IEEE Robotics & Automation Magazine - September 2018 - 64
that the object is on the table and the effect that the object is
in the robot's hand. The "Task Domain" section describes our
implementation of task domains, and Figure 3 provides a
complete example of the pick-up action with preconditions
and effects.
Motion domain: The motion domain defines the threedimensional (3-D) positions of objects in the environment,
the kinematic structure of robot joints, and the geometry, i.e.,
meshes, of objects and robot links. Collectively, we call the
robot and environment the scene and the tree or graph of the
local coordinate frames of environmental objects and robot
links the scene graph, which defines the configuration space of
a robot. For a given configuration, computing the forward
kinematics of each frame in the scene graph provides the
mesh positions; then, specialized collision checkers [18] determine whether those positions are in collision.
We specify both an initial scene, consisting of the robot
and the environment, and a goal scene for the planner.
Then we map from these scenes to task states using the
domain semantics.
Domain semantics: The domain semantics define the coupling between the discrete task domain and the continuous
motion domain. Two types of functions are necessary. First,
we need a function to map from a scene graph to a discrete
state for the task planner. For example, if the scene graph
defines some object a's position relative to the robot's hand,
the domain semantics would set to "true" a discrete variable
holding-a, indicating that the robot is holding object a.
Second, we need a function to map from a discrete task action
to a motion planning problem (start and goal states) for the
motion planner. For example, the pick-up action would
start at the robot's current configuration and move to a goal
that is a grasping configuration for the object to be picked up.
The "TMP" section discusses our implementation of the
domain semantics.
TM Planner
The TM planner finds the sequence of discrete task actions
from the task domain and their corresponding motion
plans-based on the domain semantics-that will take the
TM Planner
Task Domain
Candidate Task Plan
Task
Planner
(Using Z3)
Motion
Planner
(Using OMPL)
Motion Feasibility Constraints
Motion Domain
Domain Semantics
TM Plan
Figure 3. A TM planner implementation diagram, showing fragments of the planner's input (i.e., the task domain, domain semantics,
and motion domain) and output (i.e., the TM plan). OMPL: Open Motion Planning Library.
64
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IEEE ROBOTICS & AUTOMATION MAGAZINE
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september 2018
IEEE Robotics & Automation Magazine - September 2018
Table of Contents for the Digital Edition of IEEE Robotics & Automation Magazine - September 2018
Contents
IEEE Robotics & Automation Magazine - September 2018 - Cover1
IEEE Robotics & Automation Magazine - September 2018 - Cover2
IEEE Robotics & Automation Magazine - September 2018 - Contents
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