3 papers
cs.RO2026
Learning Dissipative Dynamics with Dissipativity-by-Construction Discrete-Time Neural Networks
Tuan Luong, Hyungpil Moon
Dissipativity is a fundamental system-theoretic property closely related to stability, passivity, and input--output stability, and is particularly important in robotics, where lear…
cs.LG2026
Data-driven discrete-time deep recurrent neural network-based modeling for dissipative systems
Tuan Luong, Hyungpil Moon
Physical AI has gained increasing attention for its role in developing AI systems that better understand, predict, and control real-world dynamics. Achieving this requires AI model…
cs.RO2026
URF: A Unified Robot Control-Policy Framework for Stable Contact Aware Manipulation
Jiyou Shin, Youngjin Seo, Jaeseog Won +5
Learning-based manipulation policies usually predict robot actions from sensory observations and leave their execution to a separate low-level controller. In rigid contact, this se…