The path towards contact-based physical human-robot interaction

TL;DR

This survey reviews contact-based physical human-robot interaction, emphasizing perception, planning, control, and data-driven techniques like reinforcement learning to improve safety and reliability in various applications.

Contribution

It provides a comprehensive overview of contact-based pHRI, highlighting recent developments, challenges, and future directions, especially in data-driven approaches and safety considerations.

Findings

Data-driven techniques like reinforcement learning are increasingly effective.

Safety and human intention understanding are central to contact-based pHRI.

The field is in early stages, guiding future research directions.

Abstract

With the advancements in human-robot interaction (HRI), robots are now capable of operating in close proximity and engaging in physical interactions with humans (pHRI). Likewise, contact-based pHRI is becoming increasingly common as robots are equipped with a range of sensors to perceive human motions. Despite the presence of surveys exploring various aspects of HRI and pHRI, there is presently a gap in comprehensive studies that collect, organize and relate developments across all aspects of contact-based pHRI. It has become challenging to gain a comprehensive understanding of the current state of the field, thoroughly analyze the aspects that have been covered, and identify areas needing further attention. Hence, the present survey. While it includes key developments in pHRI, a particular focus is placed on contact-based interaction, which has numerous applications in industrial, rehabilitation and medical robotics. Across the literature, a common denominator is the importance to establish a safe, compliant and human intention-oriented interaction. This endeavour encompasses aspects of perception, planning and control, and how they work together to enhance safety and reliability. Notably, the survey highlights the application of data-driven techniques: backed by a growing body of literature demonstrating their effectiveness, approaches like reinforcement learning and learning from demonstration have become key to improving robot perception and decision-making within complex and uncertain pHRI scenarios. As the field is yet in its early stage, these observations may help guide future developments and steer research towards the responsible integration of physically interactive robots into workplaces, public spaces, and elements of private life.