OmniJARVIS: Unified Vision-Language-Action Tokenization Enables Open-World Instruction Following Agents
zhwang4ai
Abstract
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.
Community
See our Project link: https://craftjarvis.org/OmniJarvis/)
The code and dataset will be released.
Code (Coming Soon): https://github.com/CraftJarvis/OmniJARVIS
Dataset (Coming Soon): https://huggingface.co./datasets/zhwang4ai/Minecraft-EQA-300k
The recording of OmniJARVIS playing in Minecraft.
We compare existing Vision-Language-Action models in the Figure.
(a) depicts a model where upon receiving a language instruction, actions are directly output based on the environmental state, facilitating immediate interaction with the environment at a unified frequency. Smaller models with less than 1B parameters like VPT maintain higher frequencies (>20Hz), though their capability for complex reasoning tasks is limited. Larger models with >7B parameters such as RT-2, offer enhanced performance but operate at significantly reduced frequencies (2-3Hz).
(b) illustrates a common approach utilizing large vision-language models for planning, subsequently outputting language goals like PaLM-E and DEPS. A language-conditioned policy then translates these language goals into actions at a real-time interaction rate of 20Hz, with high-level models re-planning at less than 1Hz. This hierarchical structure balances interaction frequency and performance, while it requires language as an intermediary and additional language labels. The training process of high-level vision-language models and language-conditioned policies are separate, thus performing poorly on tasks that can not be easily connected by language.
(c) (ours) mirrors the hierarchical structure of (b) but differentiates by employing a self-supervised encoder-decoder policy and FSQ quantization as a behavior tokenizer. The upper-level vision-language models produce self-supervised behavior tokens, which are then conditioned by a policy decoder to output actions, facilitating environment interaction. The behavior tokens are injected into the training corpus of vision-language-action models, which enables end-to-end inference. This approach also eliminates the need for external language supervision and scales efficiently.
OmniJARVIS seeks a different path to ensure both strong reasoning and efficient decision-making capabilities via unified tokenization of multimodal interaction data.
We first train a self-supervised behavior tokenizer of OmniJARVIS. We modify the VAE-based self-supervised learning of behavior trajectories in GROOT to train the behavior tokenizer and de-tokenizer in OmniJARVIS. Specifically, we adopt the auto-encoding objective but replace the Gaussian latent with a discrete representation based on Finite Scalar Quantizer. The encoder will then be used as the behavior tokenizer to produce discrete tokens from the actions (behavior trajectories) in multimodal interaction data, while the behavior tokens emitted by OmniJARVIS will be sent to the policy decoder to perform motor control.
The architecture and inference pipeline of OmniJARVIS. The main body of OmniJARVIS is a multimodal language model (MLM) augmented with additional behavior tokens. Given a task instruction, initial memory, and observation, OmniJARVIS will iteratively perform chain-of-thought reasoning and produce behavior tokens as a means of control via the decoder policy (behavior de-tokenizer). Every N steps, OmniJARVIS is forced to reason again and produce new behavior tokens with the latest observation. (Not shown above) OmniJARVIS can also make textual responses, e.g. answering questions.
Hi @zhwang4ai congrats on this work!
Would be great to link your dataset to this paper, see here on how to do that: https://huggingface.co./docs/hub/en/datasets-cards#linking-a-paper.
Also, if you're planning on sharing models, here's the guide regarding uploading models (including custom PyTorch ones): https://huggingface.co./docs/hub/models-uploading
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