This paper presents a manipulation planning algorithm for robots to reorient objects. It automatically finds a sequence of robot motion that manipulates and prepares an object for specific tasks. Examples of the preparatory manipulation planning problems include reorienting an electric drill to cut holes, reorienting workpieces for assembly, and reorienting cargo for packing, etc. The proposed algorithm could plan single and dual arm manipulation sequences to solve the problems. The mechanism under the planner is a regrasp graph which encodes grasp configurations and object poses. The algorithms search the graph to find a sequence of robot motion to reorient objects. The planner is able to plan both single and dual arm manipulation. It could also automatically determine whether to use a single arm, dual arms, or their combinations to finish given tasks. The planner is examined by various humanoid robots like Nextage, HRP2Kai, HRP5P, etc., using both simulation and real-world experiments.
Recent work included more complicated grasp planners and was integrally considered with backtracking, geometrical reasoning, and motion planning. For example, @cite_25 presented a framework which integrated motion planning and preparatory manipulation planning to move objects among obstacles. @cite_28 used shape primitives to plan the grasps of a multi-finger hand and implemented the preparatory manipulation planning of a cup using the multi-finger hand. @cite_20 used Bayesian Network to model task constraints and plan the preparatory manipulation sequence of containers and toys. @cite_12 used integral primitive-based prehensile and non-prehensile grasp planning to exercise preparatory manipulation of everyday objects. Lozano- @cite_1 used symbolic search to find manipulation sequences under various constraints. The method was used to plan sequences for a mobile manipulator to rearrange objects. Similarly, @cite_23 , @cite_16 , Toussaint @cite_18 , and @cite_27 respectively used symbolic reasoning to remove obstacles and pick up or rearrange objects. Especially, Toussaint @cite_18 and @cite_27 presented algorithms to plan motion sequences for robots to stack distributed objects, considering both task and motion constraints. @cite_6 presented a planner considering the constraints from coordinated manipulators. More extensively, @cite_2 and @cite_3 respectively presented a framework work that used integral prehensile and non-prehensile planning to generate sequential manipulation.
@cite_18 @cite_28 @cite_1 @cite_16 @cite_6 @cite_3 @cite_27 @cite_23 @cite_2 @cite_20 @cite_25 @cite_12
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We consider problems of sequential robot manipulation (aka. combined task and motion planning) where the objective is primarily given in terms of a cost function over the final geometric state, rather than a symbolic goal description. In this case we should leverage optimization methods to inform search over potential action sequences. We propose to formulate the problem holistically as a 1st- order logic extension of a mathematical program: a non-linear constrained program over the full world trajectory where the symbolic state-action sequence defines the (in-)equality constraints. We tackle the challenge of solving such programs by proposing three levels of approximation: The coarsest level introduces the concept of the effective end state kinematics, parametrically describing all possible end state configurations conditional to a given symbolic action sequence. Optimization on this level is fast and can inform symbolic search. The other two levels optimize over interaction keyframes and eventually over the full world trajectory across interactions. We demonstrate the approach on a problem of maximizing the height of a physically stable construction from an assortment of boards, cylinders and blocks. 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. Constrained motion planning and Manipulation planning, for generic non-linear constraints, highly rely on the ability of solving non-linear equations. The Newton-Raphson method, often used in this context, is discontinuous with respect to its input and point wise path projection can lead to discontinuous constrained paths. The discontinuities come from the pseudo-inverse involved at each iteration of the Newton-Raphson algorithm. An interval of continuity for one iteration is derived from an upper bound of the norm of the Hessian of the constraints. Such a bound is easy to compute for constraints involving joint positions and orientations. We introduce two heuristical path projection algorithms and empirically show that they generate continuous constrained paths. The algorithms are integrated into a motion planning algorithm to empirically assert the continuity of the solution. In this paper we propose a method for motion planning and feedback control of hybrid, dynamic, and non-prehensile manipulation tasks. We outline five subproblems to address this: determining a set of manipulation primitives, choosing a sequence of tasks, picking transition states, motion planning for each individual primitive, and stabilizing each mode using feedback control. We apply the framework to plan a sequence of motions for manipulating a block with a planar 3R manipulator. We demonstrate preliminary experimental results for a block resting on the manipulator with a desired goal state on a ledge outside of the robot's workspace. The planned primitives reorient the block using a series of fixed, rolling, and sliding contact modes, and throw it to the goal state. The need for combined task and motion planning in robotics is well understood. Solutions to this problem have typically relied on special purpose, integrated implementations of task planning and motion planning algorithms. We propose a new approach that uses off-the-shelf task planners and motion planners and makes no assumptions about their implementation. Doing so enables our approach to directly build on, and benefit from, the vast literature and latest advances in task planning and motion planning. It uses a novel representational abstraction and requires only that failures in computing a motion plan for a high-level action be identifiable and expressible in the form of logical predicates at the task level. We evaluate the approach and illustrate its robustness through a number of experiments using a state-of-the-art robotics simulator and a PR2 robot. These experiments show the system accomplishing a diverse set of challenging tasks such as taking advantage of a tray when laying out a table for dinner and picking objects from cluttered environments where other objects need to be re-arranged before the target object can be reached. Manipulation planning involves planning the combined motion of objects in the environment as well as the robot motions to achieve them. In this paper, we explore a hierarchical approach to planning sequences of non-prehensile and prehensile actions. We subdivide the planning problem into three stages (object contacts, object poses and robot contacts) and thereby reduce the size of search space that is explored. We show that this approach is more efficient than earlier strategies that search in the combined robot-object configuration space directly. In this paper, we present an approach towards autonomous grasping of objects according to their category and a given task. Recent advances in the field of object segmentation and categorization as well as task-based grasp inference have been leveraged by integrating them into one pipeline. This allows us to transfer task-specific grasp experience between objects of the same category. The effectiveness of the approach is demonstrated on the humanoid robot ARMAR-IIIa. This paper deals with motion planning for robots manipulating movable objects among obstacles. We propose a general manipulation planning approach capable of addressing continuous sets for modeling both the possible grasps and the stable placements of the movable object, rather than discrete sets generally assumed by the previous approaches. The proposed algorithm relies on a topological property that characterizes the existence of solutions in the subspace of configurations where the robot grasps the object placed at a stable position. It allows us to devise a manipulation planner that captures in a probabilistic roadmap the connectivity of sub-dimensional manifolds of the composite configuration space. Experiments conducted with the planner in simulated environments demonstrate its efficacy to solve complex manipulation problems.