no code implementations • 15 Sep 2023 • Rohan Sinha, Edward Schmerling, Marco Pavone
When we rely on deep-learned models for robotic perception, we must recognize that these models may behave unreliably on inputs dissimilar from the training data, compromising the closed-loop system's safety.
no code implementations • 28 Dec 2022 • Rohan Sinha, Apoorva Sharma, Somrita Banerjee, Thomas Lew, Rachel Luo, Spencer M. Richards, Yixiao Sun, Edward Schmerling, Marco Pavone
When testing conditions differ from those represented in training data, so-called out-of-distribution (OOD) inputs can mar the reliability of learned components in the modern robot autonomy stack.
no code implementations • 2 Dec 2022 • Rohan Sinha, James Harrison, Spencer M. Richards, Marco Pavone
We propose a learning-based robust predictive control algorithm that compensates for significant uncertainty in the dynamics for a class of discrete-time systems that are nominally linear with an additive nonlinear component.
no code implementations • 17 Nov 2022 • Rachel Luo, Rohan Sinha, Yixiao Sun, Ali Hindy, Shengjia Zhao, Silvio Savarese, Edward Schmerling, Marco Pavone
When deploying modern machine learning-enabled robotic systems in high-stakes applications, detecting distribution shift is critical.
no code implementations • 16 Apr 2021 • Rohan Sinha, James Harrison, Spencer M. Richards, Marco Pavone
We propose a learning-based robust predictive control algorithm that compensates for significant uncertainty in the dynamics for a class of discrete-time systems that are nominally linear with an additive nonlinear component.