Prof. Gregory J. Stein
RAIL Group, GMU

I am an Assistant Professor of Computer Science at George Mason University where I run the Robotic Anticipatory Intelligence & Learning (RAIL) Group. Our research, at the intersection of robotics and machine learning, is centered around developing representations that allow robots to better understand the impact of their actions, so that they may plan quickly and intelligently in a dynamic and uncertain world. Read more on our research page.

News

  • November 2021 Our paper Generating High-Quality Explanations for Navigation in Partially-Revealed Environments was accepted for a poster presentation at NeurIPS 2021. Video here!
  • September 2020 I gave a 5 minute lightning talk at the George Mason CS Research Day. Video here!
  • Fall 2020 I will be teaching CS 682, a graduate introduction to Computer Vision.
  • Fall 2020 I will be joining the Department of Computer Science at George Mason University as an Assistant Professor.
  • October 2018 My paper Learning over Subgoals for Efficient Navigation of Structured, Unknown Environments was a Best Paper Finalist at the 2018 Conference on Robot Learning. My accompanying 15 minute talk was awarded Best Oral Presentation:

Selected Publications

  • Gregory J. Stein "Generating High-Quality Explanations for Navigation in Partially-Revealed Environments" In: Advances in Neural Information Processing Systems (NeurIPS). 2021. talk (13 min), GitHub, blog post.
  • Christopher Bradley, Adam Pacheck, Gregory J. Stein, Sebastian Castro, Hadas Kress-Gazit, and Nicholas Roy. "Learning and Planning for Temporally Extended Tasks in Unknown Environments." In: International Conference on Robotics and Automation (ICRA). 2021. paper.
  • Gregory J. Stein, Christopher Bradley, Victoria Preston, and Nicholas Roy. "Enabling Topological Planning with Monocular Vision". In: International Conference on Robotics and Automation (ICRA). 2020. paper, talk (10 min).
  • Gregory J. Stein, Christopher Bradley, and Nicholas Roy. "Learning over Subgoals for Efficient Navigation of Structured, Unknown Environments". In: Conference on Robot Learning (CoRL). 2018. paper, talk (14 min).

    Best Paper Finalist at CoRL 2018; Best Oral Presentation at CoRL 2018.

  • Gregory J. Stein and Nicholas Roy. “GeneSIS-RT: Generating Synthetic Images for training Secondary Real-World Tasks”. In: International Conference on Robotics and Automation (ICRA). 2018. paper.
  • Mycal Tucker, Derya Aksaray, Rohan Paul, Gregory J. Stein, and Nicholas Roy. "Learning Unknown Groundings for Natural Language Interaction with Mobile Robots". In: International Symposium on Robotics Research (ISRR). 2017.

Blog Posts

  • Generating high-quality explanations for navigation in partially-revealed environments
    [Research] We generate explanations of a robot agent's behavior as it navigates through a partially-revealed environment, expressed in terms of changes to its predictions about what lies in unseen space. Blog post accompanying our NeurIPS 2021 paper.
  • Underpromise and overdeliver to your future self
    [Workflow & Process] I've started to think of expectation management as a part of self-care, and I try to think of myself as an other. As such, I try to underpromise and overdeliver to my future self.
  • Action items should have well-defined end conditions
    [Workflow & Process] I try to make sure that my tasks are both easy to start making progress towards and have clear completion criteria, essential criteria for making sure they get done.
  • Reviewing papers is incredibly valuable experience
    [Communication & Learning] Looking at the in-development work of others pulls back the curtain and reveals insight into the thought process of other researchers. Reviews are a largely-untapped pedagogical resource.
  • Accelerating team research with containers
    [Workflow & Process] All code in my research group is run exclusively inside Docker containers, helping us develop more quickly and share code with ease.
  • Machine Learning & Robotics: My (biased) 2019 State of the Field
    [Research] My thoughts on the past year of progress in Robotics and Machine Learning (2019).
  • DeepMind's AlphaZero and The Real World
    [Research] Using DeepMind's AlphaZero AI to solve real problems will require a change in the way computers represent and think about the world. In this post, we discuss how abstract models of the world can be used for better AI decision making and discuss recent work of ours that proposes such a model for the task of navigation.