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Learning Getting-Up Policies for Real-World Humanoid Robots
Source: https://arxiv.org/abs/2502.12152 Fetched: 2026-02-13 Type: Research Paper
Paper Information
- arXiv ID: 2502.12152
- Authors: Xialin He, Runpei Dong, Zixuan Chen, Saurabh Gupta
- Submission Date: February 17, 2025 (revised April 27, 2025)
- Venue: Robotics: Science and Systems (RSS) 2025
Abstract
The research addresses automatic fall recovery for humanoid robots through machine learning. The core challenge involves enabling robots to recover from varied fallen positions on different terrain types, which is difficult to hand-code due to complexity and environmental variability.
Methodology: Two-Phase Curriculum Approach
Phase 1 - Discovery
The framework identifies viable getting-up trajectories with minimal constraints on movement smoothness or torque requirements.
Phase 2 - Refinement
The discovered motions are optimized into deployable controllers that maintain smoothness, respect physical constraints, and remain robust across configuration and terrain variations.
The approach addresses two significant technical obstacles: complex contact dynamics requiring precise collision geometry modeling, and sparse reward signals inherent to this task.
Real-World Results on Unitree G1
The G1 humanoid robot successfully recovered from two primary fall configurations on multiple terrain conditions:
- Face-up and face-down positions
- Diverse surfaces: flat ground, deformable terrain, slippery surfaces, and inclined slopes (grass and snow)
Significance
This represents "one of the first successful demonstrations of learned getting-up policies for human-sized humanoid robots in the real world," advancing practical deployment capabilities for autonomous systems in unstructured environments.