Shared control enables users with motor impairments to control high-dimensional assistive robots with low-dimensional user interfaces. The challenge is to simultaneously 1) provide support for completing daily living tasks 2) enable sufficient freedom of movement to foster user empowerment 3) ensure that shared-control support precludes the robot from running into kinematic limitations such as obstacles, unreachable areas, or loss of manipulability due to joint limits. In this letter, we propose a framework that performs feasibility checks before executing a shared-control task. We activate shared control only if a task is deemed feasible, and refine the task regions by excluding paths that are infeasible, e.g. due to obstacles or kinematic limitations. This reduces task failures, whilst still ensuring freedom of movement. We evaluate our framework on a set of daily living tasks with our wheelchair-based mobile manipulator EDAN.

As labor shortage increases in the health sector, the demand for assistive robotics grows. However, the needed test data to develop those robots is scarce, especially for the application of active 3D object detection, where no real data exists at all. This short paper counters this by introducing such an annotated dataset of real environments. The captured environments represent areas which are already in use in the field of robotic health care research. We further provide ground truth data within one room, for assessing SLAM algorithms running directly on a health care robot.

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 [1] and Shared Control Templates [2]. 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.

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.

Controlling a robotic arm to achieve manipulation tasks is challenging for humans. Especially if only low-dimensional input signals can be provided, as is often the case for users with motor impairments. Using shared control to provide task-specific guidance and constraints facilitates control – for instance with the Shared Control Templates (SCT) framework – and enables even complex activities of daily living to be performed successfully. However, designing SCTs is a laborious task requiring robotic expertise. To make such design easier and faster, we propose a method for semi-automatically designing SCTs on the basis of demonstrations. Furthermore, we propose two similarity metrics, and demonstrate how these can be used to transfer knowledge from one SCT to another. We demonstrate that the SCTs so acquired can be successfully used in shared control for everyday tasks such as opening a drawer or a cupboard on our assistive robot EDAN.

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 three-dimensional (3D) control signal from voluntarily muscle activation of the operator's arm. We analyzed the influence of microgravity on task performance during a two-dimensional (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—are 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${\%}$ are reached within all conditions during microgravity. A significant effect between 0 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 International Space Station (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.

Demographic change and its various implications will offer some of the biggest challenges faced by society and our health-care systems in the coming decades. While the number of people in need of caregiving is steadily growing in most industrial nations, the number of caregivers is not keeping up with this increasing demand. Robotic assistance systems have the potential to mitigate this problem an...

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.

Light-weight robotic manipulators in combination with power wheelchairs can help to restore the mobility of people with disabilities. While such systems are available on the market, they typically are limited to fully manual control modes. In research, shared control methods are employed, to increase the usability of these systems. Here, we present an additional extension, by introducing a whole-body control concept to the assistive robotic system EDAN. Combined with shared control, the whole-body controller allows the realization of complex tasks which necessitate the coordination of arm and platform, while ensuring compliant behavior resulting from the impedance control law. The implemented approach is analyzed and validated in an exemplary task of opening a door, passing through it and closing it afterwards. While this task would exceed the reachability of the arm in a classical approach, the combination of whole-body control with a shared control scheme allows for quick and efficient execution.

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.

Neuromuscular diseases, stroke, or trauma can lead to a reduced neural function which severely inhibits limb functionality. If the disease is strongly advanced, people can’t manage their daily life independently and become reliant on 24-hour care. In this situation, assistive technology, like a robotic manipulator mounted on a wheelchair, can provide help and relief. However, control of such a device is usually achieved with a joystick, which requires to have remaining functionality in hand and finger movement. This prevents many people with tetraplegia from efficient use of such assistive technology. An alternative to the joystick, is given by Brain-Computer Interfaces. It has been shown that noninvasive interfaces like Electroencephalography can be used to achieve control over low-dimensional devices like power wheelchairs [3]. More complex tasks like control of assistive robotic devices have been demonstrated with invasive interfaces, like the BrainGate Neural Interface System [1]. We investigate the use of surface Electromyography (EMG) as an interface for assistive robotic devices. It is a comparably cheap and easy to apply technology. We could show that people with tetraplegia due to a severe Spinal Muscular Atrophy (SMA), can still achieve control over a robotic manipulator (e.g. drinking from a bottle) by recording remaining muscular activity [4]. To investigate the use of EMG as an interface to assistive technology, we developed the research platform EDAN (EMG-controlled Daily Assistant). It consists of a robotic manipulator mounted on a state of the art power wheelchair. We use a torque controlled robotic arm (DLR-LWR 3), which is well suited for safe physical interaction with humans and the environment. The five-finger hand mounted on the robotic arm allows for stable grasping of a variety of everydayobjects. The focus of our research is twofold. On the one hand, we investigate the use of EMG as a non-invasive interface to provide people with control over assistive systems. On the other hand, we develop methods to simplify the usage of such systems with the support of artificial intelligence. Manual control of robotic manipulators is rather slow and cumbersome, especially when controlled with a noisy interface like a BCI. Artificial intelligence can help to significantly improve the usability of such systems. A shared control ap-

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.

