Autonomy in Physical Human-Robot Interaction: A Brief Survey

Abstract

Sharing the control of a robotic system with an autonomous controller allows a human to reduce his/her cognitive and physical workload during the execution of a task. In recent years, the development of inference and learning techniques has widened the spectrum of applications of shared control (SC) approaches, leading to robotic systems that are capable of seamless adaptation of their autonomy level. In this perspective, shared autonomy (SA) can be defined as the design paradigm that enables this adapting behavior of the robotic system.This letter collects the latest results achieved by the research community in the field of SC and SA with special emphasis on physical human-robot interaction (pHRI). Architectures and methods developed for SC and SA are discussed throughout the paper, highlighting the key aspects of each methodology. A discussion about open issues concludes this letter.

Digital Twin for Human–Robot Interactions by Means of Industry 4.0 Enabling Technologies

“…This evolution has impacted robot programming methods as well. Furthermore, industrial deviations in manufacturing processes to incorporate hybrid teams have increased the need for more efficient interfaces [19].…”

Human-State-Aware Controller for a Tethered Aerial Robot Guiding a Human by Physical Interaction

“…2) Steady state solution under constant control input: To better characterize this phenomena, we consider the case in which the human is far from the desired position. In this condition, we saturate e R in (6) to avoid extreme forces applied to the human. It results that Γ(x) = γ where γ = [γ x γ y γ z ] ∈ R 3 is a constant value that depends on the imposed error saturation.…”

“…With the advances in manipulation capabilities of aerial robots, it is natural that the research community starts looking with interest at one of the most challenging types of physical interaction: the one with humans [6]. The highly complex dynamics of humans, the instability of aerial robots, and the strict requirement for safety, make physical Human-Aerial Robot Interaction (pHARI) an extremely hard to solve, and by far poorly addressed, research problem.…”

An adaptive admittance controller for collaborative drilling with a robot based on subtask classification via deep learning

“…This enables the collaborative robot to comply with the human by adapting the interaction controller, further reducing human effort and maximizing task efficiency [9]. Selvaggio et al [10] provide a survey of how different methods have been used for adaptive control in pHRI, through the concepts of shared control and shared autonomy, in order to maximize task efficiency in pHRI.…”