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arXiv · 2501.01895

EnerVerse: Envisioning Embodied Future Space for Robotics Manipulation

Siyuan Huang·Liliang Chen·Pengfei Zhou·Shengcong Chen·Zhengkai Jiang·Yue Hu·Yue Liao·Peng Gao·Hongsheng Li·Maoqing Yao·Guanghui Ren
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Abstract

We introduce EnerVerse, a generative robotics foundation model that constructs and interprets embodied spaces. EnerVerse employs a chunk-wise 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 adopt a multi-view video representation, providing rich perspectives to address challenges like motion ambiguity and 3D grounding. Additionally, EnerVerse-D, a data engine pipeline combining generative modeling with 4D Gaussian Splatting, forms 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), achieving state-of-the-art performance in both simulation and real-world tasks. For efficiency, EnerVerse-A reuses features from the first denoising step and predicts action chunks, achieving about 280 ms per 8-step action chunk on a single RTX 4090. Further video demos, dataset samples could be found in our project page.

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Siyuan Huang, Liliang Chen, Pengfei Zhou, Shengcong Chen, Zhengkai Jiang, Yue Hu, Yue Liao, Peng Gao, Hongsheng Li, Maoqing Yao, Guanghui Ren. 2025-01-03. EnerVerse: Envisioning Embodied Future Space for Robotics Manipulation. https://arxiv.org/abs/2501.01895

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