Bioinspired multiscale adaptive suction on complex dry surfaces enhanced by regulated water secretion.

Suction is a highly evolved biological adhesion strategy for soft-body organisms to achieve strong grasping on various objects. Biological suckers can adaptively attach to dry complex surfaces such as rocks and shells, which are extremely challenging for current artificial suction cups. Although the adaptive suction of biological suckers is believed to be the result of their soft body's mechanical deformation, some studies imply that in-sucker mucus secretion may be another critical factor in helping attach to complex surfaces, thanks to its high viscosity. Inspired by the combined action of biological suckers' soft bodies and mucus secretion, we propose a multiscale suction mechanism which successfully achieves strong adaptive suction on dry complex surfaces which are both highly curved and rough, such as a stone. The proposed multiscale suction mechanism is an organic combination of mechanical conformation and regulated water seal. Multilayer soft materials first generate a rough mechanical conformation to the substrate, reducing leaking apertures to micrometres (~10 µm). The remaining micron-sized apertures are then sealed by regulated water secretion from an artificial fluidic system based on the physical model, thereby the suction cup achieves long suction longevity on complex surfaces but minimal overflow. We discuss its physical principles and demonstrate its practical application as a robotic gripper on a wide range of complex dry surfaces. We believe the presented multiscale adaptive suction mechanism is a powerful unique adaptive suction strategy which may be instrumental in the development of versatile soft adhesion.

A Framework for Composite Layup Skill Learning and Generalizing Through Teleoperation.

In this article, an impedance control-based framework for human-robot composite layup skill transfer was developed, and the human-in-the-loop mechanism was investigated to achieve human-robot skill transfer. Although there are some works on human-robot skill transfer, it is still difficult to transfer the manipulation skill to robots through teleoperation efficiently and intuitively. In this article, we developed an impedance-based control architecture of telemanipulation in task space for the human-robot skill transfer through teleoperation. This framework not only achieves human-robot skill transfer but also provides a solution to human-robot collaboration through teleoperation. The variable impedance control system enables the compliant interaction between the robot and the environment, smooth transition between different stages. Dynamic movement primitives based learning from demonstration (LfD) is employed to model the human manipulation skills, and the learned skill can be generalized to different tasks and environments, such as the different shapes of components and different orientations of components. The performance of the proposed approach is evaluated on a 7 DoF Franka Panda through the robot-assisted composite layup on different shapes and orientations of the components.