VIMA: General Robot Manipulation with Multimodal Prompts

• URL: http://arxiv.org/abs/2210.03094v2
• Date: Sun, 28 May 2023 07:32:38 GMT
• Title: VIMA: General Robot Manipulation with Multimodal Prompts
• Authors: Yunfan Jiang, Agrim Gupta, Zichen Zhang, Guanzhi Wang, Yongqiang Dou, Yanjun Chen, Li Fei-Fei, Anima Anandkumar, Yuke Zhu, Linxi Fan
• Abstract summary: We show that a wide spectrum of robot manipulation tasks can be expressed with multimodal prompts. We develop a new simulation benchmark that consists of thousands of procedurally-generated tabletop tasks. We design a transformer-based robot agent, VIMA, that processes these prompts and outputs motor actions autoregressively.
• Score: 82.01214865117637
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Prompt-based learning has emerged as a successful paradigm in natural language processing, where a single general-purpose language model can be instructed to perform any task specified by input prompts. Yet task specification in robotics comes in various forms, such as imitating one-shot demonstrations, following language instructions, and reaching visual goals. They are often considered different tasks and tackled by specialized models. We show that a wide spectrum of robot manipulation tasks can be expressed with multimodal prompts, interleaving textual and visual tokens. Accordingly, we develop a new simulation benchmark that consists of thousands of procedurally-generated tabletop tasks with multimodal prompts, 600K+ expert trajectories for imitation learning, and a four-level evaluation protocol for systematic generalization. We design a transformer-based robot agent, VIMA, that processes these prompts and outputs motor actions autoregressively. VIMA features a recipe that achieves strong model scalability and data efficiency. It outperforms alternative designs in the hardest zero-shot generalization setting by up to $2.9\times$ task success rate given the same training data. With $10\times$ less training data, VIMA still performs $2.7\times$ better than the best competing variant. Code and video demos are available at https://vimalabs.github.io/

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