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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
TL;DR
Robotic manipulation needs accurate future-space and action prediction despite limited calibrated multi-camera data and a persistent Sim2Real gap. EnerVerse combines chunk-wise autoregressive video diffusion, multi-view representations, and a 4DGS data flywheel, then maps the resulting 4D representations to actions. It reports state-of-the-art manipulation performance in simulation and real-world settings.
Problem
Robotic manipulation requires future action prediction, while calibrated multi-camera data are costly and the Sim2Real gap remains significant.
Method
EnerVerse combines chunk-wise autoregressive diffusion with sparse memory, multi-view video generation, a 4DGS data flywheel, and a policy head for action prediction.
Results
EnerVerse-A achieves state-of-the-art performance in simulation and real-world deployments, while ENERVERSE achieves an average LIBERO score of 84.1 with single S-RGB input.
Takeaways & Limitations
The integrated model supports long-range policy execution and manipulation using generated embodied spaces and physical action prediction.
Takeaways & Limitations
Video generation produces artifacts in highly dynamic robotics tasks, and rendered views currently use heuristically set camera poses that may not be optimal.
Abstract
from arXiv · showhide
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.
1 Introduction
EnerVerse addresses robotic manipulation by predicting embodied future spaces and translating them into actions. Its design combines chunk-wise generation, multi-view spatial reasoning, and a 4DGS data flywheel.
- EnerVerse is a generative robotics foundation model for constructing and interpreting the robotics 4D world.
- Chunk-wise autoregressive video diffusion repeatedly expands embodied future space, while sparse context memory preserves essential prior content for long-term generation.
- Multi-view diffusion uses ray-direction maps and temporal attention to fuse camera data and provide a 3D prior for single-camera deployment.
- ENERVERSE-D combines generative modeling with 4D Gaussian Splatting to establish a data flywheel that narrows the Sim2Real gap.
- ENERVERSE-A translates 4D environment representations into physical actions and attains state-of-the-art performance in simulation and real-world deployments.
- The contributions comprise long-term grounded autoregressive diffusion, a multi-view spatial prior, and geometry-consistent multiview training data.
2 ENERVERSE
EnerVerse generates future embodied spaces through chunk-wise diffusion, enriches them with multi-view geometry, and closes the data loop with 4DGS. A policy head then reuses visual features to predict physical action chunks.
- 2.1 Next Chunk Diffusion: Chunk-wise autoregressive generation feeds newly denoised frames back as clean context and stops when an End-of-Sequence frame is detected.
- 2.1 Next Chunk Diffusion: Sparse memory samples clean context frames instead of using consecutive frames, leveraging video redundancy and improving robustness to out-of-distribution shifts.
- 2.1 Next Chunk Diffusion: The multi-view generator predicts future multi-view latents conditioned on camera geometry and learns cross-view consistency to address 3D structure and occlusion.
- 2.2 4D Embodied Space Generation: ENERVERSE-D uses generated videos and camera poses for 4DGS reconstruction, then renders refined high-quality images back into the pipeline.
- 2.3 From 4D Embodied Space to Physical Action: The policy head extracts a compact visual latent at the first denoising step, caches it across action denoising, and predicts action chunks.
3 Experiments
EnerVerse is evaluated across video generation and robotic policy tasks, including simulated and real-world settings. The experiments examine generation quality, multi-view consistency, benchmark performance, sparse memory, rendered views, and 4D representations.
- Experiment design: EnerVerse is evaluated for video generation quality and robotic policy performance across multiple datasets and task settings.The study uses public robotic datasets, simulated multi-view data, and policy evaluations on LIBERO and CALVIN.
- Video generation: EnerVerse outperforms DynamicCrafter (FN) in quantitative and qualitative video-generation evaluations, including visual quality, temporal dynamics, motion continuity, and long-task ability.Both methods achieve equivalent semantic accuracy, while EnerVerse supports long-range manipulation scenarios that DynamicCrafter does not.
- Multi-view generation: EnerVerse generates consistent multi-view videos in both simulator and real-world settings while maintaining high-quality images across views.The qualitative results cover LIBERO and real-world manipulation data.
- Robotic policy evaluation: 84.1 average score with single S-RGB input establishes state-of-the-art performance across the LIBERO benchmark and surpasses the reported baselines.LIBERO contains four task suites, each evaluated with separately fine-tuned policies and repeated rollouts.
- Further studies: Removing sparse memory reduces LIBERO-Long performance to 30.8 compared with 73 when the mechanism is present.The evaluation uses a single visual input on the longer-horizon LIBERO-Long suite.
- Further studies: RGB with two rendered views provides greater gains by mitigating occlusions, while the 4D policy reaches 92.1 versus 79.0 for a single-view video-policy baseline.The rendered-view comparison and the 4D comparison assess complementary contributions to policy performance.
4 Related Works
Related work spans diffusion-based video generation, video pretraining for robotics, and dynamic 4D scene generation. These directions provide foundations for EnerVerse’s embodied video and spatial modeling.
- Video Generation Models: Diffusion-based video generation has progressed from text-to-image priors with temporal modules toward video diffusion transformers and multi-camera generation.DynamicCrafter adapts motion priors from text-to-video diffusion to image-to-video generation.
- Video Pretraining for Robotics: Robotics video-pretraining methods use internet or non-robot videos, inverse dynamics, and video-generation objectives to improve manipulation policies.The cited methods include GR-2, LAPA, SEER, and AVID.
- 4D Generation: 4D-generation research reconstructs dynamic scenes using Gaussian splatting, neural radiance fields, 4D Gaussians, and multi-view video diffusion.These approaches model geometry and motion across time, including dynamic objects and spatio-temporal volumes.
