VLA-$Δ$: Learning Physical State Transition for Vision-Language-Action Models

Key points

• Some methods introduce intermediate reasoning or future-state prediction in language, visual, or latent spaces to guide action generation, but do not explicitly supervise the physical state transition itself.1
• We therefore propose VLA-$Δ$, a general framework that uses physical changes between current and future states as explicit learning targets.1
• To support this objective, we construct a large-scale pretraining dataset and derive physical state-transition annotations covering task-relevant semantics and visual motion changes, providing more direct supervision for continuous action generation.1
• VLA-$Δ$ achieves performance competitive with the state of the art on LIBERO and RoboTwin, and outperforms $π{0.5}$ in real-robot experiments.1

Sources (2)

• [1]VLA-$Δ$: Learning Physical State Transition for Vision-Language-Action Models
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 15, 05:54 AM

  Vision-language-action (VLA) models transfer semantic knowledge acquired through vision-language pretraining to robot control, achieving strong performance across various benchmarks.

  Some methods introduce intermediate reasoning or future-state prediction in language, visual, or latent spaces to guide action generation, but do not explicitly supervise the physical state transition itself.

• [2]VLA-Δ: Learning Physical State Transition for Vision-Language-Action Models
  Hugging Face Daily Papers · Oct 15, 12:00 AM · same content

Extractive summary: sentences quoted from the sources.