The phenomenon of the “traveling wave,” commonly observed in various organisms, involves a wave that propagates along the body, serving as a locomotion mechanism. Particularly, in aquatic environments, organisms such as fish and cetaceans utilize traveling waves to propel themselves through water, minimizing fluid drag and maximizing movement efficiency. Inspired by nature, robotics has extensively explored replicating such locomotion strategies. This work presents a fish robot with an innovative magnetic transmission system. The mechanism transforms the unidirectional rotation of a single motor into an oscillatory, phase-shifted movement across the modules of the kinematic chain, generating a traveling wave along the body. The robot's design and functionality are detailed, highlighting advancements in bio-inspired robotics for underwater applications, such as efficient and non-invasive monitoring and exploration of marine ecosystems. The fish robot achieved a swimming speed of approximately 2 body lengths per second (BL/s) with a tail-beat frequency of 3.24 Hz and a minimum Cost of Transport (CoT) of$5.33 ~\mathrm{J} /(\text{kg} \cdot \mathrm{m})$. Biomimetic robotics can play a key role in sustainable aquafarming, biodiversity conservation, and animal-robot interaction research, offering the potential to minimize ecosystem disruption and advance marine science.

Biomimetic robotics can help support underwater exploration and monitoring while minimizing ecosystem distur-bance. It also has potential applications in sustainable aqua-farming management, biodiversity preservation, and animal-robot interaction studies. This study proposes a bio-inspired control strategy for an underactuated robotic fish, which utilizes a single DC motor to drive a mechanism that converts the motor's oscillating motion into an oscillatory motion of the robotic fishtail through a magnetic coupling and a wire-driven system. The proposed control strategy for the robotic fish is based on central pattern generators (CPGs) and incorporates proprioceptive sensory feedback. The torque exerted on the fishtail is adjusted based on its position, allowing for increased or decreased body speed and steering with different angular speeds and radii of curvature despite the underactuated design. The robotic fish can vary the swimming speed of 0.08 body lengths per second (BL/s) with a related change in the tail-beating frequency up to 2.3 Hz, and it can vary the steering angular speed in the range of 0.08 rad/s with a relative change in the curvature radius of 0.25 m. The controller can adapt to changes in tail structure, weight, or the surrounding environment based on the proprioceptive feedback. Design changes to the modular design can improve speed and steering performances, maintaining the control strategy developed.

Recently, the avenue of adaptable, soft robotic hands has opened simplified opportunities to grasp different items; however, the potential of soft end effectors (SEEs) is still largely unexplored, especially in human-robot interaction. In this paper, we propose, for the first time, a simple touch-based approach to endow a SEE with autonomous grasp sensory-motor primitives, in response to an item passed to the robot by a human (human-to-robot handover). We capitalize on human inspiration and minimalistic sensing, while hand adaptability is exploited to generalize grasp response to different objects. We consider the Pisa/IIT SoftHand (SH), an under-actuated soft anthropomorphic robotic hand, which is mounted on a robotic arm and equipped with Inertial Measurement Units (IMUs) on the fingertips. These sensors detect the accelerations arisen from contact with external items. In response to a contact, the hand pose and closure are planned for grasping, by executing arm motions with hand closure commands. We generate these motions from human wrist poses acquired from a human maneuvering the SH to grasp an object from a table. We obtained 86% of successful grasps, considering many objects passed to the SH in different manners. We also tested our techniques in preliminary experiments, where the robot moved to autonomously grasp objects from a surface. Results are positive and open interesting perspectives for soft robotic manipulation.

Sensing contact forces can be a key enabler for higher order dexterous manipulation in robotic hands. To sense the full range of contact pressure distribution would provide the best solution, but it is in practice unfeasible when considering very deformable and adaptable hands. This paper proposes an approach to estimate the contact forces acting on an under-actuated adaptable hand by combining the compliance model of the hand with the geometric configuration of the hand itself. This is done by introducing reasonable assumptions about the net contact force on each phalanx. The proposed method is introduced and experimentally validated on two fingers of the Pisa/IIT SoftHand.

In this paper, we present a method to reconstruct the configurations of kinematic trees of rigid bodies not using measurements of relative angles (such as, e.g. rotary encoders at joints) but absolute posture sensors (such as IMUs) along with suitable filter algorithms. We argue that the relatively larger inaccuracies shown by absolute sensors can be compensated by suitable processing, such as a passive complementary filters exploiting the Mahony-Hamel formulation. The proposed method is applicable to systems where measurements of relative angles is not feasible or convenient, or where the joint kinematics are not lower pairs: for example, human body parts or soft robotic devices. In the paper, we make explicit reference to the reconstruction of posture of the compliant, underactuated Pisa/IIT SoftHand. Quantitative comparisons with ground truth data in grasping tests are used to validate the proposed method. The resulting hardware design is mechanically robust, cheap and can be easily adapted to robotic hands with different structures, as well as to sensorizing gloves for studying human grasping strategies.