Manipulation Task Planning and Motion Control Using Task Relaxations

This paper proposes a robotic manipulation task relaxation method applied to a task and motion planning framework adapted from the literature. The task relaxation method consists of defining regions of interest instead of defining the end-effector pose, which can potentially increase the robot's redundancy with respect to the manipulation task. Tasks are relaxed by controlling the end-effector distance to a target plane while respecting suitable constraints in the task-space. We formulate the problem as a constrained control problem that enforces both equality and inequality constraints while being reactive to changes in the workspace. We evaluate the adapted framework in a simulated pick-and-place task with similar complexity to the one evaluated in the original framework. The number of plan nodes that our framework generates is 54% smaller than the one in the original framework and our framework is faster both in planning and execution time. Also, the end-effector remains within the regions of interest and moves toward the target region while satisfying additional constraints.