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09/09/2023
Verifiable Reinforcement Learning Systems via Compositionality
We propose a framework for verifiable and compositional reinforcement le...
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08/10/2023
Multimodal Pretrained Models for Sequential Decision-Making: Synthesis, Verification, Grounding, and Perception
Recently developed pretrained models can encode rich world knowledge exp...
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06/10/2023
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 model...
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01/20/2023
Differential Privacy in Cooperative Multiagent Planning
Privacy-aware multiagent systems must protect agents' sensitive data whi...
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01/09/2023
Physics-Informed Kernel Embeddings: Integrating Prior System Knowledge with Data-Driven Control
Data-driven control algorithms use observations of system dynamics to co...
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12/01/2022
Compositional Learning of Dynamical System Models Using Port-Hamiltonian Neural Networks
Many dynamical systems – from robots interacting with their surroundings...
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01/17/2022
Planning Not to Talk: Multiagent Systems that are Robust to Communication Loss
In a cooperative multiagent system, a collection of agents executes a jo...
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01/14/2022
Taylor-Lagrange Neural Ordinary Differential Equations: Toward Fast Training and Evaluation of Neural ODEs
Neural ordinary differential equations (NODEs) – parametrizations of dif...
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09/14/2021
Neural Networks with Physics-Informed Architectures and Constraints for Dynamical Systems Modeling
Effective inclusion of physics-based knowledge into deep neural network ...
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06/07/2021