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

Robots navigating in human environments should use language to ask for assistance and be able to understand human responses. To study this challenge, we introduce Cooperative Vision-and-Dialog Navigation, a dataset of over 2k embodied, human-human dialogs situated in simulated, photorealistic home environments. The Navigator asks questions to their partner, the Oracle, who has privileged access to the best next steps the Navigator should take according to a shortest path planner. To train agents that search an environment for a goal location, we define the Navigation from Dialog History task. An agent, given a target object and a dialog history between humans cooperating to find that object, must infer navigation actions towards the goal in unexplored environments. We establish an initial, multi-modal sequence-to-sequence model and demonstrate that looking farther back in the dialog history improves performance. Sourcecode and a live interface demo can be found at https://cvdn.dev/

/pdf/vision-and-dialog-navigation-5bw749naaq.pdf

Vision-and-Dialog Navigation

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

A comprehensive survey on AR-enabled local collaboration

Choosing the best interaction modalities and protocols in Human-Robot Interaction (HRI) is far from being straightforward, as it strictly depends on the application domain, the tasks to be executed, the types of robots and sensors involved. In the last years, a growing number of HRI researchers exploited Virtual Reality (VR) as a mean to evaluate proposed solutions, focusing in particular on safety and correctness of collaborative tasks. This allows to prove the effectiveness and robustness of a certain approach in a simulated environment, thus permitting to converge more easily to the best solution, also avoiding to experiment potentially harmful actions in a real scenario. In this paper, we aim at reviewing existing VR based approaches targeting or embodying HRI.

A Preliminary Study on Virtual Reality Tools in Human-Robot Interaction

Collecting data in robotics, especially human-robot interactions, traditionally requires a physical robot in a prepared environment, that presents substantial scalability challenges. First, robots provide many possible points of system failure, while the availability of human participants is limited. Second, for tasks such as language learning, it is important to create environments that provide interesting’ varied use cases. Traditionally, this requires prepared physical spaces for each scenario being studied. Finally, the expense associated with acquiring robots and preparing spaces places serious limitations on the reproducible quality of experiments. We therefore propose a novel mechanism for using virtual reality to simulate robotic sensor data in a series of prepared scenarios. This allows for a reproducible dataset that other labs can recreate using commodity VR hardware. We demonstrate the effectiveness of this approach with an implementation that includes a simulated physical context, a reconstruction of a human actor, and a reconstruction of a robot. This evaluation shows that even a simple “sandbox” environment allows us to simulate robot sensor data, as well as the movement (e.g., view-port) and speech of humans interacting with the robot in a prescribed scenario.

/pdf/virtual-reality-and-photogrammetry-for-improved-4mc9b59wyp.pdf

Virtual Reality and Photogrammetry for Improved Reproducibility of Human-Robot Interaction Studies

There is increasing interest in using robots in simulation to understand and improve human-robot interaction (HRI). At the same time, the use of simulated settings to gather training data promises to help address a major data bottleneck in allowing robots to take advantage of powerful machine learning approaches. In this paper, we describe a prototype system that combines the robot operating system (ROS), the simulator Gazebo, and the Unity game engine to create human-robot interaction scenarios. A person can engage with the scenario using a monitor wall, allowing simultaneous collection of realistic sensor data and traces of human actions.

https://dl.acm.org/doi/pdf/10.1145/3306214.3338546

Learning from human-robot interactions in modeled scenes

We present an extension of the existing CoNavigator collaboration system that allows for persistence of in-person collaboration sessions through a digitally projected overlay and camera system. This augmented reality environment allows participants in a CoNavigator session to resume from a previous session without having to laboriously recreate the physical state of the space manually. By combining features traditionally found only in digital space with the intuitive nature of a tactile physical environment, we hope to produce a tool that builds on the work of CoNavigator and lowers barriers to adoption while increasing efficacy.

Extending CoNavigator into a Collaborative Digital Space

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Papers

A Simulator for Human-Robot Interaction in Virtual Reality

Virtual Reality and Photogrammetry for Improved Reproducibility of Human-Robot Interaction Studies

Learning from human-robot interactions in modeled scenes

Extending CoNavigator into a Collaborative Digital Space