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Identification of Human Dynamics in User-Led Physical Human Robot Environment Interaction

Identifikation der menschlichen Dynamik in benutzergeführter physischer Mensch-Roboter-Umgebung Wechselwirkung
: Haninger, Kevin; Surdilovic, Dragoljub

Postprint urn:nbn:de:0011-n-5319737 (2.8 MByte PDF)
MD5 Fingerprint: 60dc0b7941ac61448cd03469a8c52b6f
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Created on: 6.2.2019

Cabibihan, J.-J. ; Institute of Electrical and Electronics Engineers -IEEE-; IEEE Robotics and Automation Society:
IEEE RO-MAN 2018, 27th IEEE International Symposium on Robot and Human Interactive Communication : Nanjing, China, August 27-31, 2018
Piscataway, NJ: IEEE, 2018
ISBN: 978-1-5386-7980-7
ISBN: 978-1-5386-7979-1
ISBN: 978-1-5386-7981-4
International Symposium on Robot and Human Interactive Communication (RO-MAN) <27, 2018, Nanjing>
Conference Paper, Electronic Publication
Fraunhofer IPK ()
human-robot interaction; model identification; impedance control

Human dynamic models are useful in design of physical human-robot and human-robot-environment interaction: informing choice of robot impedance, motivating relaxations to passivity-based safety constraints, and allowing online inference to user intent. Designing for performance objectives such as stable well-damped contact transitions also requires nominal models, but the use of human models in controller design is limited. Established approaches to identify human dynamics apply position or force perturbation and measure the corresponding response, mostly to validate neuromuscular hypotheses on motor control, which raises questions about their transferability to human-led collaboration. Here, human dynamics are identified in a task which closely resembles the final application, where the human leads the robot into contact with a (virtual) wall. This paper investigates the impact of human dynamics on coupled system behavior, and establishes a general framework for identification in human-led scenarios, making consideration of unmeasured human input. Experiments with different stiffness environments allow inference to human dynamics, and characterize the range of human dynamics which can be modulated by the user.