Rewiring Semantics, Dynamics, and Control: A Simple yet Effective Action-Centric Tri-Stream Transformer

arXiv:2610.11416 · cs.RO, cs.AI · Submitted 2026-10-08 · Read on arXiv

cs.RO, cs.AI

Submitted: 2026-10-08

Updated: 2026-10-08

Terminology

• Transformer — 26× in this paper · explained in 2 episodes
• Semantic Understanding — 11× in this paper · explained in 2 episodes
• Ablation — 7× in this paper · explained in 1 episode
• Adaptation — 7× in this paper · explained in 1 episode
• Benchmark — 6× in this paper · explained in 2 episodes
• Fine-Tuning — 6× in this paper · explained in 5 episodes
• World Model — 6× in this paper · explained in 7 episodes
• Distillation — 5× in this paper · explained in 2 episodes
• Generalization — 5× in this paper · explained in 13 episodes
• Robustness — 5× in this paper · explained in 5 episodes
• Flow Matching — 4× in this paper · explained in 8 episodes
• Foundation Model — 4× in this paper · explained in 3 episodes
• Self-distillation — 4× in this paper · explained in 6 episodes
• Vision-Language-Action (VLA) Models — 3× in this paper · explained in 2 episodes
• Benchmarking — 2× in this paper · explained in 3 episodes
• Distribution Shift — 2× in this paper · explained in 5 episodes
• Foundation Models — 2× in this paper · explained in 7 episodes
• Grounding — 2× in this paper · explained in 3 episodes
• Joint Attention — 2× in this paper · explained in 2 episodes
• Aggregation — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Compositional Generalization — 1× in this paper · explained in 4 episodes
• Core — 1× in this paper · explained in 2 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Process — 1× in this paper · explained in 2 episodes
• Embodied AI — 1× in this paper · explained in 2 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Gradient Flow — 1× in this paper · explained in 2 episodes
• Steering — 1× in this paper · explained in 2 episodes
• Vision-Language Models (VLMs) — 1× in this paper · explained in 6 episodes

Sources

• World Simulation with Video Foundation Models for Physical AI
• Qwen3-VL Technical Report
• PaliGemma: A versatile 3B VLM for transfer
• GR00T N1: An Open Foundation Model for Generalist Humanoid Robots
• $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
• RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control
• InternVLA-A1: Unifying Understanding, Generation and Action for Robotic Manipulation
• WorldVLA: Towards Autoregressive Action World Model
• GR-2: A Generative Video-Language-Action Model with Web-Scale Knowledge for Robot Manipulation
• LIBERO-Plus: In-depth Robustness Analysis of Vision-Language-Action Models
• Video Prediction Policy: A Generalist Robot Policy with Predictive Visual Representations
• BagelVLA: Enhancing Long-Horizon Manipulation via Interleaved Vision-Language-Action Generation
• OpenVLA: An Open-Source Vision-Language-Action Model
• Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success
• Unified Video Action Model
• TriVLA: A Triple-System-Based Unified Vision-Language-Action Model with Episodic World Modeling for General Robot Control
• F1: A Vision-Language-Action Model Bridging Understanding and Generation to Actions
• InternVLA-A1.5: Unifying Understanding, Latent Foresight, and Action for Compositional Generalization
• RoboCasa: Large-Scale Simulation of Everyday Tasks for Generalist Robots
• $\pi^{*}_{0.6}$: a VLA That Learns From Experience

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