Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Massive datasets and high-capacity models have driven many recent advancements in computer vision and natural language understanding. This work presents a platform to enable similar success stories in Embodied AI. We propose ProcTHOR, a framework for procedural generation of Embodied AI environments. ProcTHOR enables us to sample arbitrarily large datasets of diverse, interactive, customizable, and performant virtual environments to train and evaluate embodied agents across navigation, interaction, and manipulation tasks. We demonstrate the power and potential of ProcTHOR via a sample of 10,000 generated houses and a simple neural model. Models trained using only RGB images on ProcTHOR, with no explicit mapping and no human task supervision produce state-of-the-art results across 6 embodied AI benchmarks for navigation, rearrangement, and arm manipulation, including the presently running Habitat 2022, AI2-THOR Rearrangement 2022, and RoboTHOR challenges. We also demonstrate strong 0-shot results on these benchmarks, via pre-training on ProcTHOR with no fine-tuning on the downstream benchmark, often beating previous state-of-the-art systems that access the downstream training data.

ProcTHOR: Large-Scale Embodied AI Using Procedural Generation

While Autonomous Systems (ASes) are crucial for routing Internet traffic, organizations that own them are little understood. Regional Internet Registries (RIRs) inconsistently collect, release, and update basic AS organization information (e.g., website), and prior work provides only coarse-grained classification. Bootstrapping from RIR WHOIS data, we build ASdb, a system that uses data from established business intelligence databases and machine learning to accurately categorize ASes at scale. ASdb achieves 96% coverage of ASes, and 93% and 75% accuracy on 17 industry categories and 95 sub-categories, respectively. ASdb creates a more rich, accurate, comprehensive, and maintainable dataset cataloging AS-owning organizations. This system, and resulting dataset, will allow researchers to better understand who owns the Internet, and perform new forms of meaningful analysis and interpretation at scale.

ASdb: a system for classifying owners of autonomous systems

We present RCareWorld, a human-centric simulation world for physical and social robotic caregiving designed with inputs from stakeholders. RCareWorld has realistic human models of care recipients with mobility limitations and caregivers, home environments with multiple levels of accessibility and assistive devices, and robots commonly used for caregiving. It interfaces with various physics engines to model diverse material types necessary for simulating caregiving scenarios, and provides the capability to plan, control, and learn both human and robot control policies by integrating with state-of-the-art external planning and learning libraries, and VR devices. We propose a set of realistic caregiving tasks in RCareWorld as a benchmark for physical robotic caregiving and provide baseline control policies for them. We illustrate the high-fidelity simulation capabilities of RCareWorld by demonstrating the execution of a policy learnt in simulation for one of these tasks on a real-world setup. Additionally, we perform a real-world social robotic caregiving experiment using behaviors modeled in RCareWorld. Robotic caregiving, though potentially impactful towards enhancing the quality of life of care recipients and caregivers, is a field with many barriers to entry due to its interdisciplinary facets. RCareWorld takes the first step towards building a realistic simulation world for robotic caregiving that would enable researchers worldwide to contribute to this impactful field. Demo videos and supplementary materials can be found at: https://emprise.cs.cornell.edu/rcareworld/.

RCare World: A Human-centric Simulation World for Caregiving Robots

We present RCareWorld, a human-centric simulation world for physical and social robotic caregiving designed with inputs from stakeholders. RCareWorld has realistic human models of care recipients with mobility limitations and caregivers, home environments with multiple levels of accessibility and assistive devices, and robots commonly used for caregiving. It interfaces with various physics engines to model diverse material types necessary for simulating caregiving scenarios, and provides the capability to plan, control, and learn both human and robot control policies by integrating with state-of-the-art external planning and learning libraries, and VR devices. We propose a set of realistic caregiving tasks in RCareWorld as a benchmark for physical robotic caregiving and provide baseline control policies for them. We illustrate the high-fidelity simulation capabilities of RCareWorld by demonstrating the execution of a policy learnt in simulation for one of these tasks on a real-world setup. Additionally, we perform a real-world social robotic caregiving experiment using behaviors modeled in RCareWorld. Robotic caregiving, though potentially impactful towards enhancing the quality of life of care recipients and caregivers, is a field with many barriers to entry due to its interdisciplinary facets. RCareWorld takes the first step towards building a realistic simulation world for robotic caregiving that would enable researchers worldwide to contribute to this impactful field. Demo videos and supplementary materials can be found at: https://emprise.cs.cornell.edu/rcareworld/. 

/pdf/rcare-world-a-human-centric-simulation-world-for-caregiving-dnf2e1zm.pdf

For robots deployed in human-centric spaces, natural language promises an intuitive, natural interface. However, obtaining appropriate training data for grounded language in a variety of settings is a significant barrier. In this work, we describe using human-robot interactions in virtual reality to train a robot, combining fully simulated sensing and actuation with human interaction. We present the architecture of our simulator and our grounded language learning approach, then describe our intended initial experiments.

/pdf/towards-making-virtual-human-robot-interaction-a-reality-4fgjghdk2i.pdf

Towards Making Virtual Human-Robot Interaction a Reality

We present a suite of tools to model a robot, its sensors, and the surrounding environment in VR, with the goal of collecting training data for real-world robots. The virtual robot observes a rigged avatar created in our photogrammetry facility and embodying a VR user. We are particularly interested in verbal human/robot interactions, which can be combined with the robot’s sensor data for grounded language learning. Because virtual scenes, tasks, and robots are easily reconfigured compared to their physical analogs, our approach proves extremely versatile in preparing a wide range of robot scenarios for an array of use cases.

A Simulator for Human-Robot Interaction in Virtual Reality

35th Conference on Neural Information Processing Systems (NeurIPS 2021) Track on Datasets
and Benchmarks

/pdf/a-spoken-language-dataset-of-descriptions-for-speech-based-13vnvcnojg.pdf

A Spoken Language Dataset of Descriptions for Speech-Based Grounded Language Learning

—In this work, we posit that a user’s head pose can serve as a proxy for gaze in a VR object selection task. We describe a study in which participants were asked to describe a series of objects in a known order, providing approximate labels for the focus of attention. The participants’ head pose was then evaluated as a function of the position and orientation of the headset, and how closely that pose matched the location of known objects was calculated. The object that most closely matched the gaze was then evaluated using a mean reciprocal ranking. We demonstrate that using a concept of gaze derived from head pose can be used to effectively narrow the set of objects that are the target of participants’ attention.

Head Pose as a Proxy for Gaze in Virtual Reality

Modern robotics heavily relies on machine learning and has a growing need for training data. Advances and commercialization of virtual reality (VR) present an opportunity to use VR as a tool to gather such data for human-robot interactions. We present the Robot Interaction in VR simulator, which allows human participants to interact with simulated robots and environments in real-time. We are particularly interested in spoken interactions between the human and robot, which can be combined with the robot’s sensory data for language grounding. To demonstrate the utility of the simulator, we describe a study which investigates whether a user’s head pose can serve as a proxy for gaze in a VR object selection task. Participants were asked to describe a series of known objects, providing approximate labels for the focus of attention. We demonstrate that using a concept of gaze derived from head pose can be used to effectively narrow the set of objects that are the target of participants’ attention and linguistic descriptions.

Head Pose for Object Deixis in VR-Based Human-Robot Interaction

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