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arXiv Robotics — research abstracts· Adnan Takriti, Mahmoud Sadeq, Quentin Rouxel, Daniele Cafolla, Michael Mistry, Carlo Tiseo·· 4 days agoSignalEditorial score75

A Unified Fractal Impedance Control Framework

A Unified Fractal Impedance Control Framework

Summary

This paper introduces the robotic implementation and experimental validation of the Harmonic-Passive Motion Paradigm (H-PMP), an advanced architecture of the Fractal Impedance Controller (FIC) designed to mimic human-like interaction. The authors present a unified framework for FIC deployment supported by an open-source software suite integrating ROS2, MuJoCo, and physical hardware. Through three experiments on a Kinova Gen3 arm—including trajectory tracking, wall press with sudden contact loss, and learning-by-demonstration—the H-PMP is compared against earlier FIC architectures. Results show that H-PMP achieves a seamless trade-off between admittance and impedance behaviors, making it suitable for unstructured environments where robots can transition between interaction modes without computational overhead.

Editorial context

The authors present a unified framework for deploying the Fractal Impedance Controller (FIC) with its advanced Harmonic-Passive Motion Paradigm (H-PMP), validated via three experiments on a Kinova Gen3 arm that demonstrate a trade-off between admittance and impedance behaviors without computational overhead.

Source: arXiv Robotics — research abstracts · Read original article ↗

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Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.

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Source excerpts and review record

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ive Motion Paradigm (H-PMP), an evolution of the Passive Motion Paradigm designed to mimic human-like interaction. Furthermore, we present a unified framework for FIC deployment, supported by an open-source software suite featuring ROS2, MuJoCo, and physical hardware integrations. Through three experiments conducted on a Kinova Gen3 arm (trajectory tracking, a wall press with sudden loss of contact, and learning-by-d

Open source S3

Source:arXiv Robotics — research abstracts · arxiv.org

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