34 citations · 39 across the 10 of their papers we have counts for
12 papers
Mathematical Program Networks
Forrest Laine
Mathematical Program Networks (MPNs) are introduced in this work. An MPN is a collection of interdependent Mathematical Programs (MPs) which are to be solved simultaneously, while…
Contingency Games for Multi-Agent Interaction
Lasse Peters, Andrea Bajcsy, Chih-Yuan Chiu +4
Contingency planning, wherein an agent generates a set of possible plans conditioned on the outcome of an uncertain event, is an increasingly popular way for robots to act under un…
Learning Mixed Strategies in Trajectory Games
Lasse Peters, David Fridovich-Keil, Laura Ferranti +3
In multi-agent settings, game theory is a natural framework for describing the strategic interactions of agents whose objectives depend upon one another's behavior. Trajectory game…
Incorporating Data Uncertainty in Object Tracking Algorithms
Anish Muthali, Forrest Laine, Claire Tomlin
Methodologies for incorporating the uncertainties characteristic of data-driven object detectors into object tracking algorithms are explored. Object tracking methods rely on measu…
The Computation of Approximate Generalized Feedback Nash Equilibria
Forrest Laine, David Fridovich-Keil, Chih-Yuan Chiu +1
We present the concept of a Generalized Feedback Nash Equilibrium (GFNE) in dynamic games, extending the Feedback Nash Equilibrium concept to games in which players are subject to…
Multi-Hypothesis Interactions in Game-Theoretic Motion Planning
Forrest Laine, David Fridovich-Keil, Chih-Yuan Chiu +1
We present a novel method for handling uncertainty about the intentions of non-ego players in dynamic games, with application to motion planning for autonomous vehicles. Equilibria…
Testing for Typicality with Respect to an Ensemble of Learned Distributions
Forrest Laine, Claire Tomlin
Methods of performing anomaly detection on high-dimensional data sets are needed, since algorithms which are trained on data are only expected to perform well on data that is simil…
Eyes-Closed Safety Kernels: Safety for Autonomous Systems Under Loss of Observability
Forrest Laine, Chiu-Yuan Chiu, Claire Tomlin
A framework is presented for handling a potential loss of observability of a dynamical system in a provably-safe way. Inspired by the fragility of data-driven perception systems us…
Real-time Funnel Generation for Restricted Motion Planning
Hadi Ravanbakhsh, Forrest Laine, Sanjit A. Seshia
In autonomous systems, a motion planner generates reference trajectories which are tracked by a low-level controller. For safe operation, the motion planner should account for inev…
The Parallelization of Riccati Recursion
Forrest Laine, Claire Tomlin
A method is presented for parallelizing the computation of solutions to discrete-time, linear-quadratic, finite-horizon optimal control problems, which we will refer to as LQR prob…
Efficient Computation of Feedback Control for Constrained Systems
Forrest Laine, Claire Tomlin
A method is presented for solving the discrete-time finite-horizon Linear Quadratic Regulator (LQR) problem subject to auxiliary linear equality constraints, such as fixed end-poin…
Learning Quadrotor Dynamics Using Neural Network for Flight Control
Somil Bansal, Anayo K. Akametalu, Frank J. Jiang +2
Traditional learning approaches proposed for controlling quadrotors or helicopters have focused on improving performance for specific trajectories by iteratively improving upon a n…