Search Results for author: Nicholas Roy

Found 19 papers, 1 papers with code

Active Learning of Abstract Plan Feasibility

no code implementations1 Jul 2021 Michael Noseworthy, Caris Moses, Isaiah Brand, Sebastian Castro, Leslie Kaelbling, Tomás Lozano-Pérez, Nicholas Roy

Long horizon sequential manipulation tasks are effectively addressed hierarchically: at a high level of abstraction the planner searches over abstract action sequences, and when a plan is found, lower level motion plans are generated.

Active Learning

Online Descriptor Enhancement via Self-Labelling Triplets for Visual Data Association

no code implementations6 Nov 2020 Yorai Shaoul, Katherine Liu, Kyel Ok, Nicholas Roy

We show that self-labelling challenging triplets--choosing positive examples separated by large temporal distances and negative examples close in the descriptor space--improves the quality of the learned descriptors for the multi-object tracking task.

Image Classification Multi-Object Tracking +1

Leveraging Past References for Robust Language Grounding

no code implementations CONLL 2019 Subhro Roy, Michael Noseworthy, Rohan Paul, Daehyung Park, Nicholas Roy

We therefore reframe the grounding problem from the perspective of coreference detection and propose a neural network that detects when two expressions are referring to the same object.

Referring Expression Visual Grounding

Admissible Abstractions for Near-optimal Task and Motion Planning

no code implementations3 Jun 2018 William Vega-Brown, Nicholas Roy

We define an admissibility condition for abstractions expressed using angelic semantics and show that these conditions allow us to accelerate planning while preserving the ability to find the optimal motion plan.

Motion Planning

FLaME: Fast Lightweight Mesh Estimation Using Variational Smoothing on Delaunay Graphs

no code implementations ICCV 2017 W. Nicholas Greene, Nicholas Roy

We propose a lightweight method for dense online monocular depth estimation capable of reconstructing 3D meshes on computationally constrained platforms.

Monocular Depth Estimation

PROBE-GK: Predictive Robust Estimation using Generalized Kernels

no code implementations1 Aug 2017 Valentin Peretroukhin, William Vega-Brown, Nicholas Roy, Jonathan Kelly

Many algorithms in computer vision and robotics make strong assumptions about uncertainty, and rely on the validity of these assumptions to produce accurate and consistent state estimates.

Bayesian Inference Computer Vision

Structural Return Maximization for Reinforcement Learning

no code implementations12 May 2014 Joshua Joseph, Javier Velez, Nicholas Roy

Batch Reinforcement Learning (RL) algorithms attempt to choose a policy from a designer-provided class of policies given a fixed set of training data.

Learning Theory reinforcement-learning

Modelling Observation Correlations for Active Exploration and Robust Object Detection

no code implementations18 Jan 2014 Javier Velez, Garrett Hemann, Albert S. Huang, Ingmar Posner, Nicholas Roy

In particular, the performance of detection algorithms is commonly sensitive to the position of the sensor relative to the objects in the scene.

object-detection Robust Object Detection

Efficient Planning under Uncertainty with Macro-actions

no code implementations16 Jan 2014 Ruijie He, Emma Brunskill, Nicholas Roy

We also demonstrate our algorithm being used to control a real robotic helicopter in a target monitoring experiment, which suggests that our approach has practical potential for planning in real-world, large partially observable domains where a multi-step lookahead is required to achieve good performance.

Batch-iFDD for Representation Expansion in Large MDPs

no code implementations26 Sep 2013 Alborz Geramifard, Thomas J. Walsh, Nicholas Roy, Jonathan How

Matching pursuit (MP) methods are a promising class of feature construction algorithms for value function approximation.

Nonparametric Bayesian Policy Priors for Reinforcement Learning

no code implementations NeurIPS 2010 Finale Doshi-Velez, David Wingate, Nicholas Roy, Joshua B. Tenenbaum

We consider reinforcement learning in partially observable domains where the agent can query an expert for demonstrations.


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