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.
Solving manipulation problems requires planning a coordinated sequence of motions that involves picking and placing, as well as moving through the free space. Such sequential manipulation problem is challenging due to its high dimensionality. Early work in this area used an explicit graph search to find a sequence of regrasping motions @cite_3 . Most recent approaches are constraint-based. They first formalize the geometric constraints being involved in the manipulation process, e.g., the object must be in a stable pose after being placed down, the relative pose between the manipulator and the object must be fixed during the grasp, and two objects should not collide with each other. Next, they compute a grasp sequence that can satisfy all these constraints. Some methods @cite_21 @cite_18 used these constraints to define a set of interconnected sub-manifolds in the task space, and then computed a solution sequence using probabilistic roadmaps embedded in the constrained space. Other approaches @cite_17 @cite_14 @cite_20 used these constraints to represent sequential manipulation problems as constraint-satisfaction problem (CSP), and then solved the CSP using variants of the backtracking search.
@cite_18 @cite_14 @cite_21 @cite_3 @cite_20 @cite_17
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This paper presents a new geometrical formulation of the manipulation task planning problem in robotics. The problem is shown to be a constrained instance of the coordinated motion planning problem for multiple moving bodies. The constraints are related to the placement and the motion of objects, and can be expressed geometrically. We give a general paradigm for building Manipulation Task Planners based on the proposed formulation. A manipulation task appears as a path in the configuration space of the robot and all movable objects. A manipulation path is a sequence of constrained paths: transit-paths , where the robot moves "alone'', and transfer-paths , where the robot holds an object. The approach consists in building a Manipulation Graph that models the connectivity between certain regions in the global configuration space by transit-paths and transfer-paths. This approach is then applied to the case of a finite number of object placements and grasps. The nodes in the Manipulation Graph correspond to well identified configurations and the edges correspond to paths built from a series of configuration space slices. An implemented system is presented and discussed. In this paper, we describe a strategy for integrated task and motion planning based on performing a symbolic search for a sequence of high-level operations, such as pick, move and place, while postponing geometric decisions. Partial plans (skeletons) in this search thus pose a geometric constraintsatisfaction problem (CSP), involving sequences of placements and paths for the robot, and grasps and locations of objects. We propose a formulation for these problems in a discretized configuration space for the robot. The resulting problems can be solved using existing methods for discrete CSP. 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. We address the problem of a mobile manipulation robot searching for an object in a cluttered domain that is populated with an unknown number of objects in an unknown arrangement. The robot must move around its environment, looking in containers, moving occluding objects to improve its view, and reasoning about collocation of objects of different types, all in service of finding a desired object. The key contribution in reasoning is a Markov-chain Monte Carlo (MCMC) method for drawing samples of the arrangements of objects in an occluded container, conditioned on previous observations of other objects as well as spatial constraints. The key contribution in planning is a receding-horizon forward search in the space of distributions over arrangements (including number and type) of objects in the domain; to maintain tractability the search is formulated in a model that abstracts both the observations and actions available to the robot. The strategy is shown empirically to improve upon a baseline systematic search strategy, and sometimes outperforms a method from previous work.