Criteria for Maintaining Desired Contacts for QuasiStatic Systems In robotic manipulation, finding a feasible motion plan doesnxe2x80x99t guarantee a successful execution. The real world could bring all kinds of unexpected changes to the planned motion, the most deadly ones are usually marked by or caused by unexpected changes of contacts (object slipping away between fingers; getting stuck somewhere, etc). We notice that some actions are more likely to maintain desired contacts than others. To help finding these actions, in this work we propose a set of criteria to quantify the robustness of contacts against modeling uncertainties and disturbance forces. Under the quasistatic assumption, we analyze the causes of contact mode (sticking, sliding, disengaged) transitions and discuss how to endure larger uncertainties and disturbances. We summarize our results into several physically meaningful and easytocompute scores, which can be used to evaluate the quality of each individual contacts in a manipulation system. We illustrate the meaning of the scores with a simple example.

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.