Pick-and-place regrasp is an important manipulation skill for a robot. It helps a robot accomplish tasks that cannot be achieved within a single grasp, due to constraints such as kinematics or collisions between the robot and the environment. Previous work on pick-and-place regrasp only leveraged flat surfaces for intermediate placements, and thus is limited in the capability to reorient an object. In this paper, we extend the reorientation capability of a pick-and-place regrasp by adding a vertical pin on the working surface and using it as the intermediate location for regrasping. In particular, our method automatically computes the stable placements of an object leaning against a vertical pin, finds several force-closure grasps, generates a graph of regrasp actions, and searches for the regrasp sequence. To compare the regrasping performance with and without using pins, we evaluate the success rate and the length of regrasp sequences while performing tasks on various models. Experiments on reorientation and assembly tasks validate the benefit of using support pins for regrasping.
black In order to improve the manipulation flexibility, some recent work began to incorporate non-grasp polices in the manipulation. There are many prehensile or non-prehensile strategies besides grasping, such as non-prehensile pivoting @cite_27 and prehensile pivoting @cite_8 , non-prehensile pushing @cite_1 and prehensile pushing @cite_25 . Regrasp policies leveraging external forces or external environments have also been proposed @cite_4 @cite_25 for in-hand manipulation. To make use of these non-grasp policies, @cite_12 proposed the concept of extended transit, i.e., the transition motion will not only include the transitions between prehensile grasps, but also those between non-prehensile manipulation strategies. A similar idea was also proposed by @cite_28 , which used grasping and pushing for transition motion, but only used grasping for transfer motion.
@cite_4 @cite_8 @cite_28 @cite_1 @cite_27 @cite_25 @cite_12
1198461769 1249953932 1987985958 2038890859
This paper explores the manipulation of a grasped object by pushing it against its environment. Relying on precise arm motions and detailed models of frictional contact, prehensile pushing enables dexterous manipulation with simple manipulators, such as those currently available in industrial settings, and those likely affordable by service and field robots. We address the problem of grasping everyday objects that are small relative to an anthropomorphic hand, such as pens, screwdrivers, cellphones, and hammers from their natural poses on a support surface, e.g., a table top. In such conditions, state of the art grasp generation techniques fail to provide robust, achievable solutions due to either ignoring or trying to avoid contact with the support surface. In contrast, we show that contact with support surfaces is critical for grasping small objects. This also conforms with our anecdotal observations of human grasping behaviors. We develop a simple closed-loop hybrid controller that mimics this interactive, contact-rich strategy by a position-force, pre-grasp and landing strategy for finger placement. The approach uses a compliant control of the hand during the grasp and release of objects in order to preserve safety. We conducted extensive grasping experiments on a variety of small objects with similar shape and size. The results demonstrate that our approach is robust to localization uncertainties and applies to many everyday objects. Many manipulation planning problems involve several related sub-problems, such as the selection of grasping points on an object, choice of hand posture, and determination of the arm's configuration and evolving trajectory. Traditionally, these planning sub-problems have been handled separately, potentially leading to sub-optimal, or even infeasible, combinations of the individually determined solutions. This paper formulates the combined problem of grasp contact selection, grasp force optimization, and manipulator arm hand trajectory planning as a problem in optimal control. That is, the locally optimal trajectory for the manipulator, hand mechanism, and contact locations are determined during the pre-grasping, grasping, and subsequent object transport phase. Additionally, a barrier function approach allows for non-feasible grasps to be optimized, enlarging the region of convergence for the algorithm. A simulation of a simple planar object manipulation task is used to illustrate and validate the approach. When possible, non-prehensile transportation (i.e. transporting objects without grasping them) can be faster and more efficient than prehensile transportation. However, the need to explicitly consider reaction and friction forces yields kino-dynamic constraints that are difficult to take into account by traditional planning algorithms. Based on the recently developed Admissible Velocity Propagation algorithm, we propose here a fast and general non-prehensile transportation scheme. Our contribution is twofold. First we show how to cast the dynamic balance constraints of a 3D object (e.g. a bottle) into a form compatible with the AVP algorithm. Second, we extend the AVP-RRT algorithm into a more efficient AVP-biRRT algorithm, which makes use of the idea of concurrently growing two trees, one rooted at the starting configuration and one rooted at the goal configuration. We also show both in simulations and on a real robot how our algorithm allows planning fast and dynamic trajectories for the non-prehensile transportation of a bottle.