Egocentric Vision Language Planning

• URL: http://arxiv.org/abs/2408.05802v1
• Date: Sun, 11 Aug 2024 15:37:29 GMT
• Title: Egocentric Vision Language Planning
• Authors: Zhirui Fang, Ming Yang, Weishuai Zeng, Boyu Li, Junpeng Yue, Ziluo Ding, Xiu Li, Zongqing Lu,
• Abstract summary: We explore leveraging large multi-modal models (LMMs) and text2image models to build a more general embodied agent. The paper proposes a novel approach, egocentric vision language planning (EgoPlan), to handle long-horizon tasks from an egocentric perspective.
• Score: 44.436317004108105
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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