Towards Realistic Embodied AI Agents

Recent years have witnessed the inception of a growing field of inquiry within the broader AI community termed as "Embodied AI". Problems studied under the umbrella of Embodied AI include the introduction of scene datasets and simulators to train AI agents to perform a wide spectrum of tasks requiring a curriculum of capabilities. While progress on this front has been commendable, it is nonetheless important and worthwhile to pause and carefully examine the real-world context under which such AI agents would be expected to operate. While doing so, it is critical to ensure "realism" i.e. the settings, parameters, and assumptions under which these agents and tasks are investigated in simulation indeed serve as the right test beds and high-fidelity precursors to the real world. Simulation has its own advantages of being fast, scalable/distributed, and safe and therefore, it is valuable to strive to make simulations more realistic. Towards that end, this thesis serves as an investigation into realism for Embodied AI agents in simulation. We study realism along 3 different axes. (1) Photorealism: The visual appearance of objects and rooms in indoor scenes, as viewed by the agent in simulation, must be a close approximation of what the agent would actually see in the real world. (2) Sensing and Actuation Realism: Embodied agents in simulation are often equipped with a variety of idealized sensors that provide highly privileged, noise-free sensing signals, depending on the task they are being trained for and take deterministic actions. This is in contrast to the dirty reality of noisy sensors and actuations in the real world. (3) Task Realism: Moving beyond realistic sensors and actuations, we need to ensure that the assumptions made while formulating tasks and the settings under which these tasks are being evaluated in simulation does indeed bode well with the deployment scenarios and use-cases in the real world. Finally, the thesis also explores connections between these different axes of realism.Ph.D

Embodied Question Answering

We present a new AI task -- Embodied Question Answering (EmbodiedQA) -- where an agent is spawned at a random location in a 3D environment and asked a question ("What color is the car?"). In order to answer, the agent must first intelligently navigate to explore the environment, gather information through first-person (egocentric) vision, and then answer the question ("orange"). This challenging task requires a range of AI skills -- active perception, language understanding, goal-driven navigation, commonsense reasoning, and grounding of language into actions. In this work, we develop the environments, end-to-end-trained reinforcement learning agents, and evaluation protocols for EmbodiedQA.Comment: 20 pages, 13 figures, Webpage: https://embodiedqa.org