用点云数据直接推断多指机械手关节配置，速度快且精度高。
Leveraging CVAE for Joint Configuration Estimation of Multifingered Grippers from Point Cloud Data
- 基于条件变分自编码器，从点云重建关节配置
- 0.05毫秒内完成推理，精度媲美顶尖方法
- 适合机器人抓取规划中的实时关节状态估计
本文提出一种高效方法，仅通过视觉传感器、仿真或生成神经网络产生的多指机械手多关节链点云数据，即可确定其关节配置。传统逆运动学方法虽能精确求解指尖位姿对应的关节角，但常需额外决策中间指节位置，或依赖数值逼近复杂运动学。本方法利用机器学习隐式解决上述问题，采用条件变分自编码器（CVAE），以关键结构元素的点云为输入，重建对应关节配置。在MultiDex抓取数据集上，使用Allegro手型验证，推理时间低于0.05毫秒，精度与当前最优方法相当。结果表明该流水线在人工智能驱动的抓取规划中具有显著有效性。
原文摘要 · Abstract (English)
This paper presents an efficient approach for determining the joint configuration of a multifingered gripper solely from the point cloud data of its poly-articulated chain, as generated by visual sensors, simulations or even generative neural networks. Well-known inverse kinematics (IK) techniques can provide mathematically exact solutions (when they exist) for joint configuration determination based solely on the fingertip pose, but often require post-hoc decision-making by considering the positions of all intermediate phalanges in the gripper's fingers, or rely on algorithms to numerically approximate solutions for more complex kinematics. In contrast, our method leverages machine learning to implicitly overcome these challenges. This is achieved through a Conditional Variational Auto-Encoder (CVAE), which takes point cloud data of key structural elements as input and reconstructs the corresponding joint configurations. We validate our approach on the MultiDex grasping dataset using the Allegro Hand, operating within 0.05 milliseconds and achieving accuracy comparable to state-of-the-art methods. This highlights the effectiveness of our pipeline for joint configuration estimation within the broader context of AI-driven techniques for grasp planning.
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