SLIP-VLA: Single-Step Latent Imagination for Policy Learning in Vision-Language-Action Models

arXiv:2609.33575 · cs.RO · Submitted 2026-09-27 · Read on arXiv

cs.RO

Submitted: 2026-09-27

Updated: 2026-09-27

Comments: Project page and demonstration videos: https://haoxuanxu1024.github.io/SLIP_VLA/

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Alignment — 13× in this paper · explained in 1 episode
• Latent Representation — 11× in this paper · explained in 2 episodes
• Benchmark — 10× in this paper · explained in 2 episodes
• Latent Representations — 8× in this paper · explained in 3 episodes
• Distribution Shift — 6× in this paper · explained in 5 episodes
• World Model — 6× in this paper · explained in 7 episodes
• Ablation — 4× in this paper · explained in 1 episode
• Semantic Alignment — 3× in this paper · explained in 3 episodes
• Transformer — 3× in this paper · explained in 2 episodes
• Ablation study — 2× in this paper · explained in 1 episode
• Representation Learning — 2× in this paper · explained in 2 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• VLA Models — 2× in this paper · explained in 3 episodes
• Affordance — 1× in this paper · explained in 1 episode
• Alignment Methods — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Flow Matching — 1× in this paper · explained in 7 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Iterative Refinement — 1× in this paper · explained in 3 episodes
• Latent Dynamics — 1× in this paper · explained in 2 episodes
• Latent World Models — 1× in this paper · explained in 2 episodes
• Linear probe — 1× in this paper · explained in 2 episodes
• Tokenization — 1× in this paper · explained in 2 episodes
• Vision-Language-Action (VLA) Models — 1× in this paper · explained in 2 episodes

Sources

• OpenVLA: An Open-Source Vision-Language-Action Model
• $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
• $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization
• WorldVLA: Towards Autoregressive Action World Model
• FlowVLA: Visual Chain of Thought-based Motion Reasoning for Vision-Language-Action Models
• Motubrain: An Advanced World Action Model for Robot Control
• Causal World Modeling for Robot Control
• World Action Models are Zero-shot Policies
• Geometric Action Model for Robot Policy Learning
• AffordanceVLA: A Vision-Language-Action Model Empowering Action Generation through Affordance-Aware Understanding
• Learning Transferable Dynamics Priors from Action to World Modeling
• LeWorldModel: Stable End-to-End Joint-Embedding Predictive Architecture from Pixels
• Scaling World Model for Hierarchical Manipulation Policies
• StreamVLA: Breaking the Reason-Act Cycle via Completion-State Gating
• ThinkingVLA: Interleaved Vision and Language Reasoning for Robotic Manipulation
• mimic-video: Video-Action Models for Generalizable Robot Control Beyond VLAs
• Fast-WAM: Do World Action Models Need Test-time Future Imagination?

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