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Found 21 papers, 8 papers with code
Solving Recurrent MIPs with Semi-supervised Graph Neural Networks
We propose an ML-based model that automates and expedites the solution of MIPs by predicting the values of variables.
Multi-Rate Planning and Control of Uncertain Nonlinear Systems: Model Predictive Control and Control Lyapunov Functions
Modern control systems must operate in increasingly complex environments subject to safety constraints and input limits, and are often implemented in a hierarchical fashion with different controllers running at multiple time scales.
MLNav: Learning to Safely Navigate on Martian Terrains
We present MLNav, a learning-enhanced path planning framework for safety-critical and resource-limited systems operating in complex environments, such as rovers navigating on Mars.
CEM-GD: Cross-Entropy Method with Gradient Descent Planner for Model-Based Reinforcement Learning
It then uses the top trajectories as initialization for gradient descent and applies gradient updates to each of these trajectories to find the optimal action sequence.
Interactive multi-modal motion planning with Branch Model Predictive Control
Motion planning for autonomous robots and vehicles in presence of uncontrolled agents remains a challenging problem as the reactive behaviors of the uncontrolled agents must be considered.
Learning Performance Bounds for Safety-Critical Systems
As a result, the test and evaluation ideal would be to verify the efficacy of a system simulator and use this verification result to make a statement on true system performance.
Risk-Averse Decision Making Under Uncertainty
In this paper, we consider the problem of designing policies for MDPs and POMDPs with objectives and constraints in terms of dynamic coherent risk measures, which we refer to as the constrained risk-averse problem.
Iterative Model Predictive Control for Piecewise Systems
First, we present an algorithm that leverages a feasible trajectory that completes the task to construct a control policy which guarantees that state and input constraints are recursively satisfied and that the closed-loop system reaches the goal state in finite time.
A Simple Robust MPC for Linear Systems with Parametric and Additive Uncertainty
We propose a simple and computationally efficient approach for designing a robust Model Predictive Controller (MPC) for constrained uncertain linear systems.
Learning to Control an Unstable System with One Minute of Data: Leveraging Gaussian Process Differentiation in Predictive Control
We present a straightforward and efficient way to control unstable robotic systems using an estimated dynamics model.
Unified Multi-Rate Control: from Low Level Actuation to High Level Planning
In this paper we present a hierarchical multi-rate control architecture for nonlinear autonomous systems operating in partially observable environments.
Constrained Risk-Averse Markov Decision Processes
We consider the problem of designing policies for Markov decision processes (MDPs) with dynamic coherent risk objectives and constraints.
Reactive motion planning with probabilistic safety guarantees
Motion planning in environments with multiple agents is critical to many important autonomous applications such as autonomous vehicles and assistive robots.
Robust MPC for Linear Systems with Parametric and Additive Uncertainty: A Novel Constraint Tightening Approach
We propose a novel approach to design a robust Model Predictive Controller (MPC) for constrained uncertain linear systems.
Trajectory Optimization for Nonlinear Multi-Agent Systems using Decentralized Learning Model Predictive Control
We present a decentralized minimum-time trajectory optimization scheme based on learning model predictive control for multi-agent systems with nonlinear decoupled dynamics and coupled state constraints.
ABC-LMPC: Safe Sample-Based Learning MPC for Stochastic Nonlinear Dynamical Systems with Adjustable Boundary Conditions
Sample-based learning model predictive control (LMPC) strategies have recently attracted attention due to their desirable theoretical properties and their good empirical performance on robotic tasks.
Robust Learning Model Predictive Control for Linear Systems Performing Iterative Tasks
At each iteration of the control task the closed-loop state, input and cost are stored and used in the controller design.
Safety Augmented Value Estimation from Demonstrations (SAVED): Safe Deep Model-Based RL for Sparse Cost Robotic Tasks
Reinforcement learning (RL) for robotics is challenging due to the difficulty in hand-engineering a dense cost function, which can lead to unintended behavior, and dynamical uncertainty, which makes exploration and constraint satisfaction challenging.
Model-based Reinforcement Learning
reinforcement-learning
+1
Learning Model Predictive Control for Iterative Tasks: A Computationally Efficient Approach for Linear System
The control scheme is reference-free and is able to improve its performance by learning from previous iterations.
Autonomous Racing using Learning Model Predictive Control
A novel learning Model Predictive Control technique is applied to the autonomous racing problem.
Learning Model Predictive Control for iterative tasks. A Data-Driven Control Framework
The controller is reference-free and is able to improve its performance by learning from previous iterations.
