# Provable Indefinite-Horizon Real-Time Planning for Repetitive Tasks

## TL;DR

This paper introduces a preprocessing-based planning method for repetitive robotic tasks that guarantees real-time performance over indefinite horizons with minimal memory use, significantly speeding up query times.

## Contribution

It presents a novel approach that provides provable bounds on query time for indefinite-horizon planning, addressing a key challenge in high-dimensional robotic motion planning.

## Key findings

- Achieves over tenfold speedup in query time compared to PRM.
- Provides provable bounds on query time with low memory overhead.
- Effective on a 7-DOF robot arm in repetitive tasks.

## Abstract

In many robotic manipulation scenarios, robots often have to perform highly-repetitive tasks in structured environments e.g. sorting mail in a mailroom or pick and place objects on a conveyor belt. In this work we are interested in settings where the tasks are similar, yet not identical (e.g., due to uncertain orientation of objects) and motion planning needs to be extremely fast. Preprocessing-based approaches prove to be very beneficial in these settings. They analyze the configuration-space offline to generate some auxiliary information which can then be used in the query phase to speedup planning times. Typically, the tighter the requirement is on query times the larger the memory footprint will be. In particular, for high-dimensional spaces, providing real-time planning capabilities is extremely challenging. While there are planners that guarantee real-time performance by limiting the planning horizon, we are not aware of general-purpose planners capable of doing it for indefinite horizon (i.e., planning to the goal). To this end, we propose a preprocessing-based method that provides provable bounds on the query time while incurring only a small amount of memory overhead in the query phase. We evaluate our method on a 7-DOF robot arm and show a speedup of over tenfold in query time when compared to the PRM algorithm.