# Bandit-Based Model Selection for Deformable Object Manipulation

## TL;DR

This paper introduces a bandit-based framework for deformable object manipulation that adaptively selects models without requiring precise modeling, improving performance in dynamic and uncertain environments.

## Contribution

The paper formulates deformable object manipulation as a Multi-Armed Bandit problem and proposes a Kalman Filtering approach for non-stationary bandits to enhance model selection.

## Key findings

- Outperforms previous methods on synthetic trials
- Performs competitively on simulation tasks
- Effectively adapts to changing object deformations

## Abstract

We present a novel approach to deformable object manipulation that does not rely on highly-accurate modeling. The key contribution of this paper is to formulate the task as a Multi-Armed Bandit problem, with each arm representing a model of the deformable object. To "pull" an arm and evaluate its utility, we use the arm's model to generate a velocity command for the gripper(s) holding the object and execute it. As the task proceeds and the object deforms, the utility of each model can change. Our framework estimates these changes and balances exploration of the model set with exploitation of high-utility models. We also propose an approach based on Kalman Filtering for Non-stationary Multi-armed Normal Bandits (KF-MANB) to leverage the coupling between models to learn more from each arm pull. We demonstrate that our method outperforms previous methods on synthetic trials, and performs competitively on several manipulation tasks in simulation.