Facebook AI gives maps the brushoff in helping robots find the way

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.