3 citations · 8 across the 5 of their papers we have counts for
8 papers
Explaining Multi-stage Tasks by Learning Temporal Logic Formulas from Suboptimal Demonstrations
Glen Chou, Necmiye Ozay, Dmitry Berenson
We present a method for learning multi-stage tasks from demonstrations by learning the logical structure and atomic propositions of a consistent linear temporal logic (LTL) formula…
Inferring Obstacles and Path Validity from Visibility-Constrained Demonstrations
Craig Knuth, Glen Chou, Necmiye Ozay +1
Many methods in learning from demonstration assume that the demonstrator has knowledge of the full environment. However, in many scenarios, a demonstrator only sees part of the env…
Scalable Computation of Controlled Invariant Sets for Discrete-Time Linear Systems with Input Delays
Zexiang Liu, Liren Yang, Necmiye Ozay
In this paper, we first propose a method that can efficiently compute the maximal robust controlled invariant set for discrete-time linear systems with pure delay in input. The key…
On Abstraction-Based Controller Design With Output Feedback
Rupak Majumdar, Necmiye Ozay, Anne-Kathrin Schmuck
We consider abstraction-based design of output-feedback controllers for dynamical systems with a finite set of inputs and outputs against specifications in linear-time temporal log…
Learning Constraints from Locally-Optimal Demonstrations under Cost Function Uncertainty
Glen Chou, Necmiye Ozay, Dmitry Berenson
We present an algorithm for learning parametric constraints from locally-optimal demonstrations, where the cost function being optimized is uncertain to the learner. Our method use…
Learning Parametric Constraints in High Dimensions from Demonstrations
Glen Chou, Necmiye Ozay, Dmitry Berenson
We present a scalable algorithm for learning parametric constraints in high dimensions from safe expert demonstrations. To reduce the ill-posedness of the constraint recovery probl…