Search Results for author:
Found 37 papers, 6 papers with code
Adaptive Dual Covariance Steering with Active Parameter Estimation
A dual control problem is formulated in which the effect of the planned control policy on the parameter estimates is modeled and optimized for.
Keypoint-based Stereophotoclinometry for Characterizing and Navigating Small Bodies: A Factor Graph Approach
This paper proposes the incorporation of techniques from stereophotoclinometry (SPC) into a keypoint-based structure-from-motion (SfM) system to estimate the surface normal and albedo at detected landmarks to improve autonomous surface and shape characterization of small celestial bodies from in-situ imagery.
Beyond One Model Fits All: Ensemble Deep Learning for Autonomous Vehicles
Moreover, by exploring the significance of each modality, this study offers a roadmap for future research in autonomous driving, emphasizing the importance of leveraging multiple models to achieve robust performance.
Data-Driven Robust Covariance Control for Uncertain Linear Systems
The theory of covariance control and covariance steering (CS) deals with controlling the dispersion of trajectories of a dynamical system, under the implicit assumption that accurate prior knowledge of the system being controlled is available.
Computationally Efficient Chance Constrained Covariance Control with Output Feedback
This paper studies the problem of developing computationally efficient solutions for steering the distribution of the state of a stochastic, linear dynamical system between two boundary Gaussian distributions in the presence of chance-constraints on the state and control input.
LEA*: An A* Variant Algorithm with Improved Edge Efficiency for Robot Motion Planning
LEA* is simple and easy to implement with minimum modification to A*, resulting in a very small overhead compared to previous lazy search algorithms.
Desensitization and Deception in Differential Games with Asymmetric Information
The proposed feedback strategy is evaluated for instances involving a single pursuer and a single evader with an uncertain moving obstacle, where the pursuer is assumed to only know the nominal value of the obstacle's speed.
KARNet: Kalman Filter Augmented Recurrent Neural Network for Learning World Models in Autonomous Driving Tasks
For this purpose, deep learning models can be used to learn compact latent representations from a stream of incoming data.
IBBT: Informed Batch Belief Trees for Motion Planning Under Uncertainty
In this work, we propose the Informed Batch Belief Trees (IBBT) algorithm for motion planning under motion and sensing uncertainties.
Efficient Feature Description for Small Body Relative Navigation using Binary Convolutional Neural Networks
We train and test our models on real images of small bodies from legacy and ongoing missions and demonstrate increased performance relative to traditional handcrafted methods.
Data-Driven Covariance Steering Control Design
This paper studies the problem of steering the distribution of a linear time-invariant system from an initial normal distribution to a terminal normal distribution under no knowledge of the system dynamics.
Zero-Sum Games between Large-Population Teams: Reachability-based Analysis under Mean-Field Sharing
This work studies the behaviors of two large-population teams competing in a discrete environment.
Covariance Steering for Systems Subject to Unknown Parameters
This work considers the optimal covariance steering problem for systems subject to both additive noise and uncertain parameters which may enter multiplicatively with the state and the control.
Discrete-time Optimal Covariance Steering via Semidefinite Programming
Finally, a comparative study is performed in systems of various sizes and steering horizons to illustrate the advantages of the proposed method in terms of computational resources compared to the state of the art.
Deep Monocular Hazard Detection for Safe Small Body Landing
Hazard detection and avoidance is a key technology for future robotic small body sample return and lander missions.
Probabilistic Verification of ReLU Neural Networks via Characteristic Functions
Verifying the input-output relationships of a neural network so as to achieve some desired performance specification is a difficult, yet important, problem due to the growing ubiquity of neural nets in many engineering applications.
AstroSLAM: Autonomous Monocular Navigation in the Vicinity of a Celestial Small Body -- Theory and Experiments
We propose AstroSLAM, a standalone vision-based solution for autonomous online navigation around an unknown target small celestial body.
Optimal Covariance Steering for Discrete-Time Linear Stochastic Systems
In this paper, we study the optimal control problem for steering the state covariance of a discrete-time linear stochastic system over a finite time horizon.
On the Adversarial Convex Body Chasing Problem
In this work, we extend the convex bodies chasing problem (CBC) to an adversarial setting, where an agent (the Player) is tasked with chasing a sequence of convex bodies generated adversarially by another agent (the Opponent).
