  We address the problem of applying Task and Motion Planning (TAMP) in real
world environments. TAMP combines symbolic and geometric reasoning to produce
sequential manipulation plans, typically specified as joint-space trajectories,
which are valid only as long as the environment is static and perception and
control are highly accurate. In case of any changes in the environment, slow
re-planning is required. We propose a TAMP algorithm that optimizes over
Cartesian frames defined relative to target objects. The resulting plan then
remains valid even if the objects are moving and can be executed by reactive
controllers that adapt to these changes in real time. We apply our TAMP
framework to a torque-controlled robot in a pick and place setting and
demonstrate its ability to adapt to changing environments, inaccurate
perception, and imprecise control, both in simulation and the real world.
