InSight: Self-Guided Skill Acquisition via Steerable VLAs

InSight: Self-guided autonomous skill acquisition for vision-language-action models via primitive steerability

InSight is a framework enabling VLA models to autonomously acquire new manipulation skills beyond their training data by decomposing demonstrations into labeled primitive actions (e.g., 'move gripper to bowl', 'pour the bottle') and running a VLM-guided data flywheel that identifies missing primitives, attempts demonstrations, and integrates successful ones back into training. The system requires no human demonstrations of target skills and is evaluated on simulation and real-world tasks including block flipping, drawer closing, sweeping, and pouring. Learned primitives can be composed for novel long-horizon tasks, offering a practical path toward continual skill acquisition in robotic VLA policies.