VRScout: Towards Real-Time, Autonomous Testing of Virtual Reality Games (arxiv.org)

Researchers introduced VRScout, a deep-learning agent designed for real-time, autonomous testing of virtual reality games, addressing the scalability limits of human QA. VRScout learns from human demonstrations and mimics human-like navigation and object interaction to evaluate VR content automatically. This matters because VR testing faces unique challenges — high-dimensional sensory inputs, complex 3D interactions, and strict latency constraints — making traditional automated approaches for 2D/3D games inadequate. By operating autonomously, VRScout can reduce labor, accelerate release cycles, and support safety and content-audit pipelines. Technically, VRScout uses an enhanced Action Chunking Transformer that predicts multi-step action sequences, allowing the agent to capture higher-level strategies and generalize across varied VR environments. To meet real-time needs it implements a dynamically adjustable sliding horizon that adapts temporal context at runtime, balancing responsiveness and precision. The system achieves expert-level performance on commercial VR titles with only limited training data and runs at 60 FPS on consumer-grade hardware. These results suggest VRScout is a practical, scalable framework for automated VR QA and safety auditing, able to find gameplay issues and risky interactions without extensive human supervision.