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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