title: Learning Getting-Up Policies for Real-World Humanoid Robots
summary: Automatic fall recovery is a crucial prerequisite before humanoid robots can
  be reliably deployed. Hand-designing controllers for getting up is difficult
  because of the varied configurations a humanoid can end up in after a fall and
  the challenging terrains humanoid robots are expected to operate on. This paper
  develops a learning framework to produce controllers that enable humanoid
  robots to get up from varying configurations on varying terrains. Unlike
  previous successful applications of humanoid locomotion learning, the
  getting-up task involves complex contact patterns, which necessitates
  accurately modeling the collision geometry and sparser rewards. We address
  these challenges through a two-phase approach that follows a curriculum. The
  first stage focuses on discovering a good getting-up trajectory under minimal
  constraints on smoothness or speed / torque limits. The second stage then
  refines the discovered motions into deployable (i.e. smooth and slow) motions
  that are robust to variations in initial configuration and terrains. We find
  these innovations enable a real-world G1 humanoid robot to get up from two main
  situations that we considered: a) lying face up and b) lying face down, both
  tested on flat, deformable, slippery surfaces and slopes (e.g., sloppy grass
  and snowfield). To the best of our knowledge, this is the first successful
  demonstration of learned getting-up policies for human-sized humanoid robots in
  the real world. Project page: https://humanoid-getup.github.io/
paper_authors._id: 67b41ed52867282b4eb37ce4
paper_authors.name: Xialin He
paper_authors._id: 67b41ed52867282b4eb37ce5
paper_authors.name: Runpei Dong
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-18T09:31:13.178Z
paper_authors._id: 6201fc5d91d53938a6432fbf
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6201fc5d91d53938a6432fbf/VLs8ZYaZrop4KBpZn53fH.jpeg
paper_authors.fullname: Runpei Dong
paper_authors.type: user
paper_authors.user: RunpeiDong
paper_authors._id: 67b41ed52867282b4eb37ce6
paper_authors.name: Zixuan Chen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-19T15:44:49.892Z
paper_authors._id: 64a02b4c1396c67ac07798bb
paper_authors.avatarUrl: /avatars/9b1b4319edbac5faeb7586a4933791d2.svg
paper_authors.fullname: Eric Chen
paper_authors.type: user
paper_authors.user: zxuannn
paper_authors._id: 67b41ed52867282b4eb37ce7
paper_authors.name: Saurabh Gupta
paper_title: Learning Getting-Up Policies for Real-World Humanoid Robots
paper_summary: Automatic fall recovery is a crucial prerequisite before humanoid robots can
  be reliably deployed. Hand-designing controllers for getting up is difficult
  because of the varied configurations a humanoid can end up in after a fall and
  the challenging terrains humanoid robots are expected to operate on. This paper
  develops a learning framework to produce controllers that enable humanoid
  robots to get up from varying configurations on varying terrains. Unlike
  previous successful applications of humanoid locomotion learning, the
  getting-up task involves complex contact patterns, which necessitates
  accurately modeling the collision geometry and sparser rewards. We address
  these challenges through a two-phase approach that follows a curriculum. The
  first stage focuses on discovering a good getting-up trajectory under minimal
  constraints on smoothness or speed / torque limits. The second stage then
  refines the discovered motions into deployable (i.e. smooth and slow) motions
  that are robust to variations in initial configuration and terrains. We find
  these innovations enable a real-world G1 humanoid robot to get up from two main
  situations that we considered: a) lying face up and b) lying face down, both
  tested on flat, deformable, slippery surfaces and slopes (e.g., sloppy grass
  and snowfield). To the best of our knowledge, this is the first successful
  demonstration of learned getting-up policies for human-sized humanoid robots in
  the real world. Project page: https://humanoid-getup.github.io/
paper_ai_summary: A learning framework is developed to enable humanoid robots to automatically recover from falls on varied terrains by discovering and refining motion trajectories.
paper_ai_keywords: humanoid robots fall recovery learning framework getting-up controllers complex contact patterns collision geometry sparser rewards curriculum-based approach smoothness speed torque limits deployable motions real-world G1 humanoid robot deformable surfaces slippery surfaces slopes