An inertial navigation framework using mixture-of-experts models and bicycle pedaling constraints improves tracking accuracy by at least 12% over baselines in GNSS-blocked environments, with wheel speed errors below 0.5 m/s at the 95th percentile.
On the theory of filter amplifiers
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FDN uses spectral decomposition, asymmetric heads for deterministic and probabilistic wrench components, and frequency-aware filtering to forecast high-frequency wrench from proprioception, outperforming baselines on hydraulic manipulator grinding data after pretraining and transfer.
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Tracking Large-scale Shared Bikes with Inertial Motion Learning in GNSS Blocked Environments
An inertial navigation framework using mixture-of-experts models and bicycle pedaling constraints improves tracking accuracy by at least 12% over baselines in GNSS-blocked environments, with wheel speed errors below 0.5 m/s at the 95th percentile.
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Frequency-aware Decomposition Learning for Sensorless Wrench Forecasting on a Vibration-rich Hydraulic Manipulator
FDN uses spectral decomposition, asymmetric heads for deterministic and probabilistic wrench components, and frequency-aware filtering to forecast high-frequency wrench from proprioception, outperforming baselines on hydraulic manipulator grinding data after pretraining and transfer.