EnerVerse: Envisioning Embodied Future Space for Robotics Manipulation

• URL: http://arxiv.org/abs/2501.01895v2
• Date: Mon, 10 Feb 2025 13:36:02 GMT
• Title: EnerVerse: Envisioning Embodied Future Space for Robotics Manipulation
• Authors: Siyuan Huang, Liliang Chen, Pengfei Zhou, Shengcong Chen, Zhengkai Jiang, Yue Hu, Yue Liao, Peng Gao, Hongsheng Li, Maoqing Yao, Guanghui Ren,
• Abstract summary: We introduce a generative robotics foundation model that constructs and interprets embodied spaces.<n>EnerVerse employs an autoregressive video diffusion framework to predict future embodied spaces from instructions, enhanced by a sparse context memory for long-term reasoning.<n>We present EnerVerse-D, a data engine pipeline combining the generative model with 4D Gaussian Splatting, forming a self-reinforcing data loop to reduce the sim-to-real gap.
• Score: 55.26713167507132
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce EnerVerse, a generative robotics foundation model that constructs and interprets embodied spaces. EnerVerse employs an autoregressive video diffusion framework to predict future embodied spaces from instructions, enhanced by a sparse context memory for long-term reasoning. To model the 3D robotics world, we propose Free Anchor Views (FAVs), a multi-view video representation offering flexible, task-adaptive perspectives to address challenges like motion ambiguity and environmental constraints. Additionally, we present EnerVerse-D, a data engine pipeline combining the generative model with 4D Gaussian Splatting, forming a self-reinforcing data loop to reduce the sim-to-real gap. Leveraging these innovations, EnerVerse translates 4D world representations into physical actions via a policy head (EnerVerse-A), enabling robots to execute task instructions. EnerVerse-A achieves state-of-the-art performance in both simulation and real-world settings.

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