When humans perform insertion tasks such as inserting a cup into a cupboard, routing a cable, or key insertion, they wiggle the object and observe the process through tactile and proprioceptive feedback. While recent advances in tactile sensors have resulted in tactile-based approaches, there has not been a generalized formulation based on wiggling similar to human behavior. Thus, we propose an extremum-seeking control law that can insert four keys into four types of locks without control parameter tuning despite significant variation in lock type. The resulting model-free formulation wiggles the end effector pose to maximize insertion depth while minimizing strain as measured by a GelSight Mini tactile sensor that grasps a key. The algorithm achieves a 71\% success rate over 120 randomly initialized trials with uncertainty in both translation and orientation. Over 240 deterministically initialized trials, where only one translation or rotation parameter is perturbed, 84\% of trials succeeded. Given tactile feedback at 13 Hz, the mean insertion time for these groups of trials are 262 and 147 seconds respectively.

翻译：当人类执行插入任务时，例如将杯子放入橱柜、布设线缆或插入钥匙，他们会摆动对象并通过触觉与本体感觉反馈观察过程。尽管触觉传感器的最新进展催生了基于触觉的方法，但尚未出现类似人类摆动行为的通用化公式化方法。为此，我们提出一种极值搜索控制律，该控制律能够在锁具类型差异显著的情况下，无需调整控制参数即可将四把钥匙插入四种类型的锁具。所提出的无模型方法通过摆动末端执行器位姿，以最大化插入深度，同时最小化由抓持钥匙的 GelSight Mini 触觉传感器测量的应变。该算法在120次随机初始化（平移和朝向均存在不确定性）的试验中取得了71%的成功率。在240次确定性初始化（仅扰动一个平移或旋转参数）的试验中，84%的试验获得成功。在13 Hz的触觉反馈频率下，这两组试验的平均插入时间分别为262秒和147秒。