[Paper] NavWAM: A Navigation World Action Model for Goal-Conditioned Visual Navigation
Source: arXiv - 2606.13494v1
Overview
Goal-conditioned visual navigation requires a robot to act under partial observability by anticipating how its motion will change the future egocentric view and whether that change brings it closer to the goal. Navigation world models provide such visual foresight, but they remain prediction modules that require an external planner to convert predicted futures into closed-loop control. We propose Navigation World Action Model (NavWAM), a diffusion-transformer policy that turns navigation world-model prediction into executable action by representing future observations, goal-progress values, and action chunks in a shared latent sequence. By learning future prediction jointly with the action and value targets that determine closed-loop behavior, NavWAM makes visual foresight directly usable for robot control. We build NavWAM through simulation pretraining and real-robot adaptation, and evaluate it on image-goal navigation against planning-based world models and a representative direct navigation policy. Across offline benchmarks and closed-loop real-robot deployment, NavWAM improves over planning-based world-model baselines in our evaluations while using the default policy mode without CEM-style action search. Project page: https://dachii-azm.github.io/navwam/
Key Contributions
This paper presents research in the following areas:
- cs.RO
- cs.CV
Methodology
Please refer to the full paper for detailed methodology.
Practical Implications
This research contributes to the advancement of cs.RO.
Authors
- Daichi Azuma
- Taiki Miyanishi
- Koya Sakamoto
- Shuhei Kurita
- Yaonan Zhu
- Petr Khrapchenkov
- Motoaki Kawanabe
- Yusuke Iwasawa
- Yutaka Matsuo
Paper Information
- arXiv ID: 2606.13494v1
- Categories: cs.RO, cs.CV
- Published: June 11, 2026
- PDF: Download PDF