59 citations · 225 across the 39 of their papers we have counts for
11 papers · 2 filters
Diverse Plausible Shape Completions from Ambiguous Depth Images
Brad Saund, Dmitry Berenson
We propose PSSNet, a network architecture for generating diverse plausible 3D reconstructions from a single 2.5D depth image. Existing methods tend to produce only small variations…
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…
Tracking Partially-Occluded Deformable Objects while Enforcing Geometric Constraints
Yixuan Wang, Dale McConachie, Dmitry Berenson
In order to manipulate a deformable object, such as rope or cloth, in unstructured environments, robots need a way to estimate its current shape. However, tracking the shape of a d…
TAMPC: A Controller for Escaping Traps in Novel Environments
Sheng Zhong, Zhenyuan Zhang, Nima Fazeli +1
We propose an approach to online model adaptation and control in the challenging case of hybrid and discontinuous dynamics where actions may lead to difficult-to-escape "trap" stat…
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…
Fast Planning Over Roadmaps via Selective Densification
Brad Saund, Dmitry Berenson
We propose the Selective Densification method for fast motion planning through configuration space. We create a sequence of roadmaps by iteratively adding configurations. We organi…
Learning When to Trust a Dynamics Model for Planning in Reduced State Spaces
Dale McConachie, Thomas Power, Peter Mitrano +1
When the dynamics of a system are difficult to model and/or time-consuming to evaluate, such as in deformable object manipulation tasks, motion planning algorithms struggle to find…
Manipulating Deformable Objects by Interleaving Prediction, Planning, and Control
Dale McConachie, Andrew Dobson, Mengyao Ruan +1
We present a framework for deformable object manipulation that interleaves planning and control, enabling complex manipulation tasks without relying on high-fidelity modeling or si…
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…