Artificial intelligence
Task and motion planning
Definition
Task and motion planning jointly searches over discrete task decisions and continuous robot motions. It connects choices such as which object to move or which grasp to use with geometrically and kinematically feasible trajectories.
Also known as: TAMP, Integrated task and motion planning
Updated
Discrete choices meet continuous geometry
A task planner can reason that a robot must open a cupboard before retrieving an object. A motion planner can search for a collision-free arm path to a specified pose. Task and motion planning couples these levels because the task choice may determine whether a feasible grasp, stance, and path exist.
Garrett and colleagues describe TAMP problems as containing discrete task planning, discrete-continuous mathematical programming, and continuous motion planning. A solution usually includes both a sequence of symbolic actions and continuous values such as robot configurations, object poses, grasps, or trajectories.
Why planning the levels separately can fail
Suppose a humanoid chooses to pick up a box with its right hand. That symbolic choice may be impossible from the available stance, while a left-hand grasp or a base repositioning step would work. If the task plan is fixed before geometric checks, the system may spend time refining an infeasible sequence. TAMP methods use feedback between the levels so continuous failures can change discrete choices.
This makes TAMP broader than motion planning. Motion planning normally assumes the action, start state, and goal condition are already specified. TAMP may have to decide what those actions and goals should be.
Search remains difficult
The discrete plan space grows with possible actions and objects, while every candidate can create one or more continuous feasibility problems. Collision geometry, configuration-space constraints, grasp choices, and contact modes can make a seemingly short task expensive to solve.
Different TAMP algorithms make different completeness, sampling, and modelling tradeoffs, as surveyed by Garrett and colleagues. A returned plan is only as useful as its world model and execution assumptions. Perception errors, moved objects, or failed grasps may require replanning rather than blind continuation.
Sources
Related terms
Motion planning
Motion planning finds a robot movement from an initial state to a goal while satisfying constraints such as collision avoidance. A planner may produce a geometric path, a timed trajectory, or a sequence of controls.
Robotic manipulation
Robotic manipulation is the use of a robot to change an object's position, orientation, or state through physical interaction. It includes grasping and moving objects as well as actions such as pushing or carrying them without a grasp.
Configuration space
Configuration space is the set of all possible configurations of a robot or mechanical system. Each point specifies the entire modeled arrangement, and the space has as many local dimensions as the system has degrees of freedom.
Trajectory optimization
Trajectory optimization finds a time-varying motion, and often control inputs, that minimizes an objective while satisfying specified constraints. Robot applications can include geometric, kinematic, and dynamic constraints.