# Sensorless Pose Determination using Randomized Action Sequences

## TL;DR

This paper demonstrates that randomized action sequences can effectively reduce pose uncertainty in 2D object manipulation without sensing or planning, supported by simulations and real robot experiments.

## Contribution

It shows that random actions can converge to a determined object pose, extending prior work by Erdmann and Mason with a new, sensorless approach.

## Key findings

- Randomized actions reduce object pose entropy.
- Convergence occurs under certain conditions with sufficient sequence length.
- Effectiveness varies with object shape and surface friction.

## Abstract

This paper is a study of 2D manipulation without sensing and planning, by exploring the effects of unplanned randomized action sequences on 2D object pose uncertainty. Our approach follows the work of Erdmann and Mason's sensorless reorienting of an object into a completely determined pose, regardless of its initial pose. While Erdmann and Mason proposed a method using Newtonian mechanics, this paper shows that under some circumstances, a long enough sequence of random actions will also converge toward a determined final pose of the object. This is verified through several simulation and real robot experiments where randomized action sequences are shown to reduce entropy of the object pose distribution. The effects of varying object shapes, action sequences, and surface friction are also explored.