Facebook has scored an impressive feat involving AI that can navigate without any map. Facebook's wish for bragging rights, although they said they have a way to go, were evident in its blog post, "Near-perfect point-goal navigation from 2.5 billion frames of experience." Long story short, Facebook has delivered an algorithm that, quoting MIT Technology Review, lets robots find the shortest route in unfamiliar environments, opening the door to robots that can work inside homes and offices." And, in line with the plain-and-simple, Ubergizmo's Tyler Lee also remarked: "Facebook believes that with this new algorithm, it will be capable of creating robots that can navigate an area without the need for maps...in theory, you could place a robot in a room or an area without a map and it should be able to find its way to its destination." Erik Wijmans and Abhishek Kadian in the Facebook Jan. 21 post said that, well, after all, one of the technology key challenges is "teaching these systems to navigate through complex, unfamiliar real-world environments to reach a specified destination--without a preprovided map." Facebook has taken on the challenge. The two announced that Facebook AI created a large-scale distributed reinforcement learning algorithm called DD-PPO, "which has effectively solved the task of point-goal navigation using only an RGB-D camera, GPS, and compass data," they wrote. DD-PPO stands for decentralized distributed proximal policy optimization. This is what Facebook is using to train agents and results seen in virtual environments such as houses and office buildings were encouraging. The bloggers pointed out that "even failing 1 out of 100 times is not acceptable in the physical world, where a robot agent might damage itself or its surroundings by making an error." Beyond DD-PPO, the authors gave credit to Facebook AI's open source AI Habitat platform for its "state-of-the-art speed and fidelity." AI Habitat made its open source announcement last year as a simulation platform to train embodied agents such as virtual robots in photo-realistic 3-D environments. Facebook said it was part of "Facebook AI's ongoing effort to create systems that are less reliant on large annotated data sets used for supervised training." InfoQ had said in July that "The technology was taking a different approach than relying upon static data sets which other researchers have traditionally used and that Facebook decided to open-source this technology to move this subfield forward." Jon Fingas in Engadget looked at how the team worked toward AI navigation (and this is where that 25 billion number comes in). "Previous projects tend to struggle without massive computational power.

In a recent blog post, Facebook has announced they have open-sourced AI Habitat, an Artificial Intelligence (AI) simulation platform that is designed to train embodied agents, such as virtual robots. Using this technology, robots can learn how to grab an object from an adjacent room or assist a visually-impaired person in navigating an unfamiliar transit system. The technology leverages embodied AI which focuses on interactive environments to train real-world systems. This is a different approach than relying upon static data sets which other researchers have traditionally used. A team of Facebook researchers, including Manos Savva, Abhishek Kadian, Oleksandr Maksymets and Dhruv Batra, have released a research paper that demonstrates the capabilities of Al Habitat.

Replica can be loaded up in AI Habitat, a new open platform for embodied AI research. Facebook AI created AI Habitat to be the most powerful and flexible way for researchers to train and test AI bots in simulated living and working spaces. AI Habitat allows researchers to put a bot into a Replica environment, so it can learn to tackle different tasks, like "go check if my laptop is on my desk in the kitchen." These chores are simple for humans, but for machines to master them, they must recognize objects, understand language, and navigate effectively. Today's machines -- like robotic vacuums, for example -- can respond to commands, but they don't understand and adapt to the world around them as people do.

From a robot asked to "grab my phone from the desk upstairs" to a device that helps its visually impaired wearer navigate an unfamiliar subway system, the next generation of AI-powered assistants will need to demonstrate a broad range of abilities. Many researchers believe the most effective way to develop these skills is to focus on embodied AI, which uses interactive environments to ground systems' training in the real world, rather than relying on static data sets. To accelerate progress in this space, we're sharing AI Habitat, a new simulation platform created by Facebook AI that's designed to train embodied agents (such as virtual robots) in photo-realistic 3D environments. Our goal in sharing AI Habitat is to provide the most universal simulator to date for embodied research, with an open, modular design that's both powerful and flexible enough to bring reproducibility and standardized benchmarks to this subfield. To illustrate the benefits of this new platform, we're also sharing Replica, a data set of hyperrealistic 3D reconstructions of a staged apartment, retail store, and other indoor spaces that were generated by a group of scientists within Facebook Reality Labs (FRL).

Facebook created a new open platform for embodied AI research called AI Habitat, while Facebook Reality Labs (which up until last year was Oculus Research) released a dataset of photorealistic sample spaces it's calling Replica. Both Habitat and Replica are now available for researchers to download on Github. With these tools, researchers can train AI bots to act, see, talk, reason and plan simultaneously. The Replica data set is made of 18 different sample spaces, including a living room, conference room and two-story house. By training an AI bot to respond to a command like "bring my keys" in a Replica 3D simulation of a living room, researchers hope someday it can do the same with physical robots in a real-life living room.