49 citations · 103 across the 18 of their papers we have counts for
5 papers · 1 filter
Uncertainty-Aware Constraint Learning for Adaptive Safe Motion Planning from Demonstrations
Glen Chou, Necmiye Ozay, Dmitry Berenson
We present a method for learning to satisfy uncertain constraints from demonstrations. Our method uses robust optimization to obtain a belief over the potentially infinite set of p…
Planning with Learned Dynamics: Probabilistic Guarantees on Safety and Reachability via Lipschitz Constants
Craig Knuth, Glen Chou, Necmiye Ozay +1
We present a method for feedback motion planning of systems with unknown dynamics which provides probabilistic guarantees on safety, reachability, and goal stability. To find a dom…
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…
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…