用于反应式运动生成的正则化深度符号距离场
Regularized Deep Signed Distance Fields for Reactive Motion Generation
摘要：
自主机器人应该在真实世界的动态环境中运行，并在狭小的空间中与人类合作。允许机器人离开结构化实验室和制造环境的一个关键组成部分是它们能够在线和实时评估与周围世界的碰撞。基于距离的约束是使机器人能够计划其行动并安全行动的基础，从而保护人类及其硬件。然而，不同的应用程序需要不同的距离分辨率，导致各种启发式的方法来测量距离场w.r.t.障碍物，这在计算上是昂贵的，并且阻碍了它们在动态障碍物规避用例中的应用。我们提出了正则化深度符号距离场（RedSDF），这是一个单一的神经隐式函数，可以计算任何尺度的平滑距离场，在高维流形和人类等关节体上具有细粒度的分辨率，这要归功于我们有效的数据生成和训练过程中的简单归纳偏差。我们证明了我们的方法的有效性，在代表性的模拟任务的全身控制（WBC）和安全的人机交互（HRI）在共享的机器人。最后，我们提供了一个现实世界中的应用程序的概念证明HRI移交任务与移动机械手机器人。
英文摘要：
Autonomous robots should operate in real-world dynamic environments and collaborate with humans in tight spaces. A key component for allowing robots to leave structured lab and manufacturing settings is their ability to evaluate online and real-time collisions with the world around them. Distance-based constraints are fundamental for enabling robots to plan their actions and act safely, protecting both humans and their hardware. However, different applications require different distance resolutions, leading to various heuristic approaches for measuring distance fields w.r.t. obstacles, which are computationally expensive and hinder their application in dynamic obstacle avoidance use-cases. We propose Regularized Deep Signed Distance Fields (ReDSDF), a single neural implicit function that can compute smooth distance fields at any scale, with fine-grained resolution over high-dimensional manifolds and articulated bodies like humans, thanks to our effective data generation and a simple inductive bias during training. We demonstrate the effectiveness of our approach in representative simulated tasks for whole-body control (WBC) and safe Human-Robot Interaction (HRI) in shared workspaces. Finally, we provide proof of concept of a real-world application in a HRI handover task with a mobile manipulator robot.
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