用物理约束神经网络实现高效抓取与重定位，无需专家数据
Physics-informed Neural Time Fields for Prehensile Object Manipulation
- 基于物理信息神经网络求解运动方程，无须人工标注数据
- 在复杂环境里快速规划路径，成功率高且轨迹更短
- 支持实时调整抓取策略，适合真实场景的机器人操作
机器人在日常生活场景（如仓库、医院）中需具备物体操纵能力，以在杂乱环境中将物体移动至目标位置。现有方法或依赖低效采样、需专家示范，或通过试错学习，难以满足实际应用需求。本文提出一种新型多模态物理信息神经网络（PINN），可无需专家数据高效求解等距方程，并在复杂杂乱环境中快速生成物体操纵轨迹。该方法具备多模态特性，可在操作过程中实时重规划抓取方式以达成目标姿态。我们在仿真和真实世界中验证了该方法，结果表明其对多种物体均有效，训练效率优于以往学习方法，在规划时间、轨迹长度和成功率方面表现优异。
原文摘要 · Abstract (English)
Object manipulation skills are necessary for robots operating in various daily-life scenarios, ranging from warehouses to hospitals. They allow the robots to manipulate the given object to their desired arrangement in the cluttered environment. The existing approaches to solving object manipulations are either inefficient sampling based techniques, require expert demonstrations, or learn by trial and error, making them less ideal for practical scenarios. In this paper, we propose a novel, multimodal physics-informed neural network (PINN) for solving object manipulation tasks. Our approach efficiently learns to solve the Eikonal equation without expert data and finds object manipulation trajectories fast in complex, cluttered environments. Our method is multimodal as it also reactively replans the robot's grasps during manipulation to achieve the desired object poses. We demonstrate our approach in both simulation and real-world scenarios and compare it against state-of-the-art baseline methods. The results indicate that our approach is effective across various objects, has efficient training compared to previous learning-based methods, and demonstrates high performance in planning time, trajectory length, and success rates. Our demonstration videos can be found at https://youtu.be/FaQLkTV9knI.
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