Facebook AI Introduces Habitat 2.0: Next-Generation Simulation Platform Provides Faster Training For AI Agents With Tactile Perception

Facebook recently announced Habitat 2.0, a next-generation simulation platform that lets AI researchers teach machines to navigate through photo-realistic 3D virtual environments and interact with objects just as they would in an actual kitchen or other commonly used space. With these tools at their disposal and without the need for expensive physical prototypes, future innovations can be tested before ever setting foot into reality!

Habitat 2.0 could be one of the fastest publicly available simulators of its kind that employs a human-like experience for AI agents to perform. This makes it possible for them to interact with items, drawers, and doors quickly within an accelerated space or time according to their predetermined goals, which are usually related to robotics research, so they can learn how humans think to give instructions on what they should do next by mimicking our own actions as closely as possible!

With Habitat 2.0, AI researchers can now build virtual robots that are able to perform tasks like stocking the fridge or fetching objects with high reliability and without having to rely on static 3D simulations anymore.

Introducing ReplicaCAD

Habitat 2.0’s new data set, ReplicaCAD, is a Replica mirror, which Facebook Reality Lab released previously for 3D environments and has been rebuilt to support the movement and manipulation of objects. Previously static 3D scans have now been converted into individual models with physical parameters as well as collision proxies shapes that can be used in training modules on how to move or manipulate different objects while still being safe from accidents like running into walls when trying to walk through them too quickly


When working on this new data set, we decided to enlist the help of 3D artists. Our team was tasked with recreating various spaces in Replica by making sure they had an exact representation of material composition and geometry and full attention paid towards how these objects can be used within those specific areas. Using this detailed recreation process and interactive components, users get an accurate sense of what it would feel like if they were actually there themselves!

We hope that the reconstructed ReplicaCAD data set, significantly improved speeds, and new Home Assistive Benchmarks in Habitat 2.0 will empower other research teams to train next-generation AI agents with even greater success. 

Source: https://ai.facebook.com/blog/habitat-20-training-home-assistant-robots-with-faster-simulation-and-new-benchmarks/

Github: https://github.com/facebookresearch/habitat-lab

Paper: https://arxiv.org/abs/2106.14405