  To solve multi-step manipulation tasks in the real world, an autonomous robot
must take actions to observe its environment and react to unexpected
observations. This may require opening a drawer to observe its contents or
moving an object out of the way to examine the space behind it. Upon receiving
a new observation, the robot must update its belief about the world and compute
a new plan of action. In this work, we present an online planning and execution
system for robots faced with these challenges. We perform deterministic
cost-sensitive planning in the space of hybrid belief states to select
likely-to-succeed observation actions and continuous control actions. After
execution and observation, we replan using our new state estimate. We initially
enforce that planner reuses the structure of the unexecuted tail of the last
plan. This both improves planning efficiency and ensures that the overall
policy does not undo its progress towards achieving the goal. Our approach is
able to efficiently solve partially observable problems both in simulation and
in a real-world kitchen.
