Model predictive impedance control with Gaussian processes for human and environment interaction

The advancement and development of human modeling have greatly benefited from principles used in robotics, for instance, multibody dynamics laid the foundations for physics engines of human movement simulation, and the robotics and control theory were used to contextualize human sensorimotor control. There are many common interests and interconnections between the fields of human modeling and robotics. In recent years, as robots have become safer and smarter, they actively participate in our lives and help us in various scenarios. Roboticists need tools and data from human modeling to build next-generation robots that better assist humans. In this survey, we focus on the connections between physical human-robot interaction and human modeling. On one hand, human neuromusculoskeletal and sensorimotor control models provide novel insights into the human response that robots can utilize to improve human performance. On the other hand, robots are becoming instrumental in quantifying the performance of the (neuro)musculoskeletal system. Thus, the combined use of human modeling and robotic methods in physical human-robot interaction can lead to both improved human understanding and functional assistance.

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Model predictive impedance control with Gaussian processes for human and environment interaction

Cited by 4 publications

References 71 publications

Safe tracking control for free-flying space robots via control barrier functions

Safe tracking control for free-flying space robots via control barrier functions

Semi-autonomous Teleimpedance Based on Visual Detection of Object Geometry and Material and its Relation to Environment

Human Modeling in Physical Human-Robot Interaction: A Brief Survey

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