems that interact with humans, there is an increasing need for adaptable levels of autonomy. These include direct control, shared control, supervised autonomy, and full autonomy. A concrete example of this is the wheelchair-based robot EDAN, where a user may want to initiate the opening of a door in shared control, but then let the system autonomously cross the doorway. It is essential that robot guidance in shared control is robust and helps the human to prevent errors, rather than causing them by guiding the user towards solutions that are ultimately infeasible due to obstacles or limited manipulability. In this letter, we describe a proof-of-concept for fast and iteratively-refined feasibility checks in the context of EDAN. We conduct exploratory experiments in a grasping action with obstacles

Shared Control Templates for Assistive Robotics

Light-weight robotic manipulators can be used to restore the manipulation capability of people with a motor disability. However, manipulating the environment poses a complex task, especially when the control interface is of low bandwidth, as may be the case for users with impairments. Therefore, we propose a constraint-based shared control scheme to define skills which provide support during task execution. This is achieved by representing a skill as a sequence of states, with specific user command mappings and different sets of constraints being applied in each state. New skills are defined by combining different types of constraints and conditions for state transitions, in a human-readable format. We demonstrate its versatility in a pilot experiment with three activities of daily living. Results show that even complex, high-dimensional tasks can be performed with a low-dimensional interface using our shared control approach