The development of new, light robotic systems has opened up a wealth of human-robot interaction applications. In particular, the use of robot manipulators as personal assistant for the disabled is realistic and affordable, but still requires research as to the brain-computer interface. Based on our previous work with tetraplegic individuals, we investigate the use of low-cost yet stable surface Electromyography (sEMG) interfaces for individuals with Spinal Muscular Atrophy (SMA), a disease leading to the death of neuronal cells in the anterior horn of the spinal cord; with sEMG, we can record remaining active muscle fibers. We show the ability of two individuals with SMA to actively control a robot in 3.5D continuously decoded through sEMG after a few minutes of training, allowing them to regain some independence in daily life. Although movement is not nearly as fast as natural, unimpaired movement, reach and grasp success rates are near 100% after 50s of movement.

Two people with long-standing tetraplegia use neural interface system-based control of a robotic arm to perform three-dimensional reach and grasp movements. John Donoghue and colleagues have previously demonstrated that people with tetraplegia can learn to use neural signals from the motor cortex to control a computer cursor. Work from another lab has also shown that monkeys can learn to use such signals to feed themselves with a robotic arm. Now, Donoghue and colleagues have advanced the technology to a level at which two people with long-standing paralysis — a 58-year-old woman and a 66-year-old man — are able to use a neural interface to direct a robotic arm to reach for and grasp objects. One subject was able to learn to pick up and drink from a bottle using a device implanted 5 years earlier, demonstrating not only that subjects can use the brain–machine interface, but also that it has potential longevity. Paralysis following spinal cord injury, brainstem stroke, amyotrophic lateral sclerosis and other disorders can disconnect the brain from the body, eliminating the ability to perform volitional movements. A neural interface system1,2,3,4,5 could restore mobility and independence for people with paralysis by translating neuronal activity directly into control signals for assistive devices. We have previously shown that people with long-standing tetraplegia can use a neural interface system to move and click a computer cursor and to control physical devices6,7,8. Able-bodied monkeys have used a neural interface system to control a robotic arm9, but it is unknown whether people with profound upper extremity paralysis or limb loss could use cortical neuronal ensemble signals to direct useful arm actions. Here we demonstrate the ability of two people with long-standing tetraplegia to use neural interface system-based control of a robotic arm to perform three-dimensional reach and grasp movements. Participants controlled the arm and hand over a broad space without explicit training, using signals decoded from a small, local population of motor cortex (MI) neurons recorded from a 96-channel microelectrode array. One of the study participants, implanted with the sensor 5 years earlier, also used a robotic arm to drink coffee from a bottle. Although robotic reach and grasp actions were not as fast or accurate as those of an able-bodied person, our results demonstrate the feasibility for people with tetraplegia, years after injury to the central nervous system, to recreate useful multidimensional control of complex devices directly from a small sample of neural signals.

In this paper we describe and practically demonstrate a robotic arm/hand system that is controlled in real-time in 6D Cartesian space through measured human muscular activity. The soft-robotics control architecture of the robotic system ensures safe physical human robot interaction as well as stable behaviour while operating in an unstructured environment. Muscular control is realised via surface electromyography, a non-invasive and simple way to gather human muscular activity from the skin. A standard supervised machine learning system is used to create a map from muscle activity to hand position, orientation and grasping force which then can be evaluated in real time - the existence of such a map is guaranteed by gravity compensation and low-speed movement. No kinematic or dynamic model of the human arm is necessary, which makes the system quickly adaptable to anyone. Numerical validation shows that the system achieves good movement precision. Live evaluation and demonstration of the system during a robotic trade fair is reported and confirms the validity of the approach, which has potential applications in muscle-disorder rehabilitation or in teleoperation where a close-range, safe master/slave interaction is required, and/or when optical/magnetic position tracking cannot be enforced.

In this overview paper we present current work on safety analysis for physical Human-Robot Interaction (pHRI) and motion control methods for robotic co-workers. In particular, we introduce the analysis tools for investigating the potential injury a human would suffer during robot-human impacts. Furthermore, we outline our concept for establishing a procedure towards standardized crash testing in robotics with automobile crash-test dummies. Since it is only possible to investigate blunt impacts with these devices, we developed a drop testing setup for analyzing soft-tissue injury in robotics from a biomechanics perspective. In the second part of the paper, some of our methods for task preserving and task relaxing motion schemes are described, which enable collision avoidance in real-time. The algorithms are well suited to work in an integrated fashion with the soft robotics control developed for the DLR Lightweight Robot III (LWR-III). In addition, it is shown how the torque sensing capabilities of the robot can be used to support reactive motion schemes. Finally, an overview of our human-friendly control architecture for the LWR-III is given, which unifies the rich bundle of developed methods for this manipulator.

We have investigated control of the DLR Light-Weight Robot III with DLR Five-Finger Hand by a person with tetraplegia using the BrainGate2 Neural Interface System. The goal of this research is to develop assistive technologies for people with severe physical disabilities. A BrainGate-enabled DLR LWR III would potentially permit a person with tetraplegia to gain improved control over their environment, e.g. to drink a glass of water. First results of the developed control loop are very encouraging and allow the participant to perform simple interaction tasks with her environment, e.g., pick up a bottle and move it around. To this end, only a few minutes of system training are required, after which the system can be used.