# Active End-Effector Pose Selection for Tactile Object Recognition through Monte Carlo Tree Search

## TL;DR

This paper introduces an active tactile object recognition method that adaptively selects wrist poses using Monte Carlo tree search to efficiently identify objects with minimal touches, demonstrated in simulation and real robot experiments.

## Contribution

It presents a novel approach for active tactile recognition using Monte Carlo tree search to optimize wrist pose sequences independent of workspace coordinates.

## Key findings

- Most objects recognized in at most 16 grasps in simulation.
- Objects recognized in 2-9 grasps on a real robot.
- Outperforms a greedy baseline in recognition efficiency.

## Abstract

This paper considers the problem of active object recognition using touch only. The focus is on adaptively selecting a sequence of wrist poses that achieves accurate recognition by enclosure grasps. It seeks to minimize the number of touches and maximize recognition confidence. The actions are formulated as wrist poses relative to each other, making the algorithm independent of absolute workspace coordinates. The optimal sequence is approximated by Monte Carlo tree search. We demonstrate results in a physics engine and on a real robot. In the physics engine, most object instances were recognized in at most 16 grasps. On a real robot, our method recognized objects in 2--9 grasps and outperformed a greedy baseline.