為現實世界的人形機器人學習起身策略

Learning Getting-Up Policies for Real-World Humanoid Robots

February 17, 2025

作者: Xialin He, Runpei Dong, Zixuan Chen, Saurabh Gupta

cs.AI

摘要

在人形機器人可靠部署之前，自動恢復站立是一個至關重要的前提條件。由於人形機器人在跌倒後可能處於各種不同的配置，以及預期在具有挑戰性的地形上運行，因此手工設計用於起身的控制器是困難的。本文開發了一個學習框架，以產生控制器，使人形機器人能夠從不同配置和不同地形中起身。與先前成功應用於人形機器人運動學習的應用不同，起身任務涉及複雜的接觸模式，這需要準確建模碰撞幾何和較少的獎勵。我們通過一個遵循課程的兩階段方法來應對這些挑戰。第一階段專注於在對平滑度或速度/扭矩限制最小的情況下發現良好的起身軌跡。然後，第二階段將發現的動作精煉為可部署（即平滑且緩慢）的動作，對初始配置和地形的變化具有韌性。我們發現這些創新使得真實世界中的 G1 人形機器人能夠從我們考慮的兩個主要情況中起身：a）仰臥和b）俯臥，均在平坦、可變形、滑溜的表面和斜坡（例如斜坡草地和雪地）上進行測試。據我們所知，這是在真實世界中首次成功展示了人形機器人學習起身策略的示例。項目頁面：https://humanoid-getup.github.io/

English

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/