Aman Kumar Singh

In real-world robotics, uncertainty is the norm rather than the exception. Whether it’s a self-driving car navigating in traffic or a humanoid like Boston Dynamics’ Atlas balancing on rough terrain, perfect models and zero-noise assumptions simply don’t exist. This is where Stochastic Model Predictive Control (SMPC) becomes powerful. By introducing chance constraints, SMPC allows robots to make safe and optimal decisions with high probability, rather than demanding impossible perfection.

Hi everyone! When I heard about the launch of NASA-ISRO Synthetic Aperture Radar (NISAR) mission, I was very excited. It was supposed to...

Have you ever wondered how satellites maintain their orientation in space or how engineers simulate their complex dynamics? In this...