为现实世界的人形机器人学习起身策略

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

在人形机器人可靠部署之前，自动摔倒恢复是一个至关重要的先决条件。手动设计起身控制器很困难，因为人形机器人在摔倒后可能出现各种配置，并且人形机器人预计在具有挑战性的地形上运行。本文开发了一个学习框架，用于生成控制器，使人形机器人能够从不同配置和不同地形中起身。与以往成功的人形机器人运动学习应用不同，起身任务涉及复杂的接触模式，这需要准确建模碰撞几何和更稀疏的奖励。我们通过遵循课程的两阶段方法来解决这些挑战。第一阶段侧重于在对平滑度或速度/扭矩限制最小的情况下发现一个良好的起身轨迹。然后，第二阶段将发现的动作优化为可部署的（即平稳且缓慢）动作，这些动作对初始配置和地形的变化具有鲁棒性。我们发现这些创新使得真实世界中的 G1 人形机器人能够从我们考虑的两种主要情况中起身：a）仰卧和b）俯卧，均在平坦、可变形、滑动表面和坡道（例如斜坡草地和雪地）上进行测试。据我们所知，这是首次成功展示了在现实世界中为人类大小的人形机器人学习起身策略。项目页面：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/