mlsanityData to 7 Oct 2026

How Far Can We Go with Pixels Alone? A Pilot Study on Screen-Only Navigation in Commercial 3D ARPGs

arXiv 2602.18981 ↗First posted 21 Feb 2026cs.AIcited by 0

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Abstract (arXiv)

Modern 3D game levels rely heavily on visual guidance, yet the navigability of level layouts remains difficult to quantify. Prior work either simulates play in simplified environments or analyzes static screenshots for visual affordances, but neither setting faithfully captures how players explore complex, real-world game levels. In this paper, we build on an existing open-source visual affordance detector and instantiate a screen-only exploration and navigation agent that operates purely from visual affordances. Our agent consumes live game frames, identifies salient interest points, and drives a simple finite-state controller over a minimal action space to explore Dark Souls-style linear levels and attempt to reach expected goal regions. Pilot experiments show that the agent can traverse most required segments and exhibits meaningful visual navigation behavior, but also highlight that limitations of the underlying visual model prevent truly comprehensive and reliable auto-navigation. We argue that this system provides a concrete, shared baseline and evaluation protocol for visual navigation in complex games, and we call for more attention to this necessary task. Our results suggest that purely vision-based sense-making models, with discrete single-modality inputs and without explicit reasoning, can effectively support navigation and environment understanding in idealized settings, but are unlikely to be a general solution on their own.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 17 of its 32 references that are in the map. First posted on arXiv 21 Feb 2026.

Builds on

17 of 32 references.

References · 17

  1. 2015FaceNet: A Unified Embedding for Face Recognition and Clusteringcited by 2,833
  2. 2017Curiosity-driven Exploration by Self-supervised Predictioncited by 1,173
  3. 2017AI2-THOR: An Interactive 3D Environment for Visual AIcited by 850
  4. 2018On Evaluation of Embodied Navigation Agentscited by 526
  5. 2018Gibson Env: Real-World Perception for Embodied Agentscited by 477
  6. 2022Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videoscited by 338
  7. 2020Learning to Explore using Active Neural SLAMcited by 308
  8. 2020First return, then explorecited by 256
  9. 2019Go-Explore: a New Approach for Hard-Exploration Problemscited by 207
  10. 2020Neural Topological SLAM for Visual Navigationcited by 193
  11. 2016Learning to Navigate in Complex Environmentscited by 192
  12. 2016ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learningcited by 178
5 more
  1. 2021Rapid Exploration for Open-World Navigation with Latent Goal Modelscited by 118
  2. 2020Occupancy Anticipation for Efficient Exploration and Navigationcited by 104
  3. 2016Playing FPS Games with Deep Reinforcement Learningcited by 95
  4. 2020Navigation and Exploration in 3D-Game Automated Play Testingcited by 4
  5. 2025Adaptive Visual Navigation Assistant in 3D RPGscited by 1

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