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Found 10 papers, 2 papers with code
Stochastic Data-Driven Predictive Control with Equivalence to Stochastic MPC
We propose a data-driven receding-horizon control method dealing with the chance-constrained output-tracking problem of unknown stochastic linear time-invariant (LTI) systems with partial state observation.
Data-Driven Learning of Safety-Critical Control with Stochastic Control Barrier Functions
More specifically, we propose a data-driven stochastic control barrier function (DDSCBF) framework and use supervised learning to learn the unknown stochastic dynamics via the DDSCBF scheme.
Informative Path Planning in Random Fields via Mixed Integer Programming
We present a new mixed integer formulation for the discrete informative path planning problem in random fields.
Data-Driven Model Predictive Control for Linear Time-Periodic Systems
Robustness of the algorithm to noisy data is illustrated via simulation of a regularized version of the algorithm applied to a stochastic multi-input multi-output LTP system.
Learning Reward Functions from Scale Feedback
Today's robots are increasingly interacting with people and need to efficiently learn inexperienced user's preferences.
Safety-Critical Control of Stochastic Systems using Stochastic Control Barrier Functions
We propose a notion of stochastic control barrier functions (SCBFs)and show that SCBFs can significantly reduce the control efforts, especially in the presence of noise, compared to stochastic reciprocal control barrier functions (SRCBFs), and offer a less conservative estimation of safety probability, compared to stochastic zeroing control barrier functions (SZCBFs).
Active Preference Learning using Maximum Regret
In active preference learning, a user chooses the preferred behaviour from a set of alternatives, from which the robot learns the user's preferences, modeled as a parameterized cost function.
Continuous Motion Planning with Temporal Logic Specifications using Deep Neural Networks
A na\"ive way of solving a motion planning problem with LTL specifications using reinforcement learning is to sample a trajectory and then assign a high reward for training if the trajectory satisfies the entire LTL formula.
Classification of Resting-State fMRI using Evolutionary Algorithms: Towards a Brain Imaging Biomarker for Parkinson's Disease
Hence, these findings underscore the relevance of both DCM analyses for classification and CGP as a novel classification tool for brain imaging data with medical implications for disease diagnosis, particularly in early and asymptomatic stages.
Learning a Lattice Planner Control Set for Autonomous Vehicles
This paper introduces a method to compute a sparse lattice planner control set that is suited to a particular task by learning from a representative dataset of vehicle paths.
