Search Results for author: Stephen L. Smith

Found 10 papers, 2 papers with code

Stochastic Data-Driven Predictive Control with Equivalence to Stochastic MPC

no code implementations23 Dec 2023 RuiQi Li, John W. Simpson-Porco, Stephen L. Smith

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.

Model Predictive Control

Data-Driven Learning of Safety-Critical Control with Stochastic Control Barrier Functions

no code implementations22 May 2022 Chuanzheng Wang, Yiming Meng, Stephen L. Smith, Jun Liu

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

no code implementations20 Apr 2022 Shamak Dutta, Nils Wilde, Stephen L. Smith

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

no code implementations30 Mar 2022 RuiQi Li, John W. Simpson-Porco, Stephen L. Smith

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.

LEMMA Model Predictive Control

Learning Reward Functions from Scale Feedback

1 code implementation1 Oct 2021 Nils Wilde, Erdem Biyik, Dorsa Sadigh, Stephen L. Smith

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

no code implementations6 Apr 2021 Chuanzheng Wang, Yiming Meng, Stephen L. Smith, Jun Liu

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

no code implementations8 May 2020 Nils Wilde, Dana Kulic, Stephen L. Smith

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

no code implementations2 Apr 2020 Chuanzheng Wang, Yi-Nan Li, Stephen L. Smith, Jun Liu

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.

Motion Planning reinforcement-learning +1

Classification of Resting-State fMRI using Evolutionary Algorithms: Towards a Brain Imaging Biomarker for Parkinson's Disease

no code implementations11 Oct 2019 Amir Dehsarvi, Stephen L. Smith

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.

Evolutionary Algorithms General Classification +3

Learning a Lattice Planner Control Set for Autonomous Vehicles

1 code implementation5 Mar 2019 Ryan De Iaco, Stephen L. Smith, Krzysztof Czarnecki

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.

Autonomous Vehicles

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