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Found 8 papers, 1 papers with code
A Biologically-Inspired Computational Model of Time Perception
We conclude that the agent is able to perceive time similarly to animals when it comes to their intrinsic mechanisms of interpreting time and performing time-aware actions.
Symplectic Momentum Neural Networks -- Using Discrete Variational Mechanics as a prior in Deep Learning
The combination of such formulation leads SyMos to be constrained towards preserving important geometric structures such as momentum and a symplectic form and learn from limited data.
COSMIC: fast closed-form identification from large-scale data for LTV systems
To prove its applicability to real world systems, we test with spring-mass-damper system and use the estimated model to find the optimal control path.
Decision Support Models for Predicting and Explaining Airport Passenger Connectivity from Data
Predicting if passengers in a connecting flight will lose their connection is paramount for airline profitability.
Autonomous orbit determination for satelite formations using relative sensing: observability analysis and optimization
Orbit determination of spacecraft in orbit has been mostly dependent on either GNSS satellite signals or ground station telemetry.
Conjunction Data Messages behave as a Poisson Process
(2) When exactly and with what uncertainty will the next message arrive?
Teaching robots to perceive time -- A reinforcement learning approach (Extended version)
In this paper, we study how to replicate neural mechanisms involved in time perception, allowing robots to take a step towards temporal cognition.
A deep learning approach for understanding natural language commands for mobile service robots
Using natural language to give instructions to robots is challenging, since natural language understanding is still largely an open problem.
