6 citations · 6 across the 13 of their papers we have counts for
13 papers
Constricting Tubes for Prescribed-Time Safe Control
Darshan Gadginmath, Ahmed Allibhoy, Fabio Pasqualetti
We propose a constricting Control Barrier Function (CBF) framework for prescribed-time control of control-affine systems with input constraints. Given a system starting outside a t…
Provably Safe Generative Sampling with Constricting Barrier Functions
Darshan Gadginmath, Ahmed Allibhoy, Fabio Pasqualetti
Flow-based generative models, such as diffusion models and flow matching models, have achieved remarkable success in learning complex data distributions. However, a critical gap re…
Active Probing with Multimodal Predictions for Motion Planning
Darshan Gadginmath, Farhad Nawaz, Minjun Sung +5
Navigation in dynamic environments requires autonomous systems to reason about uncertainties in the behavior of other agents. In this paper, we introduce a unified framework that c…
Graph-based Path Planning with Dynamic Obstacle Avoidance for Autonomous Parking
Farhad Nawaz, Minjun Sung, Darshan Gadginmath +6
Safe and efficient path planning in parking scenarios presents a significant challenge due to the presence of cluttered environments filled with static and dynamic obstacles. To ad…
Score Matching Diffusion Based Feedback Control and Planning of Nonlinear Systems
Karthik Elamvazhuthi, Darshan Gadginmath, Fabio Pasqualetti
In this paper, we propose a deterministic diffusion-based framework for controlling the probability density of nonlinear control-affine systems, with theoretical guarantees for dri…
Dynamics-aware Diffusion Models for Planning and Control
Darshan Gadginmath, Fabio Pasqualetti
This paper addresses the problem of generating dynamically admissible trajectories for control tasks using diffusion models, particularly in scenarios where the environment is comp…
Predicting AI Agent Behavior through Approximation of the Perron-Frobenius Operator
Shiqi Zhang, Darshan Gadginmath, Fabio Pasqualetti
Predicting the behavior of AI-driven agents is particularly challenging without a preexisting model. In our paper, we address this by treating AI agents as nonlinear dynamical syst…
Denoising Diffusion-Based Control of Nonlinear Systems
Karthik Elamvazhuthi, Darshan Gadginmath, Fabio Pasqualetti
We propose a novel approach based on Denoising Diffusion Probabilistic Models (DDPMs) to control nonlinear dynamical systems. DDPMs are the state-of-art of generative models that h…
Fusing Multiple Algorithms for Heterogeneous Online Learning
Darshan Gadginmath, Shivanshu Tripathi, Fabio Pasqualetti
This study addresses the challenge of online learning in contexts where agents accumulate disparate data, face resource constraints, and use different local algorithms. This paper…
Feedback linearization through the lens of data
C. De Persis, D. Gadginmath, F. Pasqualetti +1
Controlling nonlinear systems, especially when data are being used to offset uncertainties in the model, is hard. A natural approach when dealing with the challenges of nonlinear c…
Data-Driven Feedback Linearization using the Koopman Generator
Darshan Gadginmath, Vishaal Krishnan, Fabio Pasqualetti
This paper contributes a theoretical framework for data-driven feedback linearization of nonlinear control-affine systems. We unify the traditional geometric perspective on feedbac…
Direct vs Indirect Methods for Behavior-based Attack Detection
Darshan Gadginmath, Vishaal Krishnan, Fabio Pasqualetti
We study the problem of data-driven attack detection for unknown LTI systems using only input-output behavioral data. In contrast with model-based detectors that use errors from an…
Data-Driven Distributed Intersection Management for Connected and Automated Vehicles
Darshan Gadginmath, Pavankumar Tallapragada
This work addresses the problem of autonomous traffic management at an isolated intersection for connected and automated vehicles. We decompose the trajectory of each vehicle into…