InstructVLA:
Vision-Language-Action Instruction Tuning
from Understanding to Manipulation

To operate effectively in the real world, robots must integrate multimodal reasoning with precise action generation. However, existing vision-language-action (VLA) models often sacrifice one for the other, narrow their abilities to task-specific manipulation data, and suffer catastrophic forgetting of pre-trained vision-language capabilities. To bridge this gap, we introduce InstructVLA, an end-to-end VLA model that preserves the flexible reasoning of large vision-language models (VLMs) while delivering leading manipulation performance. InstructVLA introduces a novel training paradigm, Vision-Language-Action Instruction Tuning (VLA-IT), which employs multimodal training with mixture-of-experts adaptation to jointly optimize textual reasoning and action generation on both standard VLM corpora and a curated 650K-sample VLA-IT dataset. On in-domain SimplerEnv tasks, InstructVLA achieves 30.5% improvement over SpatialVLA. To evaluate generalization, we introduce SimplerEnv-Instruct, an 80-task benchmark requiring closed-loop control and high-level instruction understanding, where it outperforms a fine-tuned OpenVLA by 92% and an action expert aided by GPT-4o by 29%. Additionally, InstructVLA surpasses baseline VLMs on multimodal tasks and exhibits inference-time scaling by leveraging textual reasoning to boost manipulation performance in both simulated and real-world settings. These results demonstrate InstructVLA's potential for bridging intuitive and steerable human-robot interaction with efficient policy learning.

• We propose InstructVLA, a VLA architecture and training pipeline that emphasizes the importance of language capability in VLAs by efficiently preserving pretrained vision-language knowledge from VLMs while integrating manipulation as a component of instruction following.
• We design a practical data and evaluation pipeline for vision-language-action instruction following, supported by 650K tailored VLA-IT annotations and a manually curated benchmark suite, enabling evaluation of VLAs' instruction generalization capabilities.
• InstructVLA achieves leading performance across robotic manipulation tasks, multimodal benchmarks, and real-world deployments, enabling intuitive and controllable manipulation.

We curate the Vision-Language-Action Instruction Tuning (VLA-IT) dataset, consisting of 650K human-robot interactions annotated with diverse instructions, scene captions, and question-answer pairs grounded in high-quality manipulation tasks.

We introduce the SimplerEnv-Instruct benchmark, a manually designed evaluation suite featuring 80 zero-shot manipulation tasks. It encompasses both closed-loop manipulation tasks and high-level instruction reasoning, involving either situated understanding or decomposition into actionable subtasks.

Experiments includes: (1) Real-world Experiments. Few-shot experiments on the Franka Research 3 robot and zero-shot experiments on the WidowX-250 Arm. (2) Multimodal understanding performance. (3) Robotic manipulation performance. Google Robot and WidowX Robot denote two embodiments in SimplerEnv. For SimplerEnv-Instruct, we focus on two reasoning levels, Instruction Aggregation and Situated Reasoning.