Omnijarvis: Unified Vision-language-action Tokenization Enables Open-world Instruction Following Agents

Wang Zihao, Cai Shaofei, Mu Zhancun, Lin Haowei, Zhang Ceyao, Liu Xuejie, Li Qing, Liu Anji, Ma Xiaojian, Liang Yitao. Arxiv 2024

[Paper]    
Agentic GPT Masked Language Model Model Architecture Multimodal Models Pretraining Methods Reinforcement Learning Tokenization Training Techniques Transformer

We present OmniJARVIS, a novel Vision-Language-Action (VLA) model for open-world instruction-following agents in open-world Minecraft. Compared to prior works that either emit textual goals to separate controllers or produce the control command directly, OmniJARVIS seeks a different path to ensure both strong reasoning and efficient decision-making capabilities via unified tokenization of multimodal interaction data. First, we introduce a self-supervised approach to learn a behavior encoder that produces discretized tokens for behavior trajectories \(\tau\) = {\(o_0\), \(a_0\), \(\dots\)} and an imitation learning (IL) policy decoder conditioned on these tokens. These additional behavior tokens will be augmented to the vocabulary of pretrained Multimodal Language Models (MLMs). With this encoder, we then pack long-term multimodal interactions involving task instructions, memories, thoughts, observations, textual responses, behavior trajectories, etc. into unified token sequences and model them with autoregressive transformers. Thanks to the semantically meaningful behavior tokens, the resulting VLA model, OmniJARVIS, can reason (by producing chain-of-thoughts), plan, answer questions, and act (by producing behavior tokens for the IL policy decoder). OmniJARVIS demonstrates excellent performances on a comprehensive collection of atomic, programmatic, and open-ended tasks in open-world Minecraft. Our analysis further unveils the crucial design principles in interaction data formation, unified tokenization, and its scaling potentials.

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