3 papers
cs.RO2026
Learning-based Observer for Coupled Disturbance
Jindou Jia, Meng Wang, Zihan Yang +4
Achieving high-precision control for robotic systems is hindered by the low-fidelity dynamical model and external disturbances. Especially, the intricate coupling between internal…
cs.RO2026
Unified Meta-Representation and Feedback Calibration for General Disturbance Estimation
Zihan Yang, Jindou Jia, Meng Wang +3
Precise control in modern robotic applications is always an open issue due to unknown time-varying disturbances. Existing meta-learning-based approaches require a shared representa…
cs.LG2025
Feedback Favors the Generalization of Neural ODEs
Jindou Jia, Zihan Yang, Meng Wang +4
The well-known generalization problem hinders the application of artificial neural networks in continuous-time prediction tasks with varying latent dynamics. In sharp contrast, bio…