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.

ContactGrasp: Functional Multifinger Grasp Synthesis from Contact Grasping and manipulating objects is an important human skill. Since most objects are designed to be manipulated by human hands, anthropomorphic hands can enable richer humanrobot interaction. Desirable grasps are not only stable, but also functional: they enable postgrasp actions with the object. However, functional grasp synthesis for high degreeoffreedom anthropomorphic hands from object shape alone is challenging. We present ContactGrasp, a framework for functional grasp synthesis from object shape and contact on the object surface. Contact can be manually specified or obtained through demonstrations. Our contact representation is objectcentric and allows functional grasp synthesis even for hand models different than the one used for demonstration. Using a dataset of contact demonstrations from humans grasping diverse household objects, we synthesize functional grasps for three hand models and two functional intents. The project webpage is https:contactdb.cc.gatech.educontactgrasp.html.