Grasping and Manipulation

Grasp planning for multiple finger manipulators has proven to be a very challenging problem. Traditional approaches rely on models for contact planning which lead to computationally intractable solutions and often do not scale to three dimensional objects or to arbitrary numbers of contacts. We have constructed an approach for closed-loop grasp control which is provably correct for two and three contacts on regular, convex objects. This approach employs "n" asynchronous controllers (one for each contact) to achieve grasp geometries from among an equivalence class of grasp solutions. This approach generates a grasp controller - a closed-loop, differential response to tactile feedback - to remove wrench residuals in a grasp configuration. The equilibria establish necessary conditions for wrench closure on regular, convex objects, and identify good grasps, in general, for arbitrary objects. Sequences of grasp controllers, engaging sequences of contact resources can be used to optimize grasp performance and to produce manipulation gaits . The result is a very unique, sensor-based grasp controller that does not require a priori object geometry.

Robust Finger Gaits from Closed-Loop Controllers

Grasp Control

Here is an example of the Utah/MIT hand rolling a can:


Click here to see a movie of the hand rolling the can (as above), and here to see a movie of the Utah/MIT hand performing an ordinary household chore.

Sliding Control

Whole-Body Grasping

Learning Strategies for Manipulation

Exploiting Redundancy


Here Dexter demonstrates the redundancy available in bimanual grasps. The robot is able to transition between stable grasps between both hands, as well as between one hand and gravity. The video demonstrates a learned policy for these transitions that allows the robot to move the ball to locations far to it's side, not reachable while maintaining a bimanual grasp.

Grasp Pre-Shaping


In this "Grocery Bagging" example, Dexter demonstrates learned knowledge about grasp pre-shapes based on the scale and eccentricity of the object. Approach angles and offsets are found which generalize to many objects not initially trained on.