让真人大小机器人学会在各种地形上自动爬起。
Learning Getting-Up Policies for Real-World Humanoid Robots
- 分两阶段学习：先找可行起身路径，再优化为平滑可部署动作
- 实测在平坦、柔软、湿滑及斜坡地面都能从趴/仰姿态成功起身
- 适合研究机器人自适应控制与真实场景部署的开发者
自动跌倒恢复是人形机器人可靠部署的关键前提。由于跌倒后姿态多样且需应对复杂地形，手动设计起身控制器极为困难。本文提出一种学习框架，使机器人能从不同姿态在多种地形上自主起身。不同于以往用于步态学习的方法，起身任务涉及复杂接触模式（需精确建模碰撞几何）和稀疏奖励。我们采用两阶段方法引入课程学习：第一阶段在最小约束下探索可行起身轨迹；第二阶段将轨迹优化为平滑、缓慢且对初始状态和地形变化鲁棒的可部署动作。实验表明，该方法使真实世界的G1人形机器人成功在平坦、可变形、滑腻表面及斜坡（如泥地、雪地）上实现面朝上和面朝下两种姿态的起身。这是首个在真实世界中成功演示的人形机器人学习起身策略的案例。
原文摘要 · Abstract (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 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.
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