AquaWAM: A Dynamics-aware World Action Model for Underwater Embodied Agents

arXiv:2609.33299 · cs.RO, cs.AI · Submitted 2026-09-27 · Read on arXiv

cs.RO, cs.AI

Submitted: 2026-09-27

Updated: 2026-09-29

Terminology

• World Model — 9× in this paper · explained in 7 episodes
• Benchmark — 8× in this paper · explained in 2 episodes
• Fine-Tuning — 5× in this paper · explained in 5 episodes
• In International Conference on Learning Representations — 4× in this paper
• In Conference on Robot Learning — 3× in this paper
• Ablation — 2× in this paper · explained in 1 episode
• Deep Reinforcement Learning — 2× in this paper · explained in 2 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Reinforcement Learning — 2× in this paper · explained in 4 episodes
• Trace — 2× in this paper · explained in 2 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Action Flow — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Edge AI — 1× in this paper · explained in 2 episodes
• Flow Matching — 1× in this paper · explained in 8 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• In International Conference on Machine Learning — 1× in this paper
• International Conference on Robotics and Automation — 1× in this paper
• Latent Dynamics — 1× in this paper · explained in 2 episodes
• Online Adaptation — 1× in this paper · explained in 3 episodes
• Regret — 1× in this paper · explained in 3 episodes
• Vision-Language-Action (VLA) Models — 1× in this paper · explained in 2 episodes

Sources

• On Evaluation of Embodied Navigation Agents
• Layer Normalization
• WorldVLA: Towards Autoregressive Action World Model
• USIM and U0: A Vision-Language-Action Dataset and Model for General Underwater Robots
• World Models
• Dream to Control: Learning Behaviors by Latent Imagination
• TD-MPC2: Scalable, Robust World Models for Continuous Control
• Gaussian Error Linear Units (GELUs)
• GAIA-1: A Generative World Model for Autonomous Driving
• Cosmos Policy: Fine-Tuning Video Models for Visuomotor Control and Planning
• GR00T N1: An Open Foundation Model for Generalist Humanoid Robots
• Cosmos World Foundation Model Platform for Physical AI
• $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization
• SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics
• Learning Interactive Real-World Simulators
• World Action Models are Zero-shot Policies
• X-VLA: Soft-Prompted Transformer as Scalable Cross-Embodiment Vision-Language-Action Model

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