research
∙
09/13/2023
Out of Distribution Detection via Domain-Informed Gaussian Process State Space Models
In order for robots to safely navigate in unseen scenarios using learnin...
share
research
∙
07/04/2023
Stranding Risk for Underactuated Vessels in Complex Ocean Currents: Analysis and Controllers
Low-propulsion vessels can take advantage of powerful ocean currents to ...
research
∙
07/04/2023
Maximizing Seaweed Growth on Autonomous Farms: A Dynamic Programming Approach for Underactuated Systems Navigating on Uncertain Ocean Currents
Seaweed biomass offers significant potential for climate mitigation, but...
research
∙
02/03/2023
Learning Players' Objectives in Continuous Dynamic Games from Partial State Observations
Robots deployed to the real world must be able to interact with other ag...
research
∙
10/10/2022
Generalized Optimality Guarantees for Solving Continuous Observation POMDPs through Particle Belief MDP Approximation
Partially observable Markov decision processes (POMDPs) provide a flexib...
research
∙
08/23/2022
Probabilistic Safe Online Learning with Control Barrier Functions
Learning-based control schemes have recently shown great efficacy perfor...
research
∙
03/18/2022
Infinite-Horizon Reach-Avoid Zero-Sum Games via Deep Reinforcement Learning
In this paper, we consider the infinite-horizon reach-avoid zero-sum gam...
research
∙
01/21/2022
Computation of Regions of Attraction for Hybrid Limit Cycles Using Reachability: An Application to Walking Robots
Contact-rich robotic systems, such as legged robots and manipulators, ar...
research
∙
12/23/2021
Safety and Liveness Guarantees through Reach-Avoid Reinforcement Learning
Reach-avoid optimal control problems, in which the system must reach cer...
research
∙
12/17/2021
Visual Learning-based Planning for Continuous High-Dimensional POMDPs
The Partially Observable Markov Decision Process (POMDP) is a powerful f...
research
∙
09/21/2021
Towards cyber-physical systems robust to communication delays: A differential game approach
Collaboration between interconnected cyber-physical systems is becoming ...
research
∙
09/10/2021
Discretizing Dynamics for Maximum Likelihood Constraint Inference
Maximum likelihood constraint inference is a powerful technique for iden...
research
∙
06/13/2021
Pointwise Feasibility of Gaussian Process-based Safety-Critical Control under Model Uncertainty
Control Barrier Functions (CBFs) and Control Lyapunov Functions (CLFs) a...
research
∙
06/07/2021
Inferring Objectives in Continuous Dynamic Games from Noise-Corrupted Partial State Observations
Robots and autonomous systems must interact with one another and their e...
research
∙
03/09/2021
Analyzing Human Models that Adapt Online
Predictive human models often need to adapt their parameters online from...
research
∙
02/14/2021
FaSTrack: a Modular Framework for Real-Time Motion Planning and Guaranteed Safe Tracking
Real-time, guaranteed safe trajectory planning is vital for navigation i...
research
∙
01/15/2021
Scalable Learning of Safety Guarantees for Autonomous Systems using Hamilton-Jacobi Reachability
Autonomous systems like aircraft and assistive robots often operate in s...
research
∙
12/18/2020
Voronoi Progressive Widening: Efficient Online Solvers for Continuous Space MDPs and POMDPs with Provably Optimal Components
Markov decision processes (MDPs) and partially observable MDPs (POMDPs) ...
research
∙
11/14/2020
Gaussian Process-based Min-norm Stabilizing Controller for Control-Affine Systems with Uncertain Input Effects
This paper presents a method to design a min-norm Control Lyapunov Funct...
research
∙
11/09/2020
Encoding Defensive Driving as a Dynamic Nash Game
Robots deployed in real-world environments should operate safely in a ro...
research
∙
11/01/2020
Approximate Solutions to a Class of Reachability Games
In this paper, we present a method for finding approximate Nash equilibr...
research
∙
09/07/2020
Dynamically Computing Adversarial Perturbations for Recurrent Neural Networks
Convolutional and recurrent neural networks have been widely employed to...
research
∙
04/16/2020
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...
research
∙
04/15/2020
Improving Input-Output Linearizing Controllers for Bipedal Robots via Reinforcement Learning
The main drawbacks of input-output linearizing controllers are the need ...
research
∙
04/06/2020
Technical Report: Adaptive Control for Linearizable Systems Using On-Policy Reinforcement Learning
This paper proposes a framework for adaptively learning a feedback linea...
research
∙
02/11/2020
Inference-Based Strategy Alignment for General-Sum Differential Games
In many settings where multiple agents interact, the optimal choices for...
research
∙
10/29/2019
A Hamilton-Jacobi Reachability-Based Framework for Predicting and Analyzing Human Motion for Safe Planning
Real-world autonomous systems often employ probabilistic predictive mode...
research
∙
10/29/2019
Feedback Linearization for Unknown Systems via Reinforcement Learning
We present a novel approach to control design for nonlinear systems, whi...
research
∙
10/10/2019
Sparse tree search optimality guarantees in POMDPs with continuous observation spaces
Partially observable Markov decision processes (POMDPs) with continuous ...
research
∙
10/01/2019
An Iterative Quadratic Method for General-Sum Differential Games with Feedback Linearizable Dynamics
Iterative linear-quadratic (ILQ) methods are widely used in the nonlinea...
research
∙
09/12/2019
Closed-loop Model Selection for Kernel-based Models using Bayesian Optimization
Kernel-based nonparametric models have become very attractive for model-...
research
∙
09/10/2019
Efficient Iterative Linear-Quadratic Approximations for Nonlinear Multi-Player General-Sum Differential Games
Differential games offer a powerful theoretical framework for formulatin...
research
∙
05/01/2019
An Efficient Reachability-Based Framework for Provably Safe Autonomous Navigation in Unknown Environments
Real-world autonomous vehicles often operate in a priori unknown environ...
research
∙
02/27/2019
A New Simulation Metric to Determine Safe Environments and Controllers for Systems with Unknown Dynamics
We consider the problem of extracting safe environments and controllers ...
research
∙
11/19/2018
Safe and Complete Real-Time Planning and Exploration in Unknown Environments
We present a new framework for motion planning that wraps around existin...
research
∙
11/14/2018
A Scalable Framework For Real-Time Multi-Robot, Multi-Human Collision Avoidance
Robust motion planning is a well-studied problem in the robotics literat...
research
∙
09/03/2018
A Minimum Discounted Reward Hamilton-Jacobi Formulation for Computing Reachable Sets
We propose a novel formulation for approximating reachable sets through ...
research
∙
08/02/2018
Robust Tracking with Model Mismatch for Fast and Safe Planning: an SOS Optimization Approach
In the pursuit of real-time motion planning, a commonly adopted practice...
research
∙
05/31/2018
Probabilistically Safe Robot Planning with Confidence-Based Human Predictions
In order to safely operate around humans, robots can employ predictive m...
research
∙
03/08/2018
Classification-based Approximate Reachability with Guarantees Applied to Safe Trajectory Tracking
Hamilton-Jacobi (HJ) reachability analysis has been developed over the p...
research
∙
02/14/2018
Context-Specific Validation of Data-Driven Models
With an increasing use of data-driven models to control robotic systems,...
research
∙
11/16/2017
Budget-Constrained Multi-Armed Bandits with Multiple Plays
We study the multi-armed bandit problem with multiple plays and a budget...
research
∙
10/12/2017
