Learning Object Models For Nonprehensile Manipulation Using models to represent information about the world is a well known paradigm for successful robot control in the real world. Numerous methods exist today that can leverage these models to make robots perform tasks, either by directly exploiting the model structure or by accessing the model via a simulation. In this work, we explore how robots can use demonstrations to quickly build descriptive models of objects for manipulation tasks. Our framework uses demonstrations to incrementally build taskrelevant geometric and physicsbased object models that can be used to build simulations of the world that the robot is interacting with. We present experiments that involve estimating geometric features of an object when demonstration data from a user interacting with the object is available. We also demonstrate our method on the task of toppling a box with a 7DoF manipulator equipped with a palm at its end. Using our approach, the robot is able to complete the task using only a few demonstrations.

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.