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arXiv Machine Learning By Hyeonbeen Lee, Min-Jae Jung, Tae-Kyeong Yeu, Jong-Boo Han, Daegil Park, Simon Stepputtis, Jin-Gyun Kim

Frequency-aware decomposition learning for sensorless wrench estimation in vibration-rich robotic contact

Read the original on arXiv Machine Learning →

The paper introduces a Frequency-aware Decomposition Network (FDN) that estimates vibration-rich wrench signals in sensorless robotic contact tasks. FDN splits the wrench horizon into low-frequency trends and high-frequency residuals, using pointwise regression for the former and a learned conditional distribution for the latter. Experiments on a 6‑DoF hydraulic manipulator show that FDN reduces high‑frequency amplitude error by up to 47% compared to baselines while maintaining low‑frequency accuracy, and can perform 1,000 ms multi‑step‑ahead estimation in 11 ms on a single CPU thread.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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