Exploring Performance in Complex Search-and-Retrieve Tasks: A Comparative Analysis of PPO and GAIL Robots

Abstract

Prior research has extensively examined supervised and unsupervised methods for search tasks, focusing primarily on individual model accuracy and performance. However, there’s a gap in understanding how these models collaborate during complex tasks. This study’s primary objective was to evaluate a simulated robot employing Proximal Policy Optimization (PPO) and another using Generative Adversarial Imitation Learning (GAIL) algorithms, working together for a search-and-retrieve task. The environment, built in Unity3D, contained 56 distractors (e.g., walls, tables, and chairs) with negative points and 112 targets (e.g., pistol, laptop, and armour) with positive points during the gameplay. A PPO robot searched for the targets within an environment with a GAIL robot player. The results demonstrated that the inclusion of the GAIL robot along with the PPO robot led to superior performance of the multi-robot team in the search-and-retrieve task. The GAIL robot outperformed the PPO robot when both players performed individually in the search-and-retrieve task. The PPO robots demonstrated comparatively poorer performance when performing alone in a task without GAIL players. Our findings highlight the importance of collaborative multi-robot search involving generative imitation learning within a simulated environment.