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1. MineObserver: A Deep Learning Framework for Assessing Natural Language Descriptions of Minecraft Imagery

   https://doi.org/10.32473/flairs.v35i.130729  

   Mahajan, Jay; Hum, Samuel; Ginger, Jeff; Lane, H. Chad (May 2022, The International FLAIRS Conference Proceedings)

   This paper introduces a novel approach for learning natural language descriptions of scenery in Minecraft. We apply techniques from Computer Vision and Natural Language Processing to create an AI framework called MineObserver for assessing the accuracy of learner-generated descriptions of science-related images. The ultimate purpose of the system is to automatically assess the accuracy of learner observations, written in natural language, made during science learning activities that take place in Minecraft. Eventually, MineObserver will be used as part of a pedagogical agent framework for providing in-game support for learning. Preliminary results are mixed, but promising with approximately 62% of images in our test set being properly classified by our image captioning approach. Broadly, our work suggests that computer vision techniques work as expected in Minecraft and can serve as a basis for assessing learner observations. 

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2. Unsupervised Selection of Negative Examples for Grounded Language Learning

   Pillai, Nisha; Matuszek, Cynthia (January 2018, Proceedings of the 32nd Conference on Artificial Intelligence (AAAI))

   There has been substantial work in recent years on grounded language acquisition, in which a model is learned that relates linguistic constructs to the perceivable world. While powerful, this approach is frequently hindered by ambiguities and omissions found in natural language. One such omission is the lack of negative descriptions of objects. We describe an unsupervised system that learns visual classifiers associated with words, using semantic similarity to automatically choose negative examples from a corpus of perceptual and linguistic data. We evaluate the effectiveness of each stage as well as the system's performance on the overall learning task. 

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3. A Multiview Approach to Learning Articulated Motion Models

   Daniele, Andrea F. (January 2017, Proceedings of the International Symposium on Robotics Research (ISRR))

   In order for robots to operate effectively in homes and workplaces, they must be able to manipulate the articulated objects common within environments built for and by humans. Kinematic models provide a concise representation of these objects that enable deliberate, generalizable manipulation policies. However, existing approaches to learning these models rely upon visual observations of an object's motion, and are subject to the effects of occlusions and feature sparsity. Natural language descriptions provide a flexible and efficient means by which humans can provide complementary information in a weakly supervised manner suitable for a variety of different interactions (e.g., demonstrations and remote manipulation). In this paper, we present a multimodal learning framework that incorporates both vision and language information acquired in situ to estimate the structure and parameters that define kinematic models of articulated objects. The visual signal takes the form of an RGB-D image stream that opportunistically captures object motion in an unprepared scene. Accompanying natural language descriptions of the motion constitute the linguistic signal. We model linguistic information using a probabilistic graphical model that grounds natural language descriptions to their referent kinematic motion. By exploiting the complementary nature of the vision and language observations, our method infers correct kinematic models for various multiple-part objects on which the previous state-of-the-art, visual-only system fails. We evaluate our multimodal learning framework on a dataset comprised of a variety of household objects, and demonstrate a 23% improvement in model accuracy over the vision-only baseline. 

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4. A Multiview Approach to Learning Articulated Motion Models

   Daniele, Andrea F.; Howard, Thomas M.; Walter, Matthew R. (November 2017, 2017 International Symposium on Robotics Research)

   In order for robots to operate effectively in homes and workplaces, they must be able to manipulate the articulated objects common within environments built for and by humans. Kinematic models provide a concise representation of these objects that enable deliberate, generalizable manipulation policies. However, existing approaches to learning these models rely upon visual observations of an object’s motion, and are subject to the effects of occlusions and feature sparsity. Natural language descriptions provide a flexible and efficient means by which humans can provide complementary information in a weakly supervised manner suitable for a variety of different interactions (e.g., demonstrations and remote manipulation). In this paper, we present a multimodal learning framework that incorporates both vision and language information acquired in situ to estimate the structure and parameters that de- fine kinematic models of articulated objects. The visual signal takes the form of an RGB-D image stream that opportunistically captures object motion in an unprepared scene. Accompanying natural language descriptions of the motion constitute the linguistic signal. We model linguistic information using a probabilistic graphical model that grounds natural language descriptions to their referent kinematic motion. By exploiting the complementary nature of the vision and language observations, our method infers correct kinematic models for various multiple-part objects on which the previous state-of-the- art, visual-only system fails. We evaluate our multimodal learning framework on a dataset comprised of a variety of household objects, and demonstrate a 23% improvement in model accuracy over the vision-only baseline. 

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5. Multicraft: A Multimodal Interface for Supporting and Studying Learning in Minecraft

   https://doi.org/10.1007/978-3-030-77414-1_10  

   Worsley, M.; Mendoza Tudares, K.; Mwiti, T.; Zhen, M.; Jiang, M. (July 2021, HCI in Games: Serious and Immersive Games. HCII 2021. Lecture Notes in Computer Science)

   In this paper, we present work on bringing multimodal interaction to Minecraft. The platform, Multicraft, incorporates speech-based input, eye tracking, and natural language understanding to facilitate more equitable gameplay in Minecraft. We tested the platform with elementary, middle school students and college students through a collection of studies. Students found each of the provided modalities to be a compelling way to play Minecraft. Additionally, we discuss the ways that these different types of multimodal data can be used to identify the meaningful spatial reasoning practices that students demonstrate while playing Minecraft. Collectively, this paper emphasizes the opportunity to bridge a multimodal interface with a means for collecting rich data that can better support diverse learners in non-traditional learning environments. 

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