Galactic: Scaling End-to-End Reinforcement Learning for Rearrangement at
  100k Steps-Per-Second
Vincent-Pierre Berges Andrew Szot Devendra Singh Chaplot Aaron Gokaslan Roozbeh Mottaghi Dhruv Batra Eric Undersander
We present Galactic, a large-scale simulation and reinforcement-learning (RL)
framework for robotic mobile manipulation in indoor environments. Specifically,
a Fetch robot (equipped with a mobile base, 7DoF arm, RGBD camera, egomotion,
and onboard sensing) is spawned in a home environment and asked to rearrange
objects - by navigating to an object, picking it up, navigating to a target
location, and then placing the object at the target location.
  Galactic is fast. In terms of simulation speed (rendering + physics),
Galactic achieves over 421,000 steps-per-second (SPS) on an 8-GPU node, which
is 54x faster than Habitat 2.0 (7699 SPS). More importantly, Galactic was
designed to optimize the entire rendering + physics + RL interplay since any
bottleneck in the interplay slows down training. In terms of simulation+RL
speed (rendering + physics + inference + learning), Galactic achieves over
108,000 SPS, which 88x faster than Habitat 2.0 (1243 SPS).
  These massive speed-ups not only drastically cut the wall-clock training time
of existing experiments, but also unlock an unprecedented scale of new
experiments. First, Galactic can train a mobile pick skill to >80% accuracy in
under 16 minutes, a 100x speedup compared to the over 24 hours it takes to
train the same skill in Habitat 2.0. Second, we use Galactic to perform the
largest-scale experiment to date for rearrangement using 5B steps of experience
in 46 hours, which is equivalent to 20 years of robot experience. This scaling
results in a single neural network composed of task-agnostic components
achieving 85% success in GeometricGoal rearrangement, compared to 0% success
reported in Habitat 2.0 for the same approach. The code is available at
github.com/facebookresearch/galactic.