ComboBench: Can LLMs Manipulate Physical Devices to Play Virtual Reality Games?

TL;DR

This paper introduces ComboBench, a benchmark to evaluate LLMs' ability to translate semantic actions into VR device manipulations, revealing strengths in task decomposition but limitations in procedural reasoning and spatial understanding.

Contribution

The paper presents the first comprehensive benchmark for assessing LLMs' VR device manipulation capabilities across multiple games and scenarios.

Findings

Top models like Gemini-1.5-Pro excel in task decomposition.

Models struggle with procedural reasoning and spatial understanding.

Few-shot learning improves LLM performance significantly.

Abstract

Virtual Reality (VR) games require players to translate high-level semantic actions into precise device manipulations using controllers and head-mounted displays (HMDs). While humans intuitively perform this translation based on common sense and embodied understanding, whether Large Language Models (LLMs) can effectively replicate this ability remains underexplored. This paper introduces a benchmark, ComboBench, evaluating LLMs' capability to translate semantic actions into VR device manipulation sequences across 262 scenarios from four popular VR games: Half-Life: Alyx, Into the Radius, Moss: Book II, and Vivecraft. We evaluate seven LLMs, including GPT-3.5, GPT-4, GPT-4o, Gemini-1.5-Pro, LLaMA-3-8B, Mixtral-8x7B, and GLM-4-Flash, compared against annotated ground truth and human performance. Our results reveal that while top-performing models like Gemini-1.5-Pro demonstrate strong task decomposition capabilities, they still struggle with procedural reasoning and spatial understanding compared to humans. Performance varies significantly across games, suggesting sensitivity to interaction complexity. Few-shot examples substantially improve performance, indicating potential for targeted enhancement of LLMs' VR manipulation capabilities. We release all materials at https://sites.google.com/view/combobench.