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.

A DataDriven Framework for Learning Dexterous Manipulation of Unknown Objects We address the problem of developing precision, quasistatic control strategies for fingertip manipulation in robot hands. In general, analytically specifying useful object transition maps, or handobject Jacobians, for scenarios in which there is uncertainty in some key aspect of the handobject system is difficult or impossible. This could be in scenarios with standard fullyactuated hands where, for instance, there is not an accurate model of the contact conditions, or in scenarios with fewer control inputs than mechanical degrees of freedom (such as underactuated hands or those that are controlled by synergies or impedance controlled frameworks), since the output space is of higher dimension than the input space. In this work, we develop a method for extracting object transition maps by tracking the state of the grasp frame. We begin by modeling a compliant, underactuated hand and its mechanical properties through an energybased approach. From this energy model, we provide controlled actuation inputs to change the state of the grasp frame. We observe the response from these actions and develop a regression map of the actionreaction pairs, where the map is subject to our intent for grasp frame movement and the regional relationship between the contacts. Once the regression model is developed, we perform withinhand planning of the grasp frame with newly introduced objects. This approach is agnostic to the global geometry of the object and is able to adapt when undesirable contact conditions, such as sliding, occur. The learningbased methodology estimates the nonlinearities representative in the properties of the system. We test our framework physically on an adapted Yale Openhand Model O. By transferring the learned model from simulation to the physical hand without adaptation, we show that this energy modeling approach is robust to inaccuracies in parameter estimation. We demonstrate its efficacy in a handwriting task.