Dexterous Manipulation Graphs

We propose the Dexterous Manipulation Graph as a tool to address in-hand manipulation and reposition an object inside a robot's end-effector. This graph is used to plan a sequence of manipulation primitives so to bring the object to the desired end pose. This sequence of primitives is translated into motions of the robot to move the object held by the end-effector. We use a dual arm robot with parallel grippers to test our method on a real system and show successful planning and execution of in-hand manipulation

A Certified-Complete Bimanual Manipulation Planner

Planning motions for two robot arms to move an object collaboratively is a difficult problem, mainly because of the closed-chain constraint, which arises whenever two robot hands simultaneously grasp a single rigid object. In this paper, we propose a manipulation planning algorithm to bring an object from an initial stable placement (position and orientation of the object on the support surface) towards a goal stable placement. The key specificity of our algorithm is that it is certified-complete: for a given object and a given environment, we provide a certificate that the algorithm will find a solution to any bimanual manipulation query in that environment whenever one exists. Moreover, the certificate is constructive: at run-time, it can be used to quickly find a solution to a given query. The algorithm is tested in software and hardware on a number of large pieces of furniture.Comment: 12 pages, 7 figures, 1 tabl

Hybrid motion planning approach for robot dexterous hands

This paper presents a manipulation planning approach for robot hands that enables the generation of finger trajectories. The planner is based on a hybrid approach that combines discrete-continuous kinematics using a fully discrete transition system. One of the main contributions of this work consists in the representation of the universe of different submodel combinations, as states in a discrete transition system. The manipulated object geometry is taken into account and the system composed by the object and the hand is modeled as a set of closed kinematical chains. The methodology enables the synthesis of complex manipulation trajectories, when one or more fingers change the contact condition with the object. Contact condition changes include rolling contact, sliding contact, contact loss and contact establishment. Tests were carried out employing a three finger manipulation task in computer simulations and with an experimental setup

Stable Prehensile Pushing: In-Hand Manipulation with Alternating Sticking Contacts

This paper presents an approach to in-hand manipulation planning that exploits the mechanics of alternating sticking contact. Particularly, we consider the problem of manipulating a grasped object using external pushes for which the pusher sticks to the object. Given the physical properties of the object, frictional coefficients at contacts and a desired regrasp on the object, we propose a sampling-based planning framework that builds a pushing strategy concatenating different feasible stable pushes to achieve the desired regrasp. An efficient dynamics formulation allows us to plan in-hand manipulations 100-1000 times faster than our previous work which builds upon a complementarity formulation. Experimental observations for the generated plans show that the object precisely moves in the grasp as expected by the planner. Video Summary -- youtu.be/qOTKRJMx6HoComment: IEEE International Conference on Robotics and Automation 201

Scale-Dependent Grasp

This paper discusses the scale-dependent grasp.Suppose that a human approaches an object initially placed on atable and finally achieves an enveloping grasp. Under such initialand final conditions, he (or she) unconsciously changes the graspstrategy according to the size of objects, even though they havesimilar geometry. We call the grasp planning the scale-dependentgrasp. We find that grasp patterns are also changed according tothe surface friction and the geometry of cross section in additionto the scale of object. Focusing on column objects, we first classifythe grasp patterns and extract the essential motions so that we canconstruct grasp strategies applicable to multifingered robot hands.The grasp strategies constructed for robot hands are verified byexperiments. We also consider how a robot hand can recognizethe failure mode and how it can switch from one to another