Fast inverse kinematics (IK) is a central component in robotic motion planning. For complex robots, IK methods are often based on root search and non-linear optimization algorithms. These algorithms can be massively sped up using a neural network to predict a good initial guess, which can then be refined in a few numerical iterations. Besides previous work on learning-based IK, we present a learning approach for the fundamentally more complex problem of IK with collision avoidance. We do this in diverse and previously unseen environments. From a detailed analysis of the IK learning problem, we derive a network and unsupervised learning architecture that removes the need for a sample data generation step. Using the trained network's prediction as an initial guess for a two-stage Jacobian-based solver allows for fast and accurate computation of the collision-free IK. For the humanoid robot, Agile Justin (19 DoF), the collision-free IK is solved in less than 10 milliseconds (on a single CPU core) and with an accuracy of 10^-4 m and 10^-3 rad based on a high-resolution world model generated from the robot's integrated 3D sensor. Our method massively outperforms a random multi-start baseline in a benchmark with the 19 DoF humanoid and challenging 3D environments. It requires ten times less training time than a supervised training method while achieving comparable results.

翻译：快速逆运动学（IK）是机器人运动规划的核心组成部分。对于复杂机器人而言，IK方法通常基于根搜索和非线性优化算法。利用神经网络预测良好的初始猜测值，再通过少量数值迭代进行精化，可大幅加速此类算法。在现有基于学习的IK研究工作基础上，我们提出了一种面向更复杂问题的学习方法——具有避碰功能的IK，并在多样化且未见过的环境中实现。通过对IK学习问题的详细分析，我们推导出一种网络及无监督学习架构，从而消除了样本数据生成步骤的需求。将训练网络预测值作为两阶段雅可比求解器的初始猜测值，可快速、精确地计算无碰撞IK。针对类人机器人Agile Justin（19自由度），基于其集成3D传感器生成的高分辨率世界模型，无碰撞IK可在不到10毫秒（单CPU核心）内求解，精度达到10^-4米和10^-3弧度。在包含19自由度类人机器人和挑战性3D环境的基准测试中，我们的方法大幅超越了随机多起点基线方法。与监督训练方法相比，该方法在达到可比结果的同时，训练时间降低十倍。