  We propose a planning and control approach to physics-based manipulation. The
key feature of the algorithm is that it can adapt to the accuracy requirements
of a task, by slowing down and generating `careful' motion when the task
requires high accuracy, and by speeding up and moving fast when the task
tolerates inaccuracy. We formulate the problem as an MDP with action-dependent
stochasticity and propose an approximate online solution to it. We use a
trajectory optimizer with a deterministic model to suggest promising actions to
the MDP, to reduce computation time spent on evaluating different actions. We
conducted experiments in simulation and on a real robotic system. Our results
show that with a task-adaptive planning and control approach, a robot can
choose fast or slow actions depending on the task accuracy and uncertainty
level. The robot makes these decisions online and is able to maintain high
success rates while completing manipulation tasks as fast as possible.
