LLaRA: Supercharging Robot Learning Data for Vision-Language Policy
Keywords: Robot Learning, VLM, Behavior Cloning, Instruction Tuning, Self-supervised Learning
TL;DR: Effortlessly transform a pretrained VLM into a robot policy and enhance its performance using auxiliary data generated from the same behavior cloning dataset.
Abstract: LLMs with visual inputs, i.e., Vision Language Models (VLMs), have the capacity to process state information as visual-textual prompts and respond with policy decisions in text. We propose LLaRA: Large Language and Robotics Assistant, a framework that formulates robot action policy as conversations and provides improved action output when trained with auxiliary data that complements policy learning. We first introduce an automated pipeline to generate conversation-style instruction tuning data from existing behavior cloning data. Then we enrich the dataset in a self-supervised fashion by formulating six auxiliary tasks. A VLM finetuned with the resulting collection of datasets can generate meaningful robot action policy decisions. Our experiments across multiple simulated and real-world environments demonstrate the state-of-the-art performance of the proposed LLaRA framework. The code, datasets, and pretrained models will be made publicly available.
Supplementary Material: zip
Primary Area: applications to robotics, autonomy, planning
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Submission Number: 8021
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