Online Replanning in Belief Space for Partially Observable Task and Motion Problems

TL;DR

This paper introduces an online belief-space planning system enabling robots to adaptively replan in real-time for complex, partially observable manipulation tasks, improving efficiency and robustness in real-world scenarios.

Contribution

It presents a novel online planning and execution framework that reuses plan structure for efficiency and handles hybrid belief states in real-time for robotic manipulation tasks.

Findings

Successfully applied in real-world kitchen environment

Efficiently solves partially observable problems in simulation and reality

Reuses plan structure to improve planning speed

Abstract

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.