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Found 14 papers, 7 papers with code
A Multifidelity Sim-to-Real Pipeline for Verifiable and Compositional Reinforcement Learning
We propose and demonstrate a compositional framework for training and verifying reinforcement learning (RL) systems within a multifidelity sim-to-real pipeline, in order to deploy reliable and adaptable RL policies on physical hardware.
Formal Methods for Autonomous Systems
Formal methods refer to rigorous, mathematical approaches to system development and have played a key role in establishing the correctness of safety-critical systems.
Verifiable Reinforcement Learning Systems via Compositionality
We propose a framework for verifiable and compositional reinforcement learning (RL) in which a collection of RL subsystems, each of which learns to accomplish a separate subtask, are composed to achieve an overall task.
Multimodal Pretrained Models for Sequential Decision-Making: Synthesis, Verification, Grounding, and Perception
We develop an algorithm that utilizes the knowledge from pretrained models to construct and verify controllers for sequential decision-making tasks, and to ground these controllers to task environments through visual observations.
How to Learn and Generalize From Three Minutes of Data: Physics-Constrained and Uncertainty-Aware Neural Stochastic Differential Equations
We present a framework and algorithms to learn controlled dynamics models using neural stochastic differential equations (SDEs) -- SDEs whose drift and diffusion terms are both parametrized by neural networks.
Differential Privacy in Cooperative Multiagent Planning
We synthesize policies that are robust to privacy by reducing the value of the total correlation.
Physics-Informed Kernel Embeddings: Integrating Prior System Knowledge with Data-Driven Control
Our proposed approach incorporates prior knowledge of the system dynamics as a bias term in the kernel learning problem.
Automaton-Based Representations of Task Knowledge from Generative Language Models
However, the textual outputs from GLMs cannot be formally verified or used for sequential decision-making.
Compositional Learning of Dynamical System Models Using Port-Hamiltonian Neural Networks
Toward the objective of learning composite models of such systems from data, we present i) a framework for compositional neural networks, ii) algorithms to train these models, iii) a method to compose the learned models, iv) theoretical results that bound the error of the resulting composite models, and v) a method to learn the composition itself, when it is not known a priori.
Planning Not to Talk: Multiagent Systems that are Robust to Communication Loss
In this work, we develop joint policies for cooperative multiagent systems that are robust to potential losses in communication.
Taylor-Lagrange Neural Ordinary Differential Equations: Toward Fast Training and Evaluation of Neural ODEs
Neural ordinary differential equations (NODEs) -- parametrizations of differential equations using neural networks -- have shown tremendous promise in learning models of unknown continuous-time dynamical systems from data.
Neural Networks with Physics-Informed Architectures and Constraints for Dynamical Systems Modeling
The physics-informed constraints are enforced via the augmented Lagrangian method during the model's training.
Verifiable and Compositional Reinforcement Learning Systems
We propose a framework for verifiable and compositional reinforcement learning (RL) in which a collection of RL subsystems, each of which learns to accomplish a separate subtask, are composed to achieve an overall task.
Reward Machines for Cooperative Multi-Agent Reinforcement Learning
In cooperative multi-agent reinforcement learning, a collection of agents learns to interact in a shared environment to achieve a common goal.
