Learning Getting-Up Policies for Real-World Humanoid Robots
Authors: name: Xialin He, name: Runpei Dong, name: Zixuan Chen, name: Saurabh Gupta

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/