用GPU加速的混合逆运动学求解器，速度快且能避开碰撞
HJCD-IK: GPU-Accelerated Inverse Kinematics through Batched Hybrid Jacobian Coordinate Descent
- 结合贪婪坐标下降与雅可比优化，实现快速初始化与精修
- 相比现有方法速度提升一个数量级，且始终找到无碰撞解
- 适合需要实时高精度机械臂控制的应用场景
逆运动学（IK）是机器人领域的核心问题，旨在寻找使末端执行器达到目标位姿（无碰撞）的关节配置。现代IK求解器面临根本性权衡：解析方法速度快但泛化能力差，基于数值优化的方法适用性强但易陷入局部极小值且计算开销大。为此，我们提出HJCD-IK，一种基于采样的GPU加速混合求解器。通过新颖的方向感知贪婪坐标下降初始化、雅可比驱动的精修以及并行碰撞检测过滤，该方法在速度和精度上相较当前最优解有数量级提升，稳定地在精度-延迟权衡曲线上找到无碰撞解，并生成多样化的高质量样本。我们在物理弗兰卡机械臂上验证了该求解器的有效性，并开源代码。
原文摘要 · Abstract (English)
Inverse Kinematics (IK) is a core problem in robotics, in which joint configurations are found to achieve a (collision-free) desired end-effector pose. Modern IK solvers face a fundamental trade-off: analytical methods are fast but lack generality, while numerical optimization-based methods are broadly applicable but prone to local minima and high computational costs. To overcome this challenge, we introduce HJCD-IK, a GPU-accelerated, sampling-based hybrid solver. By pairing a novel orientation-aware greedy coordinate descent initialization with Jacobian-based polishing and a parallel collision filter, our method achieves up to order-of-magnitude gains in speed and accuracy over state-of-the-art solvers, consistently finding collision-free solutions on the accuracy-latency Pareto frontier, while producing a diverse distribution of high-quality samples. We validate our solver on a physical Franka manipulator and release our code open-source.
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