FLIP: Flow-Centric Generative Planning for General-Purpose Manipulation Tasks
Keywords: World Model, Long-Horizon Planning, Robot Manipulation, Flow Generation
TL;DR: We train an interactive world-model that enables model-based planning on the image flow and video spaces for diverse manipualtion tasks with only language-annotated video datasets.
Abstract: We aim to develop a model-based planning framework for world models that can be scaled with increasing model and data budgets for general-purpose manipulation tasks with only language and vision inputs. To this end, we present FLow-CentrIc generative Planning (FLIP), a model-based planning algorithm on visual space that features three key modules: 1) a multi-modal flow generation model as the general-purpose action proposal module; 2) a flow-conditioned video gen- eration model as the dynamics module; and 3) a vision-language representation learning model as the value module. Given an initial image and language instruction as the goal, FLIP can progressively search for long-horizon flow and video plans that maximize the discounted return to accomplish the task. FLIP is able to synthesize long-horizon plans across objects, robots, and tasks with image flows as the general action representation, and the dense flow information also provides rich guidance for long-horizon video generation. In addition, the synthesized flow and video plans can guide the training of low-level control policies for robot execution. Experiments on diverse benchmarks demonstrate that FLIP can improve both the success rates and quality of long-horizon video plan synthesis and has the interactive world model property, opening up wider applications for future works. Video demos are on our website: https://flow-planning.github.io/.
Supplementary Material: zip
Primary Area: applications to robotics, autonomy, planning
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Submission Number: 2338
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