8 citations · 24 across the 12 of their papers we have counts for
6 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…
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…