Learning by Demonstration and Robust Control of Dexterous InHand Robotic Manipulation Skills Dexterous robotic manipulation of unknown objects can open the way to novel tasks and applications of robots in semistructured and unstructured settings, from advanced industrial manufacturing to exploration of harsh environments. However, it is challenging for at least three reasons: the desired motion of the object might be too complex to be described analytically, precise models of the manipulated objects are not available, the controller should simultaneously ensure both a robust grasp and an effective inhand motion. To solve these issues we propose to learn inhand robotic manipulation tasks from human demonstrations, using Dynamical Movement Primitives (DMPs), and to reproduce them with a robust compliant controller based on the Virtual Springs Framework (VSF), that employs realtime feedback of the contact forces measured on the robot fingertips. With this solution, the generalization capabilities of DMPs can be transferred successfully to the dexterous inhand manipulation problem: we demonstrate this by presenting realworld experiments of inhand translation and rotation of unknown objects.

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.