Search Results for author:
Found 47 papers, 12 papers with code
Data-Driven Hamiltonian for Direct Construction of Safe Set from Trajectory Data
The reachable set computed based on the DDH is also ensured to be a conservative approximation of the true reachable set.
System-Level Analysis of Module Uncertainty Quantification in the Autonomy Pipeline
We present a novel perspective on the design, use, and role of uncertainty measures for learned modules in an autonomous system.
Safety Filters for Black-Box Dynamical Systems by Learning Discriminating Hyperplanes
As such, we believe that the new notion of the discriminating hyperplane offers a more generalizable direction towards designing safety filters, encompassing and extending existing certificate-function-based or safe RL methodologies.
A Forward Reachability Perspective on Robust Control Invariance and Discount Factors in Reachability Analysis
This strong link we establish between the reachability problem and the barrier constraint, while guaranteeing the continuity of the value function, is not achievable by previous backward reachability-based formulations.
Out of Distribution Detection via Domain-Informed Gaussian Process State Space Models
In this paper, we propose (i) a novel approach to embed existing domain knowledge in the kernel and (ii) an OoD online runtime monitor, based on receding-horizon predictions.
Maximizing Seaweed Growth on Autonomous Farms: A Dynamic Programming Approach for Underactuated Systems Navigating on Uncertain Ocean Currents
We propose a dynamic programming-based method to efficiently solve for the optimal growth value function when true currents are known.
Stranding Risk for Underactuated Vessels in Complex Ocean Currents: Analysis and Controllers
We demonstrate the safety of our approach in such realistic situations empirically with large-scale simulations of a vessel navigating in high-risk regions in the Northeast Pacific.
Safe Connectivity Maintenance of Underactuated Multi-Agent Networks in Dynamic Oceanic Environments
Next, we design a low-interference safe interaction (LISIC) policy that trades off the performance policy and the safety control to ensure safe and performant operation.
Optimality Guarantees for Particle Belief Approximation of POMDPs
Thus, when combined with sparse sampling MDP algorithms, this approach can yield algorithms for POMDPs that have no direct theoretical dependence on the size of the state and observation spaces.
Recursively Feasible Probabilistic Safe Online Learning with Control Barrier Functions
We then present the pointwise feasibility conditions of the resulting safety controller, highlighting the level of richness that the available system information must meet to ensure safety.
On the Computational Consequences of Cost Function Design in Nonlinear Optimal Control
However, despite the extensive impacts of methods such as receding horizon control, dynamic programming and reinforcement learning, the design of cost functions for a particular system often remains a heuristic-driven process of trial and error.
Koopman-Based Neural Lyapunov Functions for General Attractors
Koopman spectral theory has grown in the past decade as a powerful tool for dynamical systems analysis and control.
Infinite-Horizon Reach-Avoid Zero-Sum Games via Deep Reinforcement Learning
We address this problem by designing a new value function with a contracting Bellman backup, where the super-zero level set, i. e., the set of states where the value function is evaluated to be non-negative, recovers the reach-avoid set.
Safety and Liveness Guarantees through Reach-Avoid Reinforcement Learning
Recent successes in reinforcement learning methods to approximately solve optimal control problems with performance objectives make their application to certification problems attractive; however, the Lagrange-type objective used in reinforcement learning is not suitable to encode temporal logic requirements.
Compositional Learning-based Planning for Vision POMDPs
The Partially Observable Markov Decision Process (POMDP) is a powerful framework for capturing decision-making problems that involve state and transition uncertainty.
Towards cyber-physical systems robust to communication delays: A differential game approach
Collaboration between interconnected cyber-physical systems is becoming increasingly pervasive.
A Computationally Efficient Hamilton-Jacobi-based Formula for State-Constrained Optimal Control Problems
Based on the Lax formula [2], this paper proposes an HJ formula for the state-constrained optimal control problem for nonlinear systems.
Pointwise Feasibility of Gaussian Process-based Safety-Critical Control under Model Uncertainty
However, since these constraints rely on a model of the system, when this model is inaccurate the guarantees of safety and stability can be easily lost.
Robust Control Barrier-Value Functions for Safety-Critical Control
This paper works towards unifying two popular approaches in the safety control community: Hamilton-Jacobi (HJ) reachability and Control Barrier Functions (CBFs).
Analyzing Human Models that Adapt Online
This enables us to leverage tools from reachability analysis and optimal control to compute the set of hypotheses the robot could learn in finite time, as well as the worst and best-case time it takes to learn them.
Risk-sensitive safety analysis using Conditional Value-at-Risk
In addition, we propose a second definition for risk-sensitive safe sets and provide a tractable method for their estimation without using a parameter-dependent upper bound.
Scalable Learning of Safety Guarantees for Autonomous Systems using Hamilton-Jacobi Reachability
However, work to learn and update safety analysis is limited to small systems of about two dimensions due to the computational complexity of the analysis.
Voronoi Progressive Widening: Efficient Online Solvers for Continuous State, Action, and Observation POMDPs
This paper introduces Voronoi Progressive Widening (VPW), a generalization of Voronoi optimistic optimization (VOO) and action progressive widening to partially observable Markov decision processes (POMDPs).
Gaussian Process-based Min-norm Stabilizing Controller for Control-Affine Systems with Uncertain Input Effects and Dynamics
This paper presents a method to design a min-norm Control Lyapunov Function (CLF)-based stabilizing controller for a control-affine system with uncertain dynamics using Gaussian Process (GP) regression.
