SlotVLA: Towards Modeling of Object-Relation Representations in Robotic Manipulation
SlotVLA moves beyond dense visual embeddings by creating compact, interpretable representations for robotic manipulation. It uses slot attention and task-aware filtering to extract relevant objects and their interactions, resulting in a much smaller input for action decoding. This method achieves high efficiency while maintaining strong generalization across complex manipulation tasks.