Disentangling Spurious Correlations in Vision-Language-Action Models via Predicting Domain-Invariant Latent Lookahead

arXiv:2609.37165 · cs.RO, cs.LG · Submitted 2026-09-29 · Read on arXiv

cs.RO, cs.LG

Submitted: 2026-09-29

Updated: 2026-09-29

Project page: https://dill-vla.github.io

Terminology

• Robustness — 29× in this paper · explained in 5 episodes
• Domain-Invariant Latent Lookahead — 12× in this paper
• Alignment — 11× in this paper · explained in 1 episode
• Benchmark — 7× in this paper · explained in 2 episodes
• Cosine Similarity — 7× in this paper · explained in 3 episodes
• Generalization — 7× in this paper · explained in 13 episodes
• Ablation — 6× in this paper · explained in 1 episode
• Adaptation — 6× in this paper · explained in 1 episode
• Distribution Shift — 6× in this paper · explained in 5 episodes
• World Model — 6× in this paper · explained in 7 episodes
• VLA Models — 5× in this paper · explained in 3 episodes
• Latent Space — 4× in this paper · explained in 7 episodes
• Ablation study — 3× in this paper · explained in 1 episode
• Accuracy — 3× in this paper · explained in 1 episode
• Fine-Tuning — 3× in this paper · explained in 5 episodes
• Segmentation Masks — 3× in this paper · explained in 2 episodes
• Benchmarking — 2× in this paper · explained in 3 episodes
• Diffusion Model — 2× in this paper · explained in 8 episodes
• Diffusion Models — 2× in this paper · explained in 6 episodes
• Grounding — 2× in this paper · explained in 3 episodes
• Linear probe — 2× in this paper · explained in 2 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• Self-Supervised Learning — 2× in this paper · explained in 7 episodes
• Vision-Language-Action (VLA) Models — 2× in this paper · explained in 2 episodes
• Action Flow — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Domain Shift — 1× in this paper · explained in 4 episodes
• Embodied AI — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Latent Diffusion Model — 1× in this paper · explained in 2 episodes
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• LoRA Adaptation — 1× in this paper · explained in 2 episodes
• LoRA Adapters — 1× in this paper · explained in 3 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes

Sources

• RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control
• OpenVLA: An Open-Source Vision-Language-Action Model
• $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
• Open X-Embodiment: Robotic Learning Datasets and RT-X Models
• DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset
• LIBERO-Plus: In-depth Robustness Analysis of Vision-Language-Action Models
• VLA-Arena: An Open-Source Framework for Benchmarking Vision-Language-Action Models
• LIBERO-X: Robustness Litmus for Vision-Language-Action Models
• Shortcut Learning in Generalist Robot Policies: The Role of Dataset Diversity and Fragmentation
• Qwen2.5-VL Technical Report
• FLARE: Robot Learning with Implicit World Modeling
• VLA-JEPA: Enhancing Vision-Language-Action Model with Latent World Model
• LIBERO-PRO: Towards Robust and Fair Evaluation of Vision-Language-Action Models Beyond Memorization
• View-Invariant Policy Learning via Zero-Shot Novel View Synthesis
• RoboUniView: Visual-Language Model with Unified View Representation for Robotic Manipulation
• InSpire: Vision-Language-Action Models with Intrinsic Spatial Reasoning
• Video Prediction Policy: A Generalist Robot Policy with Predictive Visual Representations
• Unleashing Large-Scale Video Generative Pre-training for Visual Robot Manipulation
• GR-2: A Generative Video-Language-Action Model with Web-Scale Knowledge for Robot Manipulation
• Ctrl-World: A Controllable Generative World Model for Robot Manipulation

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