title: EnerVerse: Envisioning Embodied Future Space for Robotics Manipulation
summary: We introduce EnerVerse, a comprehensive framework for embodied future space
  generation specifically designed for robotic manipulation tasks. EnerVerse
  seamlessly integrates convolutional and bidirectional attention mechanisms for
  inner-chunk space modeling, ensuring low-level consistency and continuity.
  Recognizing the inherent redundancy in video data, we propose a sparse memory
  context combined with a chunkwise unidirectional generative paradigm to enable
  the generation of infinitely long sequences. To further augment robotic
  capabilities, we introduce the Free Anchor View (FAV) space, which provides
  flexible perspectives to enhance observation and analysis. The FAV space
  mitigates motion modeling ambiguity, removes physical constraints in confined
  environments, and significantly improves the robot's generalization and
  adaptability across various tasks and settings. To address the prohibitive
  costs and labor intensity of acquiring multi-camera observations, we present a
  data engine pipeline that integrates a generative model with 4D Gaussian
  Splatting (4DGS). This pipeline leverages the generative model's robust
  generalization capabilities and the spatial constraints provided by 4DGS,
  enabling an iterative enhancement of data quality and diversity, thus creating
  a data flywheel effect that effectively narrows the sim-to-real gap. Finally,
  our experiments demonstrate that the embodied future space generation prior
  substantially enhances policy predictive capabilities, resulting in improved
  overall performance, particularly in long-range robotic manipulation tasks.
paper_authors._id: 677b5c2478ac1cec9684059f
paper_authors.name: Siyuan Huang
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paper_authors.name: Pengfei Zhou
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paper_authors.fullname: ZHOU PENGFEI
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paper_authors.name: Shengcong Chen
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paper_authors.fullname: Ryan Chen
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paper_authors.name: Zhengkai Jiang
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paper_authors.fullname: Jiang Zhengkai
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paper_authors.name: Yue Hu
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paper_authors.name: Peng Gao
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paper_authors.name: Hongsheng Li
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paper_authors.fullname: Hongsheng LI
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paper_authors.name: Maoqing Yao
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paper_authors.name: Guanghui Ren
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paper_title: EnerVerse: Envisioning Embodied Future Space for Robotics Manipulation
paper_summary: We introduce EnerVerse, a comprehensive framework for embodied future space
  generation specifically designed for robotic manipulation tasks. EnerVerse
  seamlessly integrates convolutional and bidirectional attention mechanisms for
  inner-chunk space modeling, ensuring low-level consistency and continuity.
  Recognizing the inherent redundancy in video data, we propose a sparse memory
  context combined with a chunkwise unidirectional generative paradigm to enable
  the generation of infinitely long sequences. To further augment robotic
  capabilities, we introduce the Free Anchor View (FAV) space, which provides
  flexible perspectives to enhance observation and analysis. The FAV space
  mitigates motion modeling ambiguity, removes physical constraints in confined
  environments, and significantly improves the robot's generalization and
  adaptability across various tasks and settings. To address the prohibitive
  costs and labor intensity of acquiring multi-camera observations, we present a
  data engine pipeline that integrates a generative model with 4D Gaussian
  Splatting (4DGS). This pipeline leverages the generative model's robust
  generalization capabilities and the spatial constraints provided by 4DGS,
  enabling an iterative enhancement of data quality and diversity, thus creating
  a data flywheel effect that effectively narrows the sim-to-real gap. Finally,
  our experiments demonstrate that the embodied future space generation prior
  substantially enhances policy predictive capabilities, resulting in improved
  overall performance, particularly in long-range robotic manipulation tasks.
paper_ai_summary: EnerVerse enhances robotic manipulation by generating future space using convolutional and bidirectional attention, sparse memory, and generated multi-camera data.
paper_ai_keywords: convolutional mechanisms bidirectional attention mechanisms inner-chunk space modeling sparse memory context chunkwise unidirectional generative paradigm Free Anchor View FAV space 4D Gaussian Splatting 4DGS embodied future space generation policy predictive capabilities