无碰撞的人形机器人在杂乱室内场景中的移动
Collision-Free Humanoid Traversal in Cluttered Indoor Scenes
AI总结：
提出HumanoidPF势场方法，通过混合场景生成实现人形机器人在杂乱室内场景中的无碰撞移动，有效提升强化学习的训练效果。
摘要：
我们研究了在杂乱的室内场景中实现无碰撞的人形机器人移动的问题，例如跳过散落在地面的物体、在低矮的障碍物下蹲或通过狭窄的通道。为了实现这一目标，人形机器人需要将对其周围障碍物的感知与多样的空间布局和几何形状映射到相应的移动技能上。然而，缺乏一种有效表示来捕捉人形机器人与障碍物在避障过程中的关系，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），将这些关系编码为无碰撞的运动方向，显著促进了基于强化学习的移动技能学习。我们还发现，HumanoidPF作为一种感知表示，在仿真到现实的差距方面表现出令人惊讶的微小。为了进一步通过多样且具有挑战性的杂乱室内场景实现可推广的移动技能，我们进一步提出了混合场景生成方法，结合了现实3D室内场景的片段和程序生成的障碍物。我们成功地将我们的策略转移到现实世界，并开发了一个遥控系统，使用户只需单次点击即可控制人形机器人在杂乱的室内场景中移动。在仿真和现实世界中进行了大量实验以验证我们方法的有效性。演示和代码可在我们的网站上找到：https://axian12138.github.io/CAT/。
英文摘要：
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: https://axian12138.github.io/CAT/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