A Linear Programming Approach for Resource-Aware Information-Theoretic Tree Abstractions
It is shown that the resulting information-theoretic abstraction problem over the space of multi-resolution trees can be formulated as a integer linear programming (ILP) problem.
AstroVision: Towards Autonomous Feature Detection and Description for Missions to Small Bodies Using Deep Learning
Missions to small celestial bodies rely heavily on optical feature tracking for characterization of and relative navigation around the target body.
Optimal Covariance Steering for Continuous-Time Linear Stochastic Systems With Additive Noise
In this paper, we study the problem of how to optimally steer the state covariance of a general continuous-time linear stochastic system over a finite time interval subject to additive noise.
CARNet: A Dynamic Autoencoder for Learning Latent Dynamics in Autonomous Driving Tasks
Thus, there is a need for deep learning models that explicitly consider the temporal dependence of the data in their architecture.
Stochastic Entry Guidance
In this paper, closed-loop entry guidance in a randomly perturbed atmosphere, using bank angle control, is posed as a stochastic optimal control problem.
LES: Locally Exploitative Sampling for Robot Path Planning
Sampling-based algorithms solve the path planning problem by generating random samples in the search-space and incrementally growing a connectivity graph or a tree.
Robotics
Multi-Agent Consensus Subject to Communication and Privacy Constraints
The adversaries are able to listen to the inter-agent communications and try to estimate the state of the agents.
Information-Theoretic Abstractions for Resource-Constrained Agents via Mixed-Integer Linear Programming
In this paper, a mixed-integer linear programming formulation for the problem of obtaining task-relevant, multi-resolution, graph abstractions for resource-constrained agents is presented.
Chance-Constrained Covariance Steering in a Gaussian Random Field via Successive Convex Programming
The problem of optimizing affine feedback laws that explicitly steer the mean and covariance of an uncertain system state in the presence of a Gaussian random field is considered.
A Generalized A* Algorithm for Finding Globally Optimal Paths in Weighted Colored Graphs
We encode those properties in a weighted colored graph (geometric information in terms of edge weight and semantic information in terms of edge and vertex color), and propose a generalized A* to find the shortest path among the set of paths with minimal inclusion of low-ranked color edges.
Learning Nash Equilibria in Zero-Sum Stochastic Games via Entropy-Regularized Policy Approximation
We explore the use of policy approximations to reduce the computational cost of learning Nash equilibria in zero-sum stochastic games.
Information-Theoretic Abstractions for Planning in Agents with Computational Constraints
In this paper, we develop a framework for path-planning on abstractions that are not provided to the agent a priori but instead emerge as a function of the available computational resources.
Optimal Controller and Quantizer Selection for Partially Observable Linear-Quadratic-Gaussian Systems
In this paper, we consider joint optimal controller synthesis and quantizer scheduling for a partially observed Quantized-Feedback Linear-Quadratic-Gaussian (QF-LQG) system, where the measurements are quantized before being sent to the controller.
Q-Search Trees: An Information-Theoretic Approach Towards Hierarchical Abstractions for Agents with Computational Limitations
In this paper, we develop a framework to obtain graph abstractions for decision-making by an agent where the abstractions emerge as a function of the agent's limited computational resources.
Vision-Based Autonomous Vehicle Control using the Two-Point Visual Driver Control Model
This work proposes a new self-driving framework that uses a human driver control model, whose feature-input values are extracted from images using deep convolutional neural networks (CNNs).
Hierarchical State Abstractions for Decision-Making Problems with Computational Constraints
In this semi-tutorial paper, we first review the information-theoretic approach to account for the computational costs incurred during the search for optimal actions in a sequential decision-making problem.
Incremental Sampling-based Motion Planners Using Policy Iteration Methods
Contrary to the RRT* algorithm, the policy improvement during the rewiring step is not performed only locally but rather on a set of vertices that are classified as "promising" during the current iteration.
Sampling-based Algorithms for Optimal Motion Planning Using Closed-loop Prediction
We describe a new sampling-based algorithm, called CL-RRT#, which leverages ideas from the RRT# algorithm and a variant of the RRT algorithm that generates trajectories using closed-loop prediction.
Robotics
