Bioinspiration in robotics deals with applying biological principles to the design of better performing devices. In this article, we propose a novel bioinspired framework using motor primitives for locomotion assistance through a wearable cooperative exoskeleton. In particular, the use of motor primitives for assisting different locomotion modes (i.e., ground-level walking at several cadences and ascending and descending stairs) is explored by means of two different strategies. In the first strategy, identified motor primitives are combined through weights to directly produce the desired assistive torque profiles. In the second strategy, identified motor primitives are combined to serve as neural stimulations to a virtual model of the musculoskeletal system, which, in turn, produces the desired assistive torque profiles.

An emerging approach to design locomotion assistive devices deals with reproducing desirable biological principles of human locomotion. In this paper, we present a bio-inspired controller for locomotion assistive devices based on the concept of motor primitives. The weighted combination of artificial primitives results in a set of virtual muscle stimulations. These stimulations then activate a virtual musculoskeletal model producing reference assistive torque profiles for different locomotion tasks (i.e. walking, ascending and descending stairs). The paper reports the validation of the controller through a set of experiments conducted with healthy participants. The proposed controller was tested for the first time with a unilateral leg exoskeleton assisting hip, knee, and ankle joints by delivering a fraction of the computed reference torques. Importantly, subjects performed a track involving ground-level walking, ascending and descending stairs, and several transitions between these tasks. These experiments highlighted the capability of the controller to provide relevant assistive torques and to effectively handle transitions between the tasks. Subjects displayed a natural interaction with the device. Moreover, they significantly decreased the time needed to complete the track when the assistance was provided, as compared to wearing the device with no assistance.

In this article, we present an online scalable tele-impedance framework, which enables the individual and collaborative control of multiple different robotic platforms. The framework provides an intuitive low-cost interface with visual feedback and a SpaceMouse, through which the operator can define the desired task-level trajectories and impedance profiles. With a simple mouse click, the user can switch between the robots and the collaborative operation mode. The control, subsequently, manages the distribution of the required parameters into the involved robots. Thanks to the introduced virtual hand concept, where each robot is defined as a finger, new robots can be easily added or removed via their kinodynamic parameters. The proposed framework was evaluated with three different experiments: a simulated auscultation on a mock-up patient, a cooperative task where a robot drives the patient on a wheelchair and a different robot performs the auscultation, and a collaborative task where two robots relocate a container. The results demonstrate the capabilities of the framework in terms of adaptability to different robotic platforms, the number of robots involved, and the task requirements. Additionally, quantitative and subjective analysis of 12 subjects showed how the developed interface, even in the presence of inaccurate visual feedback, allowed a smooth and accurate execution of the tasks.

This work presents a novel controller for robotic hands that regulates the grasp stiffness by manipulating the pose and the finger joint stiffness of hands with multiple degrees of freedom while guaranteeing the grasp stability. The proposed approach is inspired by the observations in human motor behaviour that reveal a coordinated pattern of stiffening among the hand fingers, along with a predictive selection of the hand pose to achieve a reliable grasp. The first adjusts the magnitude of the grasp stiffness, while the latter manipulates its overall geometry (shape). The realization of a similar control approach in robotic hands can result in a reduction of the software complexity and also promote a novel mechanical design approach, in which the finger stiffness profiles of the hand are adjusted by only one active component. The proposed control is validated with the fully actuated Allegro Hand, while trying to achieve pre-defined grasp stiffness profiles or modifications of an initial one.

Sit-to-stand and stand-to-sit transfers are fundamental daily motions that enable all other types of ambulation and gait. However, the ability to perform these motions can be severely impaired by different factors, such as the occurrence of a stroke, limiting the ability to engage in other daily activities. This study presents the recording and analysis of a comprehensive database of full body biomechanics and force data captured during sit-to-stand-to-sit movements in subjects who have and have not experienced stroke. These data were then used in conjunction with simple machine learning algorithms to predict vertical motion trajectories that could be further employed for the control of an assistive robot. A total of 30 people (including 6 with stroke) each performed 20 sit-to-stand-to-sit actions at two different seat heights, from which average trajectories were created. Weighted k-nearest neighbours and linear regression models were then used on two different sets of key participant parameters (height and weight, and BMI and age), to produce a predicted trajectory. Resulting trajectories matched the true ones for non-stroke subjects with an average $R^2$ score of $0.864\pm 0.134$ using k = 3 and 100% seat height when using height and weight parameters. Even among a small sample of stroke patients, balance and motion trends were noticed along with a large within-class variation, showing that larger scale trials need to be run to obtain significant results. The full dataset of sit-to-stand-to-sit actions for each user is made publicly available for further research.