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vila look before you leap unveiling the power of gpt 4v in robotic vision language planning yingdong hu 1 2 3 fanqi lin 1 2 3 tong zhang 1 2 3 li yi 1 2 3 yang gao 1 2 3 1 tsinghua university 2 shanghai artificial intelligence laboratory 3 shanghai qi zhi institute equal contribution paper arxiv video code coming soon v i l a is a simple and effective method for long horizon robotic task planning by directly integrating vision into the reasoning and planning process v i l a can solve a variety of complex long horizon tasks both in real world and simulated settings v i l a demonstrates the capability to solve a wide array of real world everyday manipulation tasks in a zero shot manner efficiently handling diverse open set instructions and objects abstract in this study we are interested in imbuing robots with the capability of physically grounded task planning recent advancements have shown that large language models llms possess extensive knowledge useful in robotic tasks especially in reasoning and planning however llms are constrained by their lack of world grounding and dependence on external affordance models to perceive environmental information which cannot jointly reason with llms we argue that a task planner should be an inherently grounded unified multimodal system to this end we introduce robotic vi sion la nguage planning v i l a a novel approach for long horizon robotic planning that leverages vision language models vlms to generate a sequence of actionable steps v i l a directly integrates perceptual data into its reasoning and planning process enabling a profound understanding of commonsense knowledge in the visual world including spatial layouts and object attributes it also supports flexible multimodal goal specification and naturally incorporates visual feedback our extensive evaluation conducted in both real robot and simulated environments demonstrates v i l a s superiority over existing llm based planners highlighting its effectiveness in a wide array of open world manipulation tasks video vila given a language instruction and current visual observation we leverage a vlm gpt 4v to comprehend the environment scene through chain of thought reasoning subsequently generating a sequence of actionable steps the first step of this plan is then executed by a primitive policy finally the step that has been executed is added to the finished plan enabling a closed loop planning method in dynamic environments comprehension of common sense in the visual world v i l a excels in complex tasks that demand an understanding of spatial layouts or object attributes this kind of commonsense knowledge pervades nearly every task of interest in robotics but previous llm based planners consistently fall short in this regard spatial layouts pour chips v1 pour chips v2 bring pepsi can v1 bring pepsi can v2 take out marvel model v1 take out marvel model v2 bring empty plate object attributes pick righteous characters pick fresh fruits stack plates steadily prepare art class fit objects in box garbage classification prepare play area versatile goal specifications v i l a supports flexible multimodal goal specification approaches it is capable of utilizing not just language instrctions but also diverse forms of goal images and even a blend of both language and images to define objectives effectively tasks arrange sushi arrange jigsaw pieces pick vegetables tidy up study desk vegetable placement make fruit platter relocate desk items goal image goal type real image visual feedback v i l a effectively utilizes visual feedback in an intuitive and natural way enabling robust closed loop planning in dynamic environments stack blocks pack chip bags find cat food human robot interaction simulation experiments we show that v i l a can rearrange the objects on the table in some desired configuration specified by high level language instructions blocks bowls stack blocks put blocks on corner side put blocks matching colors put blocks mismatching colors put blocks different corners stack blocks cool colors stack blocks warm colors stack primary color blocks letters put letters alphabetical order spell word separate vowels put letters reverse alphabetical order spell sport sort symmetrical letters separate consonants sort letters less than d stack all the blocks put the letters on the tables in alphabetical orders bibtex article hu2023look title look before you leap unveiling the power of gpt 4v in robotic vision language planning author yingdong hu and fanqi lin and tong zhang and li yi and yang gao journal arxiv preprint arxiv 2311 17842 year 2023 website template borrowed from voxposer