62 citations · 70 across the 8 of their papers we have counts for
9 papers
Towards Compute-Optimal Many-Shot In-Context Learning
Shahriar Golchin, Yanfei Chen, Rujun Han +7
Long-context large language models (LLMs) are able to process inputs containing up to several million tokens. In the scope of in-context learning (ICL), this translates into using…
MPPI-Generic: A CUDA Library for Stochastic Trajectory Optimization
Bogdan Vlahov, Jason Gibson, Manan Gandhi +1
This paper introduces a new C++/CUDA library for GPU-accelerated stochastic optimization called MPPI-Generic. It provides implementations of Model Predictive Path Integral control,…
Safe Importance Sampling in Model Predictive Path Integral Control
Manan Gandhi, Hassan Almubarak, Evangelos Theodorou
We introduce the notion of importance sampling under embedded barrier state control, titled Safety Controlled Model Predictive Path Integral Control (SC-MPPI). For robotic systems…
Gaussian Process Barrier States for Safe Trajectory Optimization and Control
Hassan Almubarak, Manan Gandhi, Yuichiro Aoyama +2
This paper proposes embedded Gaussian Process Barrier States (GP-BaS), a methodology to safely control unmodeled dynamics of nonlinear system using Bayesian learning. Gaussian Proc…
Safety in Augmented Importance Sampling: Performance Bounds for Robust MPPI
Manan Gandhi, Hassan Almubarak, Yuichiro Aoyama +1
This work explores the nature of augmented importance sampling in safety-constrained model predictive control problems. When operating in a constrained environment, sampling based…
Variational Inference MPC using Tsallis Divergence
Ziyi Wang, Oswin So, Jason Gibson +4
In this paper, we provide a generalized framework for Variational Inference-Stochastic Optimal Control by using thenon-extensive Tsallis divergence. By incorporating the deformed e…