Robots were introduced in the field of upper limb neurorehabilitation to relieve the therapist from physical labor, and to provide high-intensity therapy to the patient. A variety of control methods were developed that incorporate patients' physiological and biomechanical states to adapt the provided assistance automatically. Higher level states, such as selected type of assistance, chosen task characteristics, defined session goals, and given patient impairments, are often neglected or modeled into tight requirements, low-dimensional study designs, and narrow inclusion criteria so that presented solutions cannot be transferred to other tasks, robotic devices or target groups. In this work, we present the design of a modular high-level control framework based on invariant states covering all decision layers in therapy. We verified the functionality of our framework on the assistance and task layer by outlaying the invariant states based on the characteristics of 20 examined state-of-the-art controllers. Then, we integrated four controllers on each layer and designed two algorithms that automatically selected suitable controllers. The framework was deployed on an arm rehabilitation robot and tested on one participant acting as a patient. We observed plausible system reactions to external changes by a second operator representing a therapist. We believe that this work will boost the development of novel controllers and selection algorithms in cooperative decision-making on layers other than assistance, and eases transferability and integration of existing solutions on lower layers into arbitrary robotic systems.

Exoskeleton robots found application in neurorehabilitation, telemanipulation, and power augmentation. The human–robot attachment system of an exoskeleton should transmit all the interaction forces while keeping the anatomical and robotic joint axes aligned. Existing attachment concepts were bounding the performance of modern exoskeletons due to insufficient stiffness for high-performance force control, time-consuming adaption processes, and/or bulkiness. Therefore, we developed an augmented attachment system for a recent fully actuated nine-degree-of-freedom upper limb exoskeleton. The proposed system was compared to a conventional solution in a case study with four participants. The proposed attachment system lowered the relative motion between the human and the robot under static loads for all defined landmarks by 45% on average. The occurrence of undesired contacts in the trials was mitigated by 74%, thus improving conditions for closed-loop force control. Furthermore, the proposed system adapted better to the user's anatomy facilitating more accurate alignment and less obstruction. On average, self-attachment took$\mathbf {43(8.3)}$$\mathrm{s}$to don(doff). Thereby, the alignment of anatomic landmarks had typically less than 15 mm offset to a thorough expert alignment, making self-attachment eligible. The augmented attachment system and the insights gained by the case study are expected to enable improvement of the physical human–robot interaction of exoskeletons.

We developed an exoskeleton for neurorehabilitation that covered all relevant degrees of freedom of the human arm while providing enough range of motion, speed, strength, and haptic-rendering function for therapy of severely affected (e.g., mobilization) and mildly affected patients (e.g., strength and speed). The ANYexo 2.0, uniting these capabilities, could be the vanguard for highly versatile therapeutic robotics applicable to a broad target group and an extensive range of exercises. Thereby, the practical adoption of these devices in clinics will be fostered. The unique kinematic structure of the robot and the bio-inspired controlled shoulder coupling allowed training for most activities of daily living. We demonstrated this capability with 15 sample activities, including interaction with real objects and the own body with the robot in transparent mode. The robot's joints can reach$200 \%$,$398 \%$, and$354 \%$of the speed required during activities of daily living at the shoulder, elbow, and wrist, respectively. Further, the robot can provide isometric strength training. We present a detailed analysis of the kinematic properties and propose algorithms for intuitive control implementation.

During robot-assisted therapy of hemiplegic patients, interaction with the patient must be intrinsically safe. Straight-forward collision avoidance solutions can provide this safety requirement with conservative margins. These margins heavily reduce the robot's workspace and make interaction with the patient's unguided body parts impossible. However, interaction with the own body is highly beneficial from a therapeutic point of view. We tackle this problem by combining haptic rendering techniques with classical computer vision methods. Our proposed solution consists of a pipeline that builds collision objects from point clouds in real-time and a controller that renders haptic interaction. The raw sensor data is processed to overcome noise and occlusion problems. Our proposed approach is validated on the 6 DoF exoskeleton ANYexo for direct impacts, sliding scenarios, and dynamic collision surfaces. The results show that this method has the potential to successfully prevent collisions and allow haptic interaction for highly dynamic environments. We believe that this work significantly adds to the usability of current exoskeletons by enabling virtual haptic interaction with the patient's body parts in human-robot therapy.

A high-quality free-motion rendering is one of the most vital traits to achieve an immersive human-robot interaction. Rendering free-motion is notably challenging for rehabilitation exoskeletons due to their relatively high weight and powerful actuators required for strength training and support. In the presence of dynamic human movements, accurate feedback linearization of the robot's dynamics is necessary to allow for a linear synthesis of interaction wrench controllers. Hence, we introduce a virtual model controller that uses two 6-DoF force sensors to control the interaction wrenches of a multi-DoF torque-controlled exoskeleton over the joint accelerations and inverse dynamics. Furthermore, we propose a disturbance observer for controlling the joint acceleration to diminish the influence of modeling errors on the inverse dynamics. To provide a high-bandwidth, low-bias estimation of the system's acceleration, we introduce a bias-observer which fuses the information from joint encoders and seven low priced IMUs. We have validated the performance of our proposed control structure on the shoulder and arm exoskeleton ANYexo. The experimental comparison of the controllers shows a reduction of the felt inertia and maximum reflected joint torque by a factor of more than three compared to state of the art. The controllers' robustness w.r.t. a model mismatch is validated. The experiments show that the closed-loop acceleration control improves the tracking, particularly at joints with low inertia. The proposed controllers' performance sets a new benchmark in haptic transparency for comparable devices and should be transferable to other applications.