ForceandMotion Constrained Planning for Tool Use The use of hand tools presents a challenge for robot manipulation in part because it calls for motions requiring continuous force application over a whole trajectory, usually involving large jointangle excursions. The feasible application of a tool, such as pulling a nail with a hammer claw, requires careful coordination of the choice of grasp and joint trajectories to ensure kinematic and force limits are not exceeded  in the grasp as well as the robot mechanism. In this paper, we formulate this type of problem as choosing the values of decision variables in the presence of various constraints. We evaluate the impact of the various constraints in some representative instances of tool use. To aid others in further investigating this class of problems, we have released materials such as printable tool models and experimental data. We hope that these can serve as the basis of a benchmark problem for investigating tasks that involve many kinematic, actuation, friction, and environment constraints.

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.