发表机构
VinRobotics; Nanyang Technological University; VinRobotics JSC; Center for AI Research (CAIR), VinUniversity（VinRobotics; 南洋理工大学; VinRobotics股份公司; VinUniversity人工智能研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种共形播种混合逆运动学框架，结合解析候选生成、学习排序与数值细化，实现偏移冗余7自由度机械臂的实时高精度求解，成功率100%。
AI 中文摘要
本文提出了一种共形播种混合策略，用于求解人形类别的偏移、冗余7自由度机械臂的逆运动学。解析逆运动学（AIK）提供闭式解，计算成本极低。然而，对于偏移运动学结构，精确的闭式解通常不可用，实际AIK必须依赖于近似或简化的运动学模型。相比之下，数值逆运动学（NIK）可以在完整运动学模型上实现高精度解。然而，其收敛性对初始化高度敏感。为了克服这些限制，我们提出了一种具有共形校准播种选择的两阶段混合逆运动学框架。首先，一个近似的解析模型高效地枚举出有限数量的候选关节解。其次，我们使用一个轻量级的学习预测器对这些候选解进行排序，该预测器预测后细化难度，并通过分裂共形预测包装成校准的上界，作为选择分数。然后，使用Levenberg-Marquardt求解器在完整运动学模型上对排名最高的种子进行细化。所提出的方法结合了快速候选生成、具有校准难度界的学习种子排序以及准确的数值细化，在我们的评估中，对可达目标实现了小于40微秒的实时性能和100%的成功率。我们通过工作空间内的大规模随机模拟以及在人形机械臂上的运动规划实验演示来验证该方法。演示视频可在以下https URL获取。
英文摘要
This paper presents a conformal-seeded hybrid strategy for solving inverse kinematics of offset, redundant 7-DoF robot arms of the humanoid class. Analytical inverse kinematics (AIK) provides closed-form solutions with very low computational cost. However, for offset kinematic structures, the exact closed-form solution is generally unavailable, and practical AIK must rely on an approximate or simplified kinematic model. In contrast, numerical inverse kinematics (NIK) can achieve high-precision solutions on the full kinematic model. However, its convergence is highly sensitive to initialization. To overcome these limitations, we propose a two-stage hybrid inverse kinematics framework with conformal-calibrated seed selection. First, an approximate analytical model efficiently enumerates a finite set of candidate joint solutions. Second, we rank these candidates using a lightweight learned predictor of post-refinement difficulty, wrapped by split-conformal prediction into a calibrated upper bound that serves as the selection score. The best-ranked seed is then refined using a Levenberg-Marquardt solver on the full kinematic model. The proposed method combines fast candidate generation, learned seed ranking with a calibrated difficulty bound, and accurate numerical refinement, achieving real-time performance of less than 40us and a success rate of 100% in our evaluation on reachable targets. We validate the approach through large-scale stochastic simulation across the workspace and experimental demonstrations with motion planning on a humanoid robot arm. Demonstration videos are available at https://youtu.be/aeiBmw1XRbw.
