Egocentric Vision Language Planning
<|reference_start|>Egocentric Vision Language Planning: We explore leveraging large multi-modal models (LMMs) and text2image models to build a more general embodied agent. LMMs excel in planning long-horizon tasks over symbolic abstractions but struggle with grounding in the physical world, often failing to accurately identify object positions in images. A bridge is needed to connect LMMs to the physical world. The paper proposes a novel approach, egocentric vision language planning (EgoPlan), to handle long-horizon tasks from an egocentric perspective in varying household scenarios. This model leverages a diffusion model to simulate the fundamental dynamics between states and actions, integrating techniques like style transfer and optical flow to enhance generalization across different environmental dynamics. The LMM serves as a planner, breaking down instructions into sub-goals and selecting actions based on their alignment with these sub-goals, thus enabling more generalized and effective decision-making. Experiments show that EgoPlan improves long-horizon task success rates from the egocentric view compared to baselines across household scenarios.<|reference_end|>
@article{fang2024egocentric,
    title={Egocentric Vision Language Planning},
    author={Zhirui Fang, Ming Yang, Weishuai Zeng, Boyu Li, Junpeng Yue, Ziluo
  Ding, Xiu Li, Zongqing Lu},
    journal={arXiv preprint arXiv:2408.05802},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.05802},
    primaryClass={cs.CV}
}