Search Results for author: Nicholas Roy

Found 31 papers, 4 papers with code

Language-Grounded Hierarchical Planning and Execution with Multi-Robot 3D Scene Graphs

no code implementations9 Jun 2025 Jared Strader, Aaron Ray, Jacob Arkin, Mason B. Peterson, Yun Chang, Nathan Hughes, Christopher Bradley, Yi Xuan Jia, Carlos Nieto-Granda, Rajat Talak, Chuchu Fan, Luca Carlone, Jonathan P. How, Nicholas Roy

In this paper, we introduce a multi-robot system that integrates mapping, localization, and task and motion planning (TAMP) enabled by 3D scene graphs to execute complex instructions expressed in natural language.

Language Modeling Language Modelling +3

Belief Roadmaps with Uncertain Landmark Evanescence

no code implementations29 Jan 2025 Erick Fuentes, Jared Strader, Ethan Fahnestock, Nicholas Roy

We refer to the propensity of a landmark to disappear as landmark evanescence.

Navigate

Semi-Supervised Neural Processes for Articulated Object Interactions

no code implementations28 Nov 2024 Emily Liu, Michael Noseworthy, Nicholas Roy

The scarcity of labeled action data poses a considerable challenge for developing machine learning algorithms for robotic object manipulation.

Object

PIETRA: Physics-Informed Evidential Learning for Traversing Out-of-Distribution Terrain

no code implementations4 Sep 2024 Xiaoyi Cai, James Queeney, Tong Xu, Aniket Datar, Chenhui Pan, Max Miller, Ashton Flather, Philip R. Osteen, Nicholas Roy, Xuesu Xiao, Jonathan P. How

Self-supervised learning is a powerful approach for developing traversability models for off-road navigation, but these models often struggle with inputs unseen during training.

Self-Supervised Learning

Real-World Deployment of a Hierarchical Uncertainty-Aware Collaborative Multiagent Planning System

no code implementations26 Apr 2024 Martina Stadler Kurtz, Samuel Prentice, Yasmin Veys, Long Quang, Carlos Nieto-Granda, Michael Novitzky, Ethan Stump, Nicholas Roy

In this paper, we describe the deployment of a planning system that used a hierarchy of planners to execute collaborative multiagent navigation tasks in real-world, unknown environments.

Navigate

Scaling Is All You Need: Autonomous Driving with JAX-Accelerated Reinforcement Learning

no code implementations23 Dec 2023 Moritz Harmel, Anubhav Paras, Andreas Pasternak, Nicholas Roy, Gary Linscott

However, running reinforcement learning experiments on the required scale for autonomous driving is extremely difficult.

All Autonomous Driving +2

EVORA: Deep Evidential Traversability Learning for Risk-Aware Off-Road Autonomy

2 code implementations10 Nov 2023 Xiaoyi Cai, Siddharth Ancha, Lakshay Sharma, Philip R. Osteen, Bernadette Bucher, Stephen Phillips, Jiuguang Wang, Michael Everett, Nicholas Roy, Jonathan P. How

For uncertainty quantification, we efficiently model both aleatoric and epistemic uncertainty by learning discrete traction distributions and probability densities of the traction predictor's latent features.

Uncertainty Quantification

AutoTAMP: Autoregressive Task and Motion Planning with LLMs as Translators and Checkers

3 code implementations10 Jun 2023 Yongchao Chen, Jacob Arkin, Charles Dawson, Yang Zhang, Nicholas Roy, Chuchu Fan

Rather than using LLMs to directly plan task sub-goals, we instead perform few-shot translation from natural language task descriptions to an intermediate task representation that can then be consumed by a TAMP algorithm to jointly solve the task and motion plan.

Motion Planning Task and Motion Planning +1

Structured Latent Variable Models for Articulated Object Interaction

no code implementations26 May 2023 Emily Liu, Michael Noseworthy, Nicholas Roy

In this paper, we investigate a scenario in which a robot learns a low-dimensional representation of a door given a video of the door opening or closing.

Object

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 Image Classification +3

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.

Object Referring Expression +1

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 Task and 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

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 +2

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 +1

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.

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