Direction-Scale Decomposition in Action Representation: Rethinking What to Tokenize for Vision-Language-Action Models

arXiv:2609.28865 · cs.CV, cs.RO · Submitted 2026-09-24 · Read on arXiv

cs.CV, cs.RO

Submitted: 2026-09-24

Updated: 2026-09-24

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

Terminology

• Tokenization — 21× in this paper · explained in 2 episodes
• Fine-Tuning — 7× in this paper · explained in 5 episodes
• Quantization — 6× in this paper · explained in 12 episodes
• Direction-Scale Decomposition — 4× in this paper
• Alignment — 3× in this paper · explained in 1 episode
• Action Space — 2× in this paper · explained in 1 episode
• Failure Modes — 2× in this paper · explained in 3 episodes
• Fidelity — 2× in this paper · explained in 2 episodes
• Keypoint Action Tokens — 2× in this paper
• Benchmarking — 1× in this paper · explained in 3 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Dynamic Time Warping (DTW) — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• VLA Models — 1× in this paper · explained in 3 episodes
• Vision-Language-Action (VLA) Models — 1× in this paper · explained in 2 episodes
• Zero-Shot Transfer — 1× in this paper · explained in 4 episodes

Sources

• RT-1: Robotics Transformer for Real-World Control at Scale
• RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control
• $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
• Discrete Diffusion VLA: Bringing Discrete Diffusion to Action Decoding in Vision-Language-Action Policies
• FAST: Efficient Action Tokenization for Vision-Language-Action Models
• $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization
• ActionCodec: What Makes for Good Action Tokenizers
• Keypoint Action Tokens Enable In-Context Imitation Learning in Robotics
• SpatialVLA: Exploring Spatial Representations for Visual-Language-Action Model
• MoMo: Dial Motion Mode in Robot Manipulation with Spatiotemporal Action Tokenization
• Open X-Embodiment: Robotic Learning Datasets and RT-X Models
• LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning
• Evaluating Real-World Robot Manipulation Policies in Simulation

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