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James Johnson

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5 papers
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5

AAAI Conference 2022 System Paper

A Goal-Driven Natural Language Interface for Creating Application Integration Workflows

  • Michelle Brachman
  • Christopher Bygrave
  • Tathagata Chakraborti
  • Arunima Chaudhary
  • Zhining Ding
  • Casey Dugan
  • David Gros
  • Thomas Gschwind

Web applications and services are increasingly important in a distributed internet filled with diverse cloud services and applications, each of which enable the completion of narrowly defined tasks. Given the explosion in the scale and diversity of such services, their composition and integration for achieving complex user goals remains a challenging task for endusers and requires a lot of development effort when specified by hand. We present a demonstration of the Goal Oriented Flow Assistant (GOFA) system, which provides a natural language solution to generate workflows for application integration. Our tool is built on a three-step pipeline: it first uses Abstract Meaning Representation (AMR) to parse utterances; it then uses a knowledge graph to validate candidates; and finally uses an AI planner to compose the candidate flow. We provide a video demonstration of the deployed system as part of our submission.

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IROS Conference 2021 Conference Paper

AquaVis: A Perception-Aware Autonomous Navigation Framework for Underwater Vehicles

  • Marios Xanthidis
  • Michail Kalaitzakis
  • Nare Karapetyan
  • James Johnson
  • Nikolaos I. Vitzilaios
  • Jason M. O'Kane
  • Ioannis M. Rekleitis

Visual monitoring operations underwater require both observing the objects of interest in close-proximity, and tracking the few feature-rich areas necessary for state estimation. This paper introduces the first navigation framework, called AquaVis, that produces on-line visibility-aware motion plans that enable Autonomous Underwater Vehicles (AUVs) to track multiple visual objectives with an arbitrary camera configuration in real-time. Using the proposed pipeline, AUVs can efficiently move in 3D, reach their goals while avoiding obstacles safely, and maximizing the visibility of multiple objectives along the path within a specified proximity. The method is sufficiently fast to be executed in real-time and is suitable for single or multiple camera configurations. Experimental results show the significant improvement on tracking multiple automatically-extracted points of interest, with low computational overhead and fast re-planning times. Accompanying short video: https://youtu.be/JKObbrIZyU

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IJCAI Conference 2021 Conference Paper

Mental Models of AI Agents in a Cooperative Game Setting (Extended Abstract)

  • Katy Ilonka Gero
  • Zahra Ashktorab
  • Casey Dugan
  • Qian Pan
  • James Johnson
  • Werner Geyer
  • Maria Ruiz
  • Sarah Miller

As more and more forms of AI become prevalent, it becomes increasingly important to understand how people develop mental models of these systems. In this work we study people's mental models of an AI agent in a cooperative word guessing game. We run a study in which people play the game with an AI agent while ``thinking out loud''; through thematic analysis we identify features of the mental models developed by participants. In a large-scale study we have participants play the game with the AI agent online and use a post-game survey to probe their mental model. We find that those who win more often have better estimates of the AI agent's abilities. We present three components---global knowledge, local knowledge, and knowledge distribution---for modeling AI systems and propose that understanding the underlying technology is insufficient for developing appropriate conceptual models---analysis of behavior is also necessary.

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ICRA Conference 2020 Conference Paper

Navigation in the Presence of Obstacles for an Agile Autonomous Underwater Vehicle

  • Marios Xanthidis
  • Nare Karapetyan
  • Hunter Damron
  • Sharmin Rahman
  • James Johnson
  • Allison O'Connell
  • Jason M. O'Kane
  • Ioannis M. Rekleitis

Navigation underwater traditionally is done by keeping a safe distance from obstacles, resulting in "fly-overs" of the area of interest. Movement of an autonomous underwater vehicle (AUV) through a cluttered space, such as a shipwreck or a decorated cave, is an extremely challenging problem that has not been addressed in the past. This paper proposes a novel navigation framework utilizing an enhanced version of Trajopt for fast 3D path-optimization planning for AUVs. A sampling-based correction procedure ensures that the planning is not constrained by local minima, enabling navigation through narrow spaces. Two different modalities are proposed: planning with a known map results in efficient trajectories through cluttered spaces; operating in an unknown environment utilizes the point cloud from the visual features detected to navigate efficiently while avoiding the detected obstacles. The proposed approach is rigorously tested, both on simulation and in-pool experiments, proven to be fast enough to enable safe real-time 3D autonomous navigation for an AUV.

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IROS Conference 2019 Conference Paper

Experimental Comparison of Open Source Visual-Inertial-Based State Estimation Algorithms in the Underwater Domain

  • Bharat Joshi
  • Nikolaos I. Vitzilaios
  • Ioannis M. Rekleitis
  • Sharmin Rahman
  • Michail Kalaitzakis
  • Brennan Cain
  • James Johnson
  • Marios Xanthidis

A plethora of state estimation techniques have appeared in the last decade using visual data, and more recently with added inertial data. Datasets typically used for evaluation include indoor and urban environments, where supporting videos have shown impressive performance. However, such techniques have not been fully evaluated in challenging conditions, such as the marine domain. In this paper, we compare ten recent open-source packages to provide insights on their performance and guidelines on addressing current challenges. Specifically, we selected direct and indirect methods that fuse camera and Inertial Measurement Unit (IMU) data together. Experiments are conducted by testing all packages on datasets collected over the years with underwater robots in our laboratory. All the datasets are made available online.

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