Claire J. Tomlin

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...

0 Lasse Peters, et al. ∙

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...

0 Michael H. Lim, et al. ∙

research

∙ 08/23/2022

Probabilistic Safe Online Learning with Control Barrier Functions

Learning-based control schemes have recently shown great efficacy perfor...

0 Fernando Castañeda, et al. ∙

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...

0 Jingqi Li, et al. ∙

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...

0 Jason J. Choi, et al. ∙

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...

0 Kai-Chieh Hsu, et al. ∙

research

∙ 12/17/2021

Visual Learning-based Planning for Continuous High-Dimensional POMDPs

The Partially Observable Markov Decision Process (POMDP) is a powerful f...

0 Sampada Deglurkar, et al. ∙

research

∙ 09/21/2021

Towards cyber-physical systems robust to communication delays: A differential game approach

Collaboration between interconnected cyber-physical systems is becoming ...

0 Shankar A. Deka, et al. ∙

research

∙ 09/10/2021

Discretizing Dynamics for Maximum Likelihood Constraint Inference

Maximum likelihood constraint inference is a powerful technique for iden...

0 Kaylene C. Stocking, et al. ∙

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...

0 Fernando Castañeda, et al. ∙

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...

0 Lasse Peters, et al. ∙

research

∙ 03/09/2021

Analyzing Human Models that Adapt Online

Predictive human models often need to adapt their parameters online from...

11 Andrea Bajcsy, et al. ∙

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...

0 Mo Chen, et al. ∙

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...

0 Sylvia Herbert, et al. ∙

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) ...

0 Michael H. Lim, et al. ∙

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...

0 Fernando Castañeda, et al. ∙

research

∙ 11/09/2020

Encoding Defensive Driving as a Dynamic Nash Game

Robots deployed in real-world environments should operate safely in a ro...

0 Chih-Yuan Chiu, et al. ∙

research

∙ 11/01/2020

Approximate Solutions to a Class of Reachability Games

In this paper, we present a method for finding approximate Nash equilibr...

0 David Fridovich-Keil, et al. ∙

research

∙ 09/07/2020

Dynamically Computing Adversarial Perturbations for Recurrent Neural Networks

Convolutional and recurrent neural networks have been widely employed to...

5 Shankar A. Deka, et al. ∙

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...

0 Jason Choi, et al. ∙

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 ...

0 Fernando Castañeda, et al. ∙

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...

5 Tyler Westenbroek, et al. ∙

research

∙ 02/11/2020

Inference-Based Strategy Alignment for General-Sum Differential Games

In many settings where multiple agents interact, the optimal choices for...

0 Lasse Peters, et al. ∙

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...

0 Somil Bansal, et al. ∙

research

∙ 10/29/2019

Feedback Linearization for Unknown Systems via Reinforcement Learning

We present a novel approach to control design for nonlinear systems, whi...

1 Tyler Westenbroek, et al. ∙

research

∙ 10/10/2019

Sparse tree search optimality guarantees in POMDPs with continuous observation spaces

Partially observable Markov decision processes (POMDPs) with continuous ...

0 Michael H. Lim, et al. ∙

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...

0 David Fridovich-Keil, et al. ∙

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-...

0 Thomas Beckers, et al. ∙

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...

0 David Fridovich-Keil, et al. ∙

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...

0 Andrea Bajcsy, et al. ∙

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 ...

0 Shromona Ghosh, et al. ∙

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...

0 David Fridovich-Keil, et al. ∙

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...

0 Andrea Bajcsy, et al. ∙

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 ...

0 Anayo K. Akametalu, et al. ∙

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...

0 Sumeet Singh, et al. ∙

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...

0 Jaime F. Fisac, et al. ∙

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...

0 Vicenc Rubies Royo, et al. ∙

research

∙ 02/14/2018

Context-Specific Validation of Data-Driven Models

With an increasing use of data-driven models to control robotic systems,...

0 Somil Bansal, et al. ∙

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...

0 Datong P. Zhou, et al. ∙

research

∙ 10/12/2017

Planning, Fast and Slow: A Framework for Adaptive Real-Time Safe Trajectory Planning

Motion planning is an extremely well-studied problem in the robotics com...

0 David Fridovich-Keil, et al. ∙

Claire J. Tomlin

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