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原文摘要

This paper focuses on improving the behavioral diversity and effectiveness of language-conditioned manipulation policies trained through imitation learning. Recently, continuous-action policies have become mainstream in robotic manipulation, but the lack of diverse demonstrations tends to restrict their behavioral diversity. For grasping, a fundamental subtask of manipulation, 6-DoF grasp detection provides diverse grasp hypotheses, but open-loop execution constrains responsiveness in dynamic environments. This motivates us to combine the diversity of grasp hypotheses with the adaptability of continuous-action policies. To this end, we propose GraSLA, a lightweight and effective spatial-language-action model with grasp hypotheses as geometric priors. In particular, we employ spatial and language foundation models to encode scene geometry and task semantics, and integrate them with grasp hypotheses through bidirectional cross-attention for continuous action prediction. On LIBERO, GraSLA achieves an average success rate of 99.30%.