Towards planning Human-Robot Interactive manipulation tasks: Task dependent and human oriented autonomous selection of grasp and placement

Pandey, Amit Kumar; Saut, Jean-Philippe; Sidobre, Daniel; Alami, Rachid

doi:10.1109/biorob.2012.6290776

Cited by 27 publications

(20 citation statements)

References 17 publications

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“…The placement graph can be computed lazily to avoid computing low-level paths that are never considered by the task level planner. Finally, the placement graph can be grown adaptively: Using constraint propagation techniques [20], [34] it is possible to significantly reduce the number of local paths that are generated. We are also investigating the use of feedback from the motion planning level to the solver to help guide its search.…”

Section: Discussionmentioning

confidence: 99%

SMT-based synthesis of integrated task and motion plans from plan outlines

Nedunuri

Prabhu

Moll

et al. 2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-We present a new approach to integrated task and motion planning (ITMP) for robots performing mobile manipulation. In our approach, the user writes a high-level specification that captures partial knowledge about a mobile manipulation setting. In particular, this specification includes a plan outline that syntactically defines a space of plausible integrated plans, a set of logical requirements that the generated plan must satisfy, and a description of the physical space that the robot manipulates. A synthesis algorithm is now used to search for an integrated plan that falls within the space defined by the plan outline, and also satisfies all requirements.Our synthesis algorithm complements continuous motion planning algorithms with calls to a Satisfiability Modulo Theories (SMT) solver. From the scene description, a motion planning algorithm is used to construct a placement graph, an abstraction of a manipulation graph whose paths represent feasible, low-level motion plans. An SMT-solver is now used to symbolically explore the space of all integrated plans that correspond to paths in the placement graph, and also satisfy the constraints demanded by the plan outline and the requirements.Our approach is implemented in a system called RO-BOSYNTH. We have evaluated ROBOSYNTH on a generalization of an ITMP problem investigated in prior work. The experiments demonstrate that our method is capable of generating integrated plans for a number of interesting variations on the problem. I. INTRODUCTION Integrated task and motion planning (ITMP) [1]-[4]is a challenging class of planning problems that involve complex combinations of high-level task planning and low-level motion planning. In this paper, we present a new approachembodied in a system called ROBOSYNTH-to ITMP.In the version of ITMP considered here, the task planning level is discrete and requires combinatorial exploration of the space of possible integrated plans, while the motion planning level is responsible for finding paths in continuous spaces. The task level planner has to search a space that is exponential in the number of actions required to achieve a goal, while the continuous planning problem is PSPACE-complete in the degrees of freedom of the robot [5]. Unsurprisingly, the seamless integration of these two levels is difficult. A strictly hierarchical approach where the task planner operates on an abstraction and passes the solution to a continuous motion planner does not always work: it either sacrifices completeness or requires extensive backtracking, which can be highly timeconsuming. While we do not solve the above problem in its

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Section: Discussionmentioning

confidence: 99%

SMT-based synthesis of integrated task and motion plans from plan outlines

Nedunuri

Prabhu

Moll

et al. 2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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“…[9]. The task planner interprets a hierarchy of constraints (representing the task to be performed), then finds a goal configuration and at appropriate stage involves a trajectory planner to find a feasible solution.…”

Section: Instantiation and Resultsmentioning

confidence: 99%

Ingredients and a Framework of Dexterous Manipulation Skills for Robots in Human Centered Environment and HRI

Pandey

Alami²

2014

JRSJ

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“…Pandey et al [10] and deSilva et al [11] use HTNs instead of generative task planning. Their system can backtrack over choices made by the geometric module, allowing more freedom to the geometric planning than in the approach of Dornhege et al [3].…”

Section: Related Workmentioning

confidence: 99%

FFRob: An Efficient Heuristic for Task and Motion Planning

Garrett

Lozano-Pérez

Kaelbling

2015

Springer Tracts in Advanced Robotics

103

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Abstract. Manipulation problems involving many objects present substantial challenges for motion planning algorithms due to the high dimensionality and multi-modality of the search space. Symbolic task planners can efficiently construct plans involving many entities but cannot incorporate the constraints from geometry and kinematics. In this paper, we show how to extend the heuristic ideas from one of the most successful symbolic planners in recent years, the FastForward (FF) planner, to motion planning, and to compute it efficiently. We use a multi-query roadmap structure that can be conditionalized to model different placements of movable objects. The resulting tightly integrated planner is simple and performs efficiently in a collection of tasks involving manipulation of many objects.

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Towards planning Human-Robot Interactive manipulation tasks: Task dependent and human oriented autonomous selection of grasp and placement

Cited by 27 publications

References 17 publications

SMT-based synthesis of integrated task and motion plans from plan outlines

SMT-based synthesis of integrated task and motion plans from plan outlines

Ingredients and a Framework of Dexterous Manipulation Skills for Robots in Human Centered Environment and HRI

FFRob: An Efficient Heuristic for Task and Motion Planning

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