The Task Motion Kit

R require novel reasoning systems to achieve complex objectives in new environments. Everyday activities in the physical world couple discrete and continuous reasoning. For example, to set the table in Fig. 1, the robot must make discrete decisions about which objects to pick and the order in which to do so, and execute these decisions by computing continuous motions to reach objects or desired locations. Robotics has traditionally treated these issues in isolation. Reasoning about discrete events is referred to as task planning while reasoning about and computing continuous motions is the realm of motion planning. However, several recent works have shown that separating task planning from motion planning—that is finding first a series of actions that will later be executed through continuous motion—is problematic; for example, the next discrete action may specify picking an object, but there may be no continuous motion for the robot to bring its hand to a configuration that can actually grasp the object to pick it up. Instead, Task–Motion Planning (TMP) tightly couples task planning and motion planning, producing a sequence of steps that can actually be executed by a real robot to bring the world from an initial to a final state. This article provides an introduction to TMP and discusses the implementation and use of an open-source TMP framework that is adaptable to new robots, scenarios, and algorithms. TMP presents challenges both in algorithmic design and software engineering. Interaction between the discrete, task component and the continuous, motion component imposes requirements not faced by stand-alone task planners or motion planners. The planner may need to consider alternate task plans in an efficient way until finding one that can actually be executed by the robot at hand, whereas typical task planners generate only a single plan. In addition, actions where the robot grasps and rearranges objects will change the kinematics and configuration space in which the robot can move, whereas typical motion planners assume a fixed configuration space. Thus, we cannot expect to combine existing tools for isolated task planning and motion planning and produce frameworks that can consistently use high-level specifications of behavior to produce motion. Instead, we must handle the possible interactions of discrete and continuous components to identify task plans and executable motions. The Task–Motion Kit (TMKit)1 is an end-to-end system for probabilistically complete task–motion planning and real-