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Shreenabh Agrawal

4 accepted 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

Cited by 1Source ↗code ↗Scholar ↗
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…

Cited by 0Source ↗PDF ↗Scholar ↗
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

Cited by 1Source ↗Scholar ↗
2024

Barrier Functions Inspired Reward Shaping for Reinforcement Learning

Nilaksh, Abhishek Ranjan, Shreenabh Agrawal, Aayush Jain, Pushpak Jagtap, Shishir Kolathaya
ICRA 2024poster

Reinforcement Learning (RL) has progressed from simple control tasks to complex real-world challenges with large state spaces. While RL excels in these tasks, training time remains a limitation. Reward shaping is a popular solution, but existing methods often rely on value functions, which face scal…

Cited by 7Source ↗code ↗Scholar ↗