5 Conclusions
EnerVerse models embodied future spaces for multi-view generation and long-range policy execution. Its EnerVerse-D pipeline and EnerVerse-A policy head support manipulation in simulation and real-world settings, while video artifacts and camera-pose choices remain limitations.
- EnerVerse models embodied future spaces for multi-view video generation and long-range policy execution.
- Sparse contextual memory and chunkwise autoregressive generation enhance spatial reasoning and task adaptability.
- The EnerVerse-D pipeline combines generative modeling with 4DGS to bridge the sim-to-real gap and reduce reliance on real-world data.
- EnerVerse-A achieves state-of-the-art manipulation performance in both simulator and real-world settings.
- Video artifacts, limited attention analysis, and heuristically selected rendered camera poses remain open limitations.
A Real-World Robotic Experiments
Real-world experiments evaluate EnerVerse-A on precise block placement, sorting, and related manipulation tasks. The system performs strongly across most target positions and outperforms OpenVLA on grasp and place, although instruction following is stronger for OpenVLA.
- The block-placement task requires natural-language compartment selection, precise insertion, and stable grasping of heavy blocks.
- Four metrics evaluate grasp, place, instruction following, and overall success for the robotic system.
- Five trials per compartment are averaged, with EnerVerse-A showing strong performance in most target positions.
- EnerVerse-A outperforms OpenVLA in grasp and place subtasks, while OpenVLA performs better on instruction following.
- Additional experiments cover transparent-object sorting and fruit sorting.
B Further Discussions on the Tasks Types and Video Quality in the Real-World Settings.
EnerVerse is tested beyond simple pick-and-place, while its generated videos serve primarily as 4D spatiotemporal priors rather than final visual outputs. Artifacts remain substantial in LIBERO-Object, where direct action fine-tuning degrades policy performance.
- EnerVerse models tasks involving deformable objects and kinematic constraints, including cloth folding, articulation, button turning, and drawer opening.
- Generated-video artifacts have minimal impact on robot execution because the videos provide a 4D spatiotemporal prior refined during fine-tuning.
- LIBERO-Object pretrained generation exhibits severe artifacts and scene collapse after several frames, attributed to domain differences from pretraining data.
- Direct fine-tuning on LIBERO-Object actions without adapting video generation substantially degrades final policy performance.
D Attention Map Analysis
Attention analysis examines how the policy head aligns predicted action steps with sparse memory and generated future space. Different layers and heads emphasize future predictions or memory at different stages.
- Early attention can concentrate on generated future space, indicating use of sparse-memory conditions and future predictions from the outset.
- Other layers sharply focus on sparse memory, revealing hierarchical variation across heads and layers.
- Attention maps use eight predicted action steps as queries and sparse memory plus future-space tokens as key-value inputs.
E Robustness against OOD Samples
EnerVerse is evaluated on texture-shifted scenes, unseen containers, and mixed LIBERO splits to assess out-of-distribution robustness. It also reports inference resource requirements for single- and multi-view configurations.
- OOD evaluation: Texture changes and unseen-container experiments required no retraining on LIBERO-Object.
- OOD evaluation: 87.63 Avg with train-all, test-all exceeded 84.1 Avg from single-split training.The authors attribute the improvement to shared textures and spatial layouts across the larger mixed dataset.
- Training cost: Training required 8 A100 GPUs for approximately 20 hours of video-generation adaptation and 12 additional hours of action learning.
- Inference efficiency: 12 GB of GPU memory supported single-view video generation, compared with 13.5 GB for three views.Generating one video chunk took around 20 seconds per view.
- Inference efficiency: 300 ms per action chunk was required for single-S-RGB action inference with eight-frame chunks.Single-S-RGB used 10.6 GB GPU memory, while three-view inference used 12 GB.
G Further Details on the Model Architecture
The architecture extends DynamiCrafter into a multi-view 4D latent pipeline and connects denoised visual features to an action head. Attention reshaping separates spatial, temporal, and decoding operations across views and time.
- Latent architecture: The model processes BCVTHW latents with view- and time-aware reshaping for spatial and temporal attention.B denotes batch size, C channels, V views, and T timestamps.
- Attention operations: Spatial attention uses the (BT) (V HW) C arrangement, while temporal attention uses (BV HW) TC.
- Decoding and action head: The pipeline reshapes features to (BV T) CHW before decoding, with image decoding performed per view and frame.
- Decoding and action head: The action head contains 18 DiT blocks and a linear layer that predicts delta-pose actions.Actions are represented in a seven-dimensional pose-space vector, including positional and rotational components.
H More Details on Training Data
Training combines heterogeneous embodied datasets with a newly constructed Isaac Sim multi-view corpus because existing public data largely lacks calibrated multi-view observations. The simulator corpus addresses limited benchmark scale and supports multi-view learning.
- Existing datasets: Pretraining uses RT-1, Language Table, Bridge, RoboTurk, ManiSkill, and Isaac Sim datasets with clear task logic.
- Dataset composition: The source datasets span real-robot and simulation data, including 3.7M RT-1 frames, 7.0M Language Table frames, and 4.0M ManiSkill frames.Isaac Simulator contributes 3.0M frames with egocentric and eight third-person views.
- Data limitations: Public datasets predominantly provide single third-person or egocentric views, while LIBERO contains fewer than 500 trajectories.These properties are described as insufficient for robust multi-view generation training.
- Simulator corpus: The Isaac Sim corpus contains about 40K episodes across 8 tasks spanning industrial and home scenarios.It was constructed with ground-truth camera parameters for multi-view learning.
- Task coverage: Simulator examples include tasks such as placing trash into a dustbin, