Physical Human-Robot Interaction (pHRI) is of great interest in robotics. Currently, we have a good working knowledge of our engineered robots, but we still lack a deep understanding of human behavior, and how to effectively use it for real-time reactive robot behavior in the context of pHRI, e.g., rehabilitation robotics. This understanding is crucial to evolve the pHRI paradigms into safer and more effective human-robot interactions. Therefore, progress and innovations in human modeling are necessary to significantly improve how humans physically collaborate with exoskeletons, prostheses, Cobots, and other robotic devices.

The fields of pHRI and human modeling have produced impressive research progress in recent decades. However, there has been limited interconnection between these two fields, despite their obvious benefits to each other, e.g., human models offer insights into the human biomechanical responses to robot actions while pHRI provides new ways of conducting biomechanical experiments. This workshop aims to stimulate discussion and promote collaboration between the communities of robotics and biomechanics. The latest high-quality research results from both biomechanists and roboticists will be presented by invited speakers, modern human modeling techniques and tools will be demonstrated, and the state-of-the-art and future directions of human modeling and pHRI will be discussed.

Research in human modeling and physical human-robot interaction (pHRI) share a common goal: to understand and improve human function, from reducing injury risk to facilitating rehabilitation. However, we are still challenged in restoring or enhancing human movement robotically, using concepts of human-robot interaction. The main reason is that we have a limited understanding (and prediction capacity) of how the human body responds when interacting physically with robots. That is, we still cannot accurately predict how the neuro-muscular system responds when connected (in series or in parallel) with robotic prostheses, exoskeletons or when collaborating with Cobots. To address this challenge, it is essential to stimulate the scientific community toward the development of accurate, predictive and real-time models that can estimate human responses to interacting robots.

The fields of pHRI and human modeling have produced impressive research progress in recent decades. However, there has been limited interconnection and cross-pollination between these two fields, despite their obvious benefits to each other. Human models offer unique insights into the human biomechanical responses to robot actions, yet only recently has pHRI begun to integrate human models to provide real-time feedback, as opposed to offline planning based on normative responses. An example is a recent series of IROS workshops on “Ergonomic human-robot collaboration” organized by Peternel, et al. where the focus was limited to the Cobots that use human body dynamics models for optimizing workplace ergonomics. On the other hand, only recently have studies of human models started to implement pHRI methods as a tool that can provide a highly standardized testing setup and improved quantitative results. An example is the IROS workshop on “Bio-electrical signals For Motor Control in Robotics” organized by Sartori, et al.. Thus, now is an ideal time to push the state of the art in both fields in a direction of extensive interconnection and cross-pollination.

The objective of this workshop is to stimulate and facilitate the interconnection and cross-pollination between the fields of human modeling and pHRI. We aim to focus on four main pillars:

1. Online predictive control of robots based on human models in the control loop

2. Informing human modeling based on measures and feedback from pHRI experiments

3. Real-time human models for enhanced and optimized pHRI

4. Fusion of data-driven and neuromechanical modeling approaches to generalize to different tasks and personalize to different individuals

These pillars are essential for effective robotics applications like exoskeletons, prostheses, neurorehabilitation, and physiotherapy in the context of healthcare, and human-robot collaboration in the context of Industry 4.0.

During the workshop, we will review the progress of the research and development in the fields of human modeling and pHRI. Through the interactive discussion, we will identify potential gaps in the knowledge and opportunities for the interconnection of human modeling and pHRI. To do so, we gathered a diverse ensemble of organizers and speakers, who are leading experts in their respective areas that are highly relevant to this workshop topic.

The workshop topics of interest are described by the following keywords:

• Human musculoskeletal modeling

• Human neuromechanical modeling

• Human sensorimotor control modeling

• Data-driven human modeling

• Human movement optimization

• Online model predictive control

• Mutual human-robot adaptation

• Model-informed robotic rehabilitation

• Model-informed human-robot collaboration

This workshop is closely related to our successfully organized special issue of the same topic on the IEEE Robotics and Automation Letters (RA-L) (https://www.ieee-ras.org/publications/ra-l/special-issues/current-special-issues/cfp-innovations-and-applications-of-human-modelling-in-physical-human-robot-interaction). As part of the special issue, a survey paper on this topic can be found here, https://ieeexplore.ieee.org/document/ 10185034 .

The following IEEE RAS Technical Committees confirmed their endorsement for the proposed workshop via support letters which are attached at the end of this proposal:

• IEEE RAS Technical Committee for Bio Robotics

• IEEE RAS Technical Committee for Human-Robot Interaction & Coordination

• IEEE RAS Technical Committee for Human Movement Understanding

• IEEE RAS Technical Committee for Wearable Robotics

For questions or additional information, please contact one of the organizers:

• Cheng Fang: [email protected]

• Luka Peternel: [email protected]

• Ajay Seth: [email protected]

• Massimo Sartori: [email protected]

• Yanan Li: [email protected]

• Pauline Maurice: [email protected]

• Eiichi Yoshida: [email protected]