ST-$π$: Structured SpatioTemporal VLA for Robotic Manipulation

• URL: http://arxiv.org/abs/2604.17880v1
• Date: Mon, 20 Apr 2026 06:48:47 GMT
• Title: ST-$π$: Structured SpatioTemporal VLA for Robotic Manipulation
• Abstract summary: We propose a structured structuredtemporal V model for robotic manipulation.<n>Our model is guided by two key designs: 1) Stemporal VLM and 2) Stemporal action expert.<n>In addition, we propose a real-world robotic dataset with annotations for fine-tuning.
• Score: 24.44733656837927
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision-language-action (VLA) models have achieved great success on general robotic tasks, but still face challenges in fine-grained spatiotemporal manipulation. Typically, existing methods mainly embed spatiotemporal knowledge into visual and action representations, and directly perform a cross-modal mapping for step-level action prediction. However, such spatiotemporal reasoning remains largely implicit, making it difficult to handle multiple sequential behaviors with explicit spatiotemporal boundaries. In this work, we propose ST-$π$, a structured spatiotemporal VLA model for robotic manipulation. Our model is guided by two key designs: 1) Spatiotemporal VLM. We encode 4D observations and task instructions into latent spaces, and feed them into the LLM to generate a sequence of causally ordered chunk-level action prompts consisting of sub-tasks, spatial grounding and temporal grounding. 2) Spatiotemporal action expert. Conditioned on chunk-level action prompts, we design a structured dual-generator guidance to jointly model spatial dependencies and temporal causality, thus predicting step-level action parameters. Within this structured framework, the VLM explicitly plans global spatiotemporal behavior, and the action expert further refines local spatiotemporal control. In addition, we propose a real-world robotic dataset with structured spatiotemporal annotations for fine-tuning. Extensive experiments have been conducted to demonstrate the effectiveness of our model. Our code link: https://github.com/chuanhaoma/ST-pi.

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