Robots with intrinsic joint elasticity can perform highly dynamic manoeuvres by leveraging energy storage and release, enabling explosive motions such as throwing. By augmenting elastic robots with clutch mechanisms, link decoupling can be used to fully exploit inertial coupling effects and gravitational acceleration in motion while effectively circumventing spring deflection limits. However, braking such systems in a decoupled state presents a challenge, as re-engaging the link risks damaging the joint. While optimal control strategies could be applied, they are not inherently safe due to model uncertainties. To address this, we propose a feedback-based two-stage method that coordinates the transition through the hybrid modes of the system. These modes are characterized by underactuated and actuated dynamics. First, a decoupled link is braked via inertial coupling until a safe velocity for clutching is reached, after which the link is re-coupled and actively braked. We demonstrate the effectiveness of this method through simulations comparing it with optimal control and validate it experimentally using a physical prototype.

Intrinsically elastic robots surpass their rigid counterparts in a range of different characteristics. By temporarily storing potential energy and subsequently converting it to kinetic energy, elastic robots are capable of highly dynamic motions even with limited motor power. However, the time-dependency of this energy storage and release mechanism remains one of the major challenges in controlling elastic robots. A possible remedy is the introduction of locking elements (i.e. clutches and brakes) in the drive train. This gives rise to a new class of robots, so-called clutched-elastic robots (CER), with which it is possible to precisely control the energy-transfer timing. A prevalent challenge in the realm of CERs is the automatic discovery of clutch sequences. Due to complexity, many methods still rely on pre-defined modes. In this paper, we introduce a novel contact-implicit scheme designed to optimize both control input and clutch sequence simultaneously. A penalty in the objective function ensures the prevention of unnecessary clutch transitions. We empirically demonstrate the effectiveness of our proposed method on a double pendulum equipped with two of our newly proposed clutch-based Bi-Stiffness Actuators (BSA).

This study addresses the absence of an identification framework to quantify a comprehensive dynamic model of human and anthropomorphic tendon-driven fingers, which is necessary to investigate the physiological properties of human fingers and improve the control of robotic hands. First, a generalized dynamic model was formulated, which takes into account the inherent properties of such a mechanical system. This includes rigid-body dynamics, coupling matrix, joint viscoelasticity, and tendon friction. Then, we propose a methodology comprising a series of experiments, for step-wise identification and validation of this dynamic model. Moreover, an experimental setup was designed and constructed that features actuation modules and peripheral sensors to facilitate the identification process. To verify the proposed methodology, a 3D-printed robotic finger based on the index finger design of the Dexmart hand was developed, and the proposed experiments were executed to identify and validate its dynamic model. This study could be extended to explore the identification of cadaver hands, aiming for a consistent dataset from a single cadaver specimen to improve the development of musculoskeletal hand models.

Recent advancements in artificial hand development have primarily concentrated on enhancing adaptive grasping, dexterity, as well as the integration of biomimetic skin. However, few designs have successfully combined lightweight, cost-effective solutions, and tactile sensing along with adaptive grasping in a human-sized prototype. We propose, an open-source, highly integrated artificial hand. It leverages a compliant linkage mechanism for versatile grasping capabilities, featuring six degrees of actuation and MEMS-based tactile sensors on every fingertip.

Mobile manipulators (MM) have proven valuable in assisting humans in industrial settings. However, their strict separation from humans in controlled environments limits their effectiveness. Efforts have been made to bridge this gap for physical human-robot interaction (pHRI), leading to the development of collaborative mobile manipulators (CMM). Nonetheless, unpredictable environments continue to present challenges. This paper introduces an innovative suspension design for mobile bases (MBs) to enhance the safety and autonomy of CMMs. We propose an electromechanical approach leveraging variable stiffness and combining passive springs with adaptive transmission mechanisms. Through simulation, physical prototype development, and experimental validation, we demonstrate the effectiveness of our approach in stabilizing the MB against external disturbances. Our findings provide valuable insights for the development of CMMs in dynamic environments.

Upper limb prostheses are commonly mounted to the human residual limb by a passive socket. By this design, the sensitive residual limb is exposed to high reaction wrenches, which can be a source of medical complications. In this article, we introduce an active force-sensitive robotic socket, which carries the prosthesis, offloads the residual limb, and allows guidance via small interaction forces at the same time. We investigate the feasibility of this concept by a force-sensitive and wearable shoulder exoskeleton, calledexoprosthesiswhen being combined with a prosthesis. We provide a first mechatronics prototype, two floating base controllers, and an analysis of the loads acting on the user body. Simulations and experiments confirm the concept and reveal that the wrench at residual limb can be fully compensated for the static case and by$\approx \text{50}\%$for the investigated motions. Human-in-the-loop tests are successfully performed by three able-bodied users showing the later real-world use case in a complex grasping situation. Overall, we believe that a force-sensitive robotic socket has the potential to advance prosthetics to a new level as it provides an intuitive and seamless user control interface.

In modern highly dynamic robot manipulation, collisions between a robot and objects may be intentionally executed to improve performance. To distinguish between these deliberate contacts and accidental collisions beyond the limit of state-of-the-art human-robot interactions, new sensing approaches are required. This work seeks an easy-to-implement and real-time capable solution to detect the identity of the impacted material. We developed an inertial measurement unit (IMU) based setup that records vibration signals occurring after collisions. Furthermore, a data-set was generated in an unsupervised learning manner using the measurements of collision experiments with several materials commonly used in realistic applications. The data-set was used to train an artificial neural network to classify the type of material involved. Our results show that the neural net detects collisions and a detailed distinction between materials is achieved, even with estimating different human body parts. The unsupervised data-set generation allows for a simple integration of new classes, which provides broader applicability of our approach. As the calculations are running faster than the control cycle of the robot, the output of our classifier can be used in real-time to decide about the robots reaction behavior.