12 papers
Multi-Agent Flow Matching with Decoupled Generative Guidance
Ruoyu Lin, Magnus Egerstedt, Fabio Pasqualetti
Generative modeling is widely used for producing diverse objects from complex, multimodal distributions. However, its expressivity does not, in general, come with formal guarantees…
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…
Geometric SSM: LTI State Space Models for Selective Tasks
Umberto Casti, Giacomo Baggio, Sandro Zampieri +1
A key claim in recent work on Selective State Space Models is that selectivity, the ability to focus on relevant information while filtering irrelevant inputs, requires breaking th…
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…
Noise in the reverse process improves the approximation capabilities of diffusion models
Karthik Elamvazhuthi, Samet Oymak, Fabio Pasqualetti
In Score based Generative Modeling (SGMs), the state-of-the-art in generative modeling, stochastic reverse processes are known to perform better than their deterministic counterpar…
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…
Astrocytes as a mechanism for meta-plasticity and contextually-guided network function
Lulu Gong, Fabio Pasqualetti, Thomas Papouin +1
Astrocytes are a ubiquitous and enigmatic type of non-neuronal cell and are found in the brain of all vertebrates. While traditionally viewed as being supportive of neurons, it is…