2 citations · 5 across the 12 of their papers we have counts for
13 papers
SemNav: A Model-Based Planner for Zero-Shot Object Goal Navigation Using Vision-Foundation Models
Arnab Debnath, Gregory J. Stein, Jana Kosecka
Object goal navigation is a fundamental task in embodied AI, where an agent is instructed to locate a target object in an unexplored environment. Traditional learning-based methods…
A Hybrid Approach to Indoor Social Navigation: Integrating Reactive Local Planning and Proactive Global Planning
Arnab Debnath, Gregory J. Stein, Jana Kosecka
We consider the problem of indoor building-scale social navigation, where the robot must reach a point goal as quickly as possible without colliding with humans who are freely movi…
Active Information Gathering for Long-Horizon Navigation Under Uncertainty by Learning the Value of Information
Raihan Islam Arnob, Gregory J. Stein
We address the task of long-horizon navigation in partially mapped environments for which active gathering of information about faraway unseen space is essential for good behavior.…
Improving Reliable Navigation under Uncertainty via Predictions Informed by Non-Local Information
Raihan Islam Arnob, Gregory J. Stein
We improve reliable, long-horizon, goal-directed navigation in partially-mapped environments by using non-locally available information to predict the goodness of temporally-extend…
Guided Sampling-Based Motion Planning with Dynamics in Unknown Environments
Abhish Khanal, Hoang-Dung Bui, Gregory J. Stein +1
Despite recent progress improving the efficiency and quality of motion planning, planning collision-free and dynamically-feasible trajectories in partially-mapped environments rema…
Anticipatory Planning: Improving Long-Lived Planning by Estimating Expected Cost of Future Tasks
Roshan Dhakal, Md Ridwan Hossain Talukder, Gregory J. Stein
We consider a service robot in a household environment given a sequence of high-level tasks one at a time. Most existing task planners, lacking knowledge of what they may be asked…
Data-Efficient Policy Selection for Navigation in Partial Maps via Subgoal-Based Abstraction
Abhishek Paudel, Gregory J. Stein
We present a novel approach for fast and reliable policy selection for navigation in partial maps. Leveraging the recent learning-augmented model-based Learning over Subgoals Plann…
Learning Augmented, Multi-Robot Long-Horizon Navigation in Partially Mapped Environments
Abhish Khanal, Gregory J. Stein
We present a novel approach for efficient and reliable goal-directed long-horizon navigation for a multi-robot team in a structured, unknown environment by predicting statistics of…
Comparison of Model-Free and Model-Based Learning-Informed Planning for PointGoal Navigation
Yimeng Li, Arnab Debnath, Gregory J. Stein +1
In recent years several learning approaches to point goal navigation in previously unseen environments have been proposed. They vary in the representations of the environments, pro…
Learning-Augmented Model-Based Planning for Visual Exploration
Yimeng Li, Arnab Debnath, Gregory Stein +1
We consider the problem of time-limited robotic exploration in previously unseen environments where exploration is limited by a predefined amount of time. We propose a novel explor…
Learning and Planning for Temporally Extended Tasks in Unknown Environments
Christopher Bradley, Adam Pacheck, Gregory J. Stein +3
We propose a novel planning technique for satisfying tasks specified in temporal logic in partially revealed environments. We define high-level actions derived from the environment…
Enabling Topological Planning with Monocular Vision
Gregory J. Stein, Christopher Bradley, Victoria Preston +1
Topological strategies for navigation meaningfully reduce the space of possible actions available to a robot, allowing use of heuristic priors or learning to enable computationally…
GeneSIS-RT: Generating Synthetic Images for training Secondary Real-world Tasks
Gregory J. Stein, Nicholas Roy
We propose a novel approach for generating high-quality, synthetic data for domain-specific learning tasks, for which training data may not be readily available. We leverage recent…