Vision-Language-Delta-Action: Learning Compact Deltas as Actionable Transition Interfaces for Robotic Manipulation

Key points

• Robotic manipulation is not merely observation-to-action generation, but the realization of task-specified state changes in the physical world.1
• Existing vision-language-action (VLA) methods directly learn observation-to-action mappings, imposing a substantial learning burden due to the gap between semantic understanding and action execution.1
• LT-Filter captures task-relevant manipulation-centric changes while suppressing static reconstruction biases.1
• Experiments show that VLDA improves long-horizon consistency, robustness, and generalization, validating actionable Delta as an alternative to direct action prediction and full future-state modeling.1

Sources (2)

• [1]Vision-Language-Delta-Action: Learning Compact Deltas as Actionable Transition Interfaces for Robotic Manipulation
  Hugging Face Daily Papers · Oct 19, 12:00 AM

  We propose Vision-Language-Delta-Action (VLDA), a paradigm that reformulates full future prediction as compact actionable transition modeling.

  Robotic manipulation is not merely observation-to-action generation, but the realization of task-specified state changes in the physical world.

• [2]Vision-Language-Delta-Action: Learning Compact Deltas as Actionable Transition Interfaces for Robotic Manipulation
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 06:35 AM · same content

Extractive summary: sentences quoted from the sources.