Intelligent Machines for Autonomous Robotics and Manipulation: Combining Soft Robotics, Deep Learning and Human Inspiration

THE execution of reliable and stable grasping with artificial hands is a main challenge in the robotics field, due to its practical relevance and theoretical complexity. The classic approach used to grasp with rigid robotic hands generally favored object-centric analytical solutions. More specifically, a set of available contact points is hypothesized, while their position and contact forces are evaluated from the object knowledge [1]. Although very elegant and theoretically sound, this approach has not yet produced the desired outcomes in practice. To address these limitations, in soft artificial hands part of the control intelligence has been directly embedded in their mechanism, through the purposeful introduction of elastic elements and under-actuation patterns [2], [3]. Thanks to their intrinsic compliance, soft hands can mold around the external items and exploit their environment, thus multiplying their grasping capabilities, similarly to humans.

Several papers have shown that soft end-effectors can achieve high-level grasping performance when operated by humans (see e.g. [4], [5]). However, such level of dexterity is still unmatched in autonomous grasp execution. This represents an important challenge not only for industrial applications but also for advanced human-robot interaction. Indeed, as of today, robots are no more in their cages – where they originally acted in industrial pipelines for accomplishing precise yet repetitive tasks – but actively interact with humans as assistive devices or co-bots, facing action execution in unstructured environments. Under this regard, the compliance of the mechanical structure of soft systems and hands not only enables a safe interaction with humans, but it also allows a purposeful adaptation with respect to different object shapes, for manipulation purposes. However, classic approaches cannot be applied to this kind of hands, which - by their own nature - do not allow fingertips placement with the required precision and relative independence. On the contrary, data driven approaches could be the key to learn from humans how to manage soft hands, towards higher levels of autonomous grasping capabilities.