从单视角点云直接生成23自由度抓取轨迹，实现高自由度机器人精准抓握。
23 DoF Grasping Policies from a Raw Point Cloud
- 基于二阶几何动力学建模，直接预测关节空间加速度。
- 在3种新物体上验证泛化能力，抓取成功率显著提升。
- 适合需要高精度、多自由度操作的机器人抓取场景。
协调高自由度（23 DoF）机器人抓取物体面临诸多挑战。本文提出一种新的模仿学习方法，直接从单个固定相机获取的局部点云中预测23自由度的抓取轨迹。核心是基于二阶几何的动力学行为模型——神经几何织构（NGF）策略，该策略在关节空间中直接预测加速度。实验表明，该策略能有效泛化到新物体，并与几何织构运动规划器结合形成闭环，生成稳定的抓取轨迹。我们在三类不同物体上评估了该方法，比较了不同策略结构，并进行了消融实验，以分析不同物体编码对策略学习的重要性。
原文摘要 · Abstract (English)
Coordinating the motion of robots with high degrees of freedom (DoF) to grasp objects gives rise to many challenges. In this paper, we propose a novel imitation learning approach to learn a policy that directly predicts 23 DoF grasp trajectories from a partial point cloud provided by a single, fixed camera. At the core of the approach is a second-order geometric-based model of behavioral dynamics. This Neural Geometric Fabric (NGF) policy predicts accelerations directly in joint space. We show that our policy is capable of generalizing to novel objects, and combine our policy with a geometric fabric motion planner in a loop to generate stable grasping trajectories. We evaluate our approach on a set of three different objects, compare different policy structures, and run ablation studies to understand the importance of different object encodings for policy learning.
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