EnerVerse: Envisioning Embodied Future Space for Robotics Manipulation
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.
677b5c2478ac1cec9684059f
Siyuan Huang
claimed_verified
2025-01-06T07:59:36.568Z
634e4120038b5879133552f5
/avatars/34ec861b4bbf1aecf927a7d6e726c7a4.svg
Siyuan
user
SiyuanH
677b5c2478ac1cec968405a0
Liliang Chen
admin_assigned
2025-01-06T08:21:46.139Z
640b00555a9c21b95c6449b3
/avatars/5fa43b956f3acc671f033e31b7ca76c5.svg
Liliang Chen
user
pathcn
677b5c2478ac1cec968405a1
Pengfei Zhou
admin_assigned
2025-01-06T08:22:24.033Z
65df481e530333731ea24617
/avatars/3ed41f5d7d0489193807b5e6260f16c9.svg
ZHOU PENGFEI
user
lyuukuu
677b5c2478ac1cec968405a2
Shengcong Chen
claimed_verified
2025-05-14T12:18:25.565Z
66c4816e96583c59b09fec30
https://cdn-avatars.huggingface.co/v1/production/uploads/66c4816e96583c59b09fec30/RLurCsmcgOfyuWVkpsv1L.jpeg
Ryan Chen
user
ryancll118
677b5c2478ac1cec968405a3
Zhengkai Jiang
admin_assigned
2025-01-06T08:22:42.112Z
67593dd0f522f4409e614ba0
https://cdn-avatars.huggingface.co/v1/production/uploads/67593dd0f522f4409e614ba0/cvb9w_8seu3Kbjg_XAnNj.jpeg
Jiang Zhengkai
user
jzzzzk
677b5c2478ac1cec968405a4
Yue Hu
677b5c2478ac1cec968405a5
Peng Gao
677b5c2478ac1cec968405a6
Hongsheng Li
admin_assigned
2025-01-06T08:23:37.965Z
65c04e9c27a5fdca81abcbd9
/avatars/12a155683c824fa23da4a9e2bed4f64e.svg
Hongsheng LI
user
hsli-cuhk
677b5c2478ac1cec968405a7
Maoqing Yao
admin_assigned
2025-01-06T08:23:43.822Z
67739bfa64e8b7438ae68eb4
/avatars/15193bfbce487b2de4ce8c86bd18885a.svg
Maoqing Yao
user
AutobotZero
677b5c2478ac1cec968405a8
Guanghui Ren
claimed_verified
2025-01-06T12:37:40.659Z
646ec9b135f55eb49e405faa
/avatars/a17194be585d20e2a021e77a5a20e213.svg
Guanghui Ren
user
sundrops
EnerVerse: Envisioning Embodied Future Space for Robotics Manipulation
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.
EnerVerse enhances robotic manipulation by generating future space using convolutional and bidirectional attention, sparse memory, and generated multi-camera data.
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