title: Galactic: Scaling End-to-End Reinforcement Learning for Rearrangement at
    100k Steps-Per-Second
summary: 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.
paper_authors._id: 648941f837666e47d1dfc688
paper_authors.name: Vincent-Pierre Berges
paper_authors._id: 648941f837666e47d1dfc689
paper_authors.name: Andrew Szot
paper_authors._id: 648941f837666e47d1dfc68a
paper_authors.name: Devendra Singh Chaplot
paper_authors._id: 648941f837666e47d1dfc68b
paper_authors.name: Aaron Gokaslan
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-06-14T12:13:48.931Z
paper_authors._id: 620573d0522e40b4a18d8763
paper_authors.avatarUrl: /avatars/9353c064ef8ccac84d0397411d38fa90.svg
paper_authors.fullname: Aaron Gokaslan
paper_authors.type: user
paper_authors.user: Skylion007
paper_authors._id: 648941f837666e47d1dfc68c
paper_authors.name: Roozbeh Mottaghi
paper_authors._id: 648941f837666e47d1dfc68d
paper_authors.name: Dhruv Batra
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-06-14T12:14:20.962Z
paper_authors._id: 63ed1e57f765928ceeacbe02
paper_authors.avatarUrl: /avatars/e5a7428bb0acb43a240f1c4a8c1a5639.svg
paper_authors.fullname: Dhruv Batra
paper_authors.type: user
paper_authors.user: dbatra
paper_authors._id: 648941f837666e47d1dfc68e
paper_authors.name: Eric Undersander
paper_title: Galactic: Scaling End-to-End Reinforcement Learning for Rearrangement at
    100k Steps-Per-Second
paper_summary: 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.
paper_ai_summary: Galactic is a high-speed simulation and reinforcement learning framework for robotic manipulation tasks, achieving significant speedups and larger scale experiments compared to existing methods.
paper_ai_keywords: reinforcement-learning Fetch robot 7DoF arm RGBD camera egomotion onboard sensing home environment object rearrangement rendering physics interplay optimization simulation speed steps-per-second (SPS) rendering + physics + RL speed mobile pick skill GeometricGoal rearrangement