Model Predictive Control
242 papers with code • 0 benchmarks • 0 datasets
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Use these libraries to find Model Predictive Control models and implementationsMost implemented papers
Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning
Model-free deep reinforcement learning algorithms have been shown to be capable of learning a wide range of robotic skills, but typically require a very large number of samples to achieve good performance.
Multi-Period Trading via Convex Optimization
The methods we describe in this paper can be thought of as good ways to exploit predictions, no matter how they are made.
Trust-aware Safe Control for Autonomous Navigation: Estimation of System-to-human Trust for Trust-adaptive Control Barrier Functions
A trust-aware safe control system for autonomous navigation in the presence of humans, specifically pedestrians, is presented.
Neural Potential Field for Obstacle-Aware Local Motion Planning
Experiment on Husky UGV mobile robot showed that our approach allows real-time and safe local planning.
Deep convolutional recurrent autoencoders for learning low-dimensional feature dynamics of fluid systems
In this work we propose a deep learning-based strategy for nonlinear model reduction that is inspired by projection-based model reduction where the idea is to identify some optimal low-dimensional representation and evolve it in time.
Learning, Planning, and Control in a Monolithic Neural Event Inference Architecture
We introduce REPRISE, a REtrospective and PRospective Inference SchEme, which learns temporal event-predictive models of dynamical systems.
Differentiable MPC for End-to-end Planning and Control
locuslab/differentiable-mpc
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NeurIPS 2018
We present foundations for using Model Predictive Control (MPC) as a differentiable policy class for reinforcement learning in continuous state and action spaces.
Interactive Differentiable Simulation
While learning-based models of the environment dynamics have contributed to significant improvements in sample efficiency compared to model-free reinforcement learning algorithms, they typically fail to generalize to system states beyond the training data, while often grounding their predictions on non-interpretable latent variables.
Driving in Dense Traffic with Model-Free Reinforcement Learning
Traditional planning and control methods could fail to find a feasible trajectory for an autonomous vehicle to execute amongst dense traffic on roads.
Deep Dynamics Models for Learning Dexterous Manipulation
Dexterous multi-fingered hands can provide robots with the ability to flexibly perform a wide range of manipulation skills.
