Abstract
We study the problem of collision-free humanoid traversal in cluttered indoor scenes, such as hurdling over objects scattered on the floor, crouching under low-hanging obstacles, or squeezing through narrow passages. To achieve this goal, the humanoid needs to map its perception of surrounding obstacles with diverse spatial layouts and geometries to the corresponding traversal skills. However, the lack of an effective representation that captures humanoid-obstacle relationships during collision avoidance makes directly learning such mappings difficult. We therefore propose Humanoid Potential Field (HumanoidPF), which encodes these relationships as collision-free motion directions, significantly facilitating RL-based traversal skill learning. We also find that HumanoidPF exhibits a surprisingly negligible sim-to-real gap as a perceptual representation. To further enable generalizable traversal skills through diverse and challenging cluttered indoor scenes, we further propose a hybrid scene generation method, incorporating crops of realistic 3D indoor scenes and procedurally synthesized obstacles. We successfully transfer our policy to the real world and develop a teleoperation system where users could command the humanoid to traverse in cluttered indoor scenes with just a single click. Extensive experiments are conducted in both simulation and the real world to validate the effectiveness of our method. Demos and code can be found in our website: this https URL.
Abstract (translated)
我们研究了在充满障碍物的室内环境中实现人类机器人无碰撞行走的问题，包括跨越地面上散落的物体、低矮障碍物下蹲通过或穿过狭窄通道等情况。为了达到这一目标，人类机器人需要将其对周围障碍物的各种空间布局和几何形状的认知映射到相应的行走技能上。然而，在避免碰撞的过程中缺乏有效的表示方法来捕捉人形机器人与障碍物之间的关系，这使得直接学习这种映射变得困难。因此，我们提出了“人形势场”（HumanoidPF），它将这些关系编码为无碰撞运动方向，从而显著促进了基于强化学习的行走技能的学习过程。此外，我们发现作为感知表示方法，HumanoidPF在模拟环境与真实环境之间的差异非常小。 为了进一步使机器人能够在多样且具有挑战性的充满障碍物的室内环境中具备泛化的行走能力，我们还提出了一种混合场景生成方法，结合了现实世界3D室内环境的截屏以及程序合成的障碍物。我们成功地将策略转移到实际环境中，并开发了一个遥操作系统，在该系统中用户只需点击一下即可指挥人形机器人在充满障碍物的室内环境中行走。 我们在模拟和真实世界环境中进行了广泛的实验以验证我们的方法的有效性。演示视频和代码可以在我们的网站上找到：此链接。
URL
https://arxiv.org/abs/2601.16035
