AL and S Methods: Two Extensions for LMethod With the advent of smart IoT and M2M scenarios it becomes necessary to develop autonomous systems that optimize themselves with minimal human intervention. One possible method to achieve this is through Kneeelbow point estimation. Most of the time these points represent ideal compromises for parameters, methods and algorithms. However, estimating the kneeelbow point in curves is a challenging task. Our focus is on determining the ideal number of clusters autonomously. We analyse and discuss wellknown kneeelbow estimators and two extensions based on the theoretical definition. The proposed methods (named AL and S methods) were evaluated against stateoftheart estimators. The proposed methods are a viable stable solution for kneeelbow estimation.

ARMCL: ARM Contact point Localization via Monte Carlo Localization Detecting and localizing contacts acting on a manipulator is a relevant problem for manipulation tasks like grasping, since contact information can be helpful for recovering from collisions or for improving the grasping performance itself. In this work, we present a solution for contact point localization, which is based on Monte Carlo Localization. Usually, an Articulated Robotic Manipulator (ARM) is not equipped with tactile skin, but with proprioceptive sensors, which we assume as an input for our method. In our experiments, we compare our method with a direct optimization method, machine learning approaches and another particle filter method, both on simulated and real world data from a Kinova Jaco2. While our proposed method clearly outperforms the other optimization approaches, it performs about equally well as Random Forest (RF) classifiers, although both methods have their strengths on different parts of the manipulator, and even achieves better results than multilayer perceptions (MLPs) on the links farthest from the manipulator base.