Learning Getting-Up Policies for Real-World Humanoid Robots
This addresses the crucial challenge of fall recovery for humanoid robots, enabling more reliable deployment in real-world environments, though it is incremental as it builds on prior learning methods for locomotion.
The paper tackled the problem of enabling humanoid robots to autonomously recover from falls by developing a learning framework that produces controllers for getting up from varied configurations and terrains, achieving successful real-world demonstrations on a G1 humanoid robot in situations like lying face up or down on surfaces such as grass and snowfields.
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 learning to humanoid locomotion, the getting-up task involves complex contact patterns (which necessitates accurately modeling of the collision geometry) and sparser rewards. We address these challenges through a two-phase approach that induces 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). This is one of the first successful demonstrations of learned getting-up policies for human-sized humanoid robots in the real world.