Manipulation with Multiple Action Types

2013 IEEE International Conference on Robotics and Automation

2013

Self Cite

Abstract-We define the Diverse Action Manipulation (DAMA) problem in which we are given a mobile robot, a set of movable objects, and a set of diverse, possibly nonprehensile manipulation actions, and the goal is to find a sequence of actions that moves each of the objects to a goal configuration. We show that the DAMA problem can be framed as a multi-modal planning problem and describe a hierarchical algorithm that takes advantage of this multimodal nature. We also extend our earlier forward search sampling algorithm to a bi-directional version. We give results on a complicated manipulation domain and demonstrate that both new algorithms are significantly more efficient than the original, and that the hierarchical algorithm is usually much more efficient than the forward or bi-directional searches.

“…More examples of primitives will be given in Section VI. The DAMA problem was informally introduced in Barry et al [2], but here we give a formal definition.…”

Section: A Dama Problemmentioning

confidence: 99%

Section: A Darrt and Darrtconnect Algorithmsmentioning

confidence: 99%

See 1 more Smart Citation

A hierarchical approach to manipulation with diverse actions

Barry

Kaelbling

2013 IEEE International Conference on Robotics and Automation

2013

Self Cite

“…They are also multi-modal [Siméon et al, 2004, Hauser and Ng-Thow-Hing, 2011, Barry et al, 2012 requiring choosing grasps and finding locations to place objects, as well as finding collision-free paths. As such, they are well beyond the state of the art in motion planning.…”

Section: Hierarchymentioning

confidence: 99%

Integrated Robot Task and Motion Planning in the Now

Kaelbling¹,

2012

Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. This paper provides an approach to integrating geometric motion planning with logical task planning for long-horizon tasks in domains with many objects. We propose a tight integration between the logical and geometric aspects of planning. We use a logical representation which includes entities that refer to poses, grasps, paths and regions, without the need for a priori discretization. Given this representation and some simple mechanisms for geometric inference,we characterize the pre-conditions and effects of robot actions in terms of these logical entities. We then reason about the interaction of the geometric and non-geometric aspects of our domains using the general-purpose mechanism of goal regression (also known as pre-image backchaining). We propose an aggressive mechanism for temporal hierarchical decomposition, which postpones the pre-conditions of actions to create an abstraction hierarchy that both limits the lengths of plans that need to be generated and limits the set of objects relevant to each plan. We describe an implementation of this planning method and demonstrate it in a simulated kitchen environment in which it solves problems that require approximately 100 individual pick or place operations for moving multiple objects in a complex domain. This paper provides an approach to integrating geometric motion planning with logical task planning for long-horizon tasks in domains with many objects. We propose a tight integration between the logical and geometric aspects of planning. We use a logical representation which includes entities that refer to poses, grasps, paths and regions, without the need for a priori discretization. Given this representation and some simple mechanisms for geometric inference, we characterize the pre-conditions and effects of robot actions in terms of these logical entities.We then reason about the interaction of the geometric and non-geometric aspects of our domains using the general-purpose mechanism of goal regression (also known as pre-image backchaining). We propose an aggressive mechanism for temporal hierarchical decomposition, which postpones the pre-conditions of actions to create an abstraction hierarchy that both limits the lengths of plans that need t...

“…It has long been recognized [1], [2], [3], [4], [5], [6], [7], however, that solving manipulation problems requires planning a coordinated sequence of motions that involve picking, placing, pushing, or otherwise manipulating the objects, as well as moving through free space. However, the dimensionality (and therefore the running time) of these manipulation planning problems increases substantially over traditional single motion-planning problems.…”

Section: Introductionmentioning

confidence: 99%

A constraint-based method for solving sequential manipulation planning problems

2014 IEEE/RSJ International Conference on Intelligent Robots and Systems

Kaelbling

2014

Self Cite

138

Abstract-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.