Allen Emmanuel Binny — accepted AI-conference papers

2026

Safe and Stable Neural Network Dynamical Systems for Robot Motion Planning

Allen Emmanuel Binny, Mahathi Anand, Hugo T. M. Kussaba, Lingyun Chen, Shreenabh Agrawal, Fares J. Abu-Dakka +1

RA-L 2026

Learning safe and stable robot motions from demonstrations remains a challenge, especially in complex, nonlinear tasks involving dynamic, obstacle-rich environments. In this paper, we propose Safe and Stable Neural Network Dynamical Systems S<inline-formula xmlns:mml="http://www.w3.org/1998/Math/Mat

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2026

Safe and Stable Neural Network Dynamical Systems for Robot Motion Planning

Allen Emmanuel Binny, Mahathi Anand, Hugo Tadashi Kussaba, Lingyun Chen, Shreenabh Agrawal, Fares Abu-Dakka +1

ICRA 2026poster

Learning safe and stable robot motions from demonstrations remains a challenge, especially in complex, nonlinear tasks involving dynamic, obstacle-rich environments. In this paper, we propose Safe and Stable Neural Network Dynamical Systems S²-NNDS, a learning-from-demonstration framework that simul…

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2025

Scalable Learning of High-Dimensional Demonstrations with Composition of Linear Parameter Varying Dynamical Systems

Shreenabh Agrawal, Hugo T. M. Kussaba, Lingyun Chen, Allen Emmanuel Binny, Pushpak Jagtap, Sami Haddadin +1

IROS 2025

Learning from Demonstration (LfD) techniques enable robots to learn and generalize tasks from user demonstrations, eliminating the need for coding expertise among end-users. One established technique to implement LfD in robots is to encode demonstrations in a stable Dynamical System (DS). However, f

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