Approximate Solutions to a Class of Reachability Games
In this paper, we present a method for finding approximate Nash equilibria in a broad class of reachability games.
Dynamically Computing Adversarial Perturbations for Recurrent Neural Networks
Convolutional and recurrent neural networks have been widely employed to achieve state-of-the-art performance on classification tasks.
Reinforcement Learning for Safety-Critical Control under Model Uncertainty, using Control Lyapunov Functions and Control Barrier Functions
In this paper, the issue of model uncertainty in safety-critical control is addressed with a data-driven approach.
Improving Input-Output Linearizing Controllers for Bipedal Robots via Reinforcement Learning
The main drawbacks of input-output linearizing controllers are the need for precise dynamics models and not being able to account for input constraints.
Technical Report: Adaptive Control for Linearizable Systems Using On-Policy Reinforcement Learning
This paper proposes a framework for adaptively learning a feedback linearization-based tracking controller for an unknown system using discrete-time model-free policy-gradient parameter update rules.
A Hamilton-Jacobi Reachability-Based Framework for Predicting and Analyzing Human Motion for Safe Planning
We construct a new continuous-time dynamical system, where the inputs are the observations of human behavior, and the dynamics include how the belief over the model parameters change.
Feedback Linearization for Unknown Systems via Reinforcement Learning
We present a novel approach to control design for nonlinear systems which leverages model-free policy optimization techniques to learn a linearizing controller for a physical plant with unknown dynamics.
Sparse tree search optimality guarantees in POMDPs with continuous observation spaces
Partially observable Markov decision processes (POMDPs) with continuous state and observation spaces have powerful flexibility for representing real-world decision and control problems but are notoriously difficult to solve.
An Iterative Quadratic Method for General-Sum Differential Games with Feedback Linearizable Dynamics
Iterative linear-quadratic (ILQ) methods are widely used in the nonlinear optimal control community.
Systems and Control Computer Science and Game Theory Multiagent Systems Robotics Systems and Control
PowerSGD: Powered Stochastic Gradient Descent Methods for Accelerated Non-Convex Optimization
In this paper, we propose a novel technique for improving the stochastic gradient descent (SGD) method to train deep networks, which we term \emph{PowerSGD}.
Closed-loop Model Selection for Kernel-based Models using Bayesian Optimization
In this work, we present a framework to optimize the kernel and hyperparameters of a kernel-based model directly with respect to the closed-loop performance of the model.
Efficient Iterative Linear-Quadratic Approximations for Nonlinear Multi-Player General-Sum Differential Games
We benchmark our method in a three-player general-sum simulated example, in which it takes < 0. 75 s to identify a solution and < 50 ms to solve warm-started subproblems in a receding horizon.
Systems and Control Robotics Systems and Control
An Efficient Reachability-Based Framework for Provably Safe Autonomous Navigation in Unknown Environments
Our safety method is planner-agnostic and provides guarantees for a variety of mapping sensors.
Robotics
Safely Probabilistically Complete Real-Time Planning and Exploration in Unknown Environments
We present a new framework for motion planning that wraps around existing kinodynamic planners and guarantees recursive feasibility when operating in a priori unknown, static environments.
Robotics Systems and Control
Probabilistically Safe Robot Planning with Confidence-Based Human Predictions
In order to safely operate around humans, robots can employ predictive models of human motion.
Context-Specific Validation of Data-Driven Models
We propose a context-specific validation framework to quantify the quality of a learned model based on a distance measure between the closed-loop actual system and the learned model.
Budget-Constrained Multi-Armed Bandits with Multiple Plays
Secondly, for the adversarial case in which the entire sequence of rewards and costs is fixed in advance, we derive an upper bound on the regret of order $O(\sqrt{NB\log(N/K)})$ utilizing an extension of the well-known $\texttt{Exp3}$ algorithm.
Planning, Fast and Slow: A Framework for Adaptive Real-Time Safe Trajectory Planning
Motion planning is an extremely well-studied problem in the robotics community, yet existing work largely falls into one of two categories: computationally efficient but with few if any safety guarantees, or able to give stronger guarantees but at high computational cost.
Systems and Control Computer Science and Game Theory
Hamilton-Jacobi Reachability: A Brief Overview and Recent Advances
Hamilton-Jacobi (HJ) reachability analysis is an important formal verification method for guaranteeing performance and safety properties of dynamical systems; it has been applied to many small-scale systems in the past decade.
Systems and Control Dynamical Systems Optimization and Control
Goal-Driven Dynamics Learning via Bayesian Optimization
Real-world robots are becoming increasingly complex and commonly act in poorly understood environments where it is extremely challenging to model or learn their true dynamics.
FaSTrack: a Modular Framework for Fast and Guaranteed Safe Motion Planning
We propose a new algorithm FaSTrack: Fast and Safe Tracking for High Dimensional systems.
Robotics
Using Neural Networks to Compute Approximate and Guaranteed Feasible Hamilton-Jacobi-Bellman PDE Solutions
To sidestep the curse of dimensionality when computing solutions to Hamilton-Jacobi-Bellman partial differential equations (HJB PDE), we propose an algorithm that leverages a neural network to approximate the value function.
On the Powerball Method for Optimization
We propose a new method to accelerate the convergence of optimization algorithms.
