Omnigrasp: 利用仿人机器人仿真抓取多样化物体
Omnigrasp: Grasping Diverse Objects with Simulated Humanoids
AI总结：
针对现有仿人机器人抓取方法适用范围有限的问题，提出利用类人运动表示训练控制器，可抓取超1200种物体并沿轨迹移动，在轨迹跟随和新物体泛化上达SOTA，无需成对运动轨迹数据集。
摘要：
我们提出了一种控制仿真仿人机器人抓取物体并使其沿目标轨迹移动的方法。由于控制带有灵巧双手的仿人机器人存在挑战，现有方法通常使用脱离躯体的手，且仅考虑垂直提升或短轨迹场景。这种有限的应用范围限制了它们在动画与仿真所需的物体操作任务中的适用性。为弥补这一不足，我们训练了一个控制器，能够抓取超过1200种物体，并携带它们沿随机生成的轨迹移动。我们的核心思路是利用一种仿人运动表示，该表示可提供类人运动技能并显著加快训练速度。仅使用简单的奖励、状态和物体表示，我们的方法在多样化物体和轨迹上展现出良好的可扩展性。训练阶段，我们不需要成对的全身运动与物体轨迹数据集。测试阶段，我们仅需物体网格模型以及抓取和运输的目标轨迹。为验证方法的性能，我们在物体轨迹跟随和未见过物体的泛化能力上取得了当前最优的成功率。代码和模型将公开发布。
英文摘要：
We present a method for controlling a simulated humanoid to grasp an object and move it to follow an object's trajectory. Due to the challenges in controlling a humanoid with dexterous hands, prior methods often use a disembodied hand and only consider vertical lifts or short trajectories. This limited scope hampers their applicability for object manipulation required for animation and simulation. To close this gap, we learn a controller that can pick up a large number (>1200) of objects and carry them to follow randomly generated trajectories. Our key insight is to leverage a humanoid motion representation that provides human-like motor skills and significantly speeds up training. Using only simplistic reward, state, and object representations, our method shows favorable scalability on diverse objects and trajectories. For training, we do not need a dataset of paired full-body motion and object trajectories. At test time, we only require the object mesh and desired trajectories for grasping and transporting. To demonstrate the capabilities of our method, we show state-of-the-art success rates in following object trajectories and generalizing to unseen objects. Code and models will be released.
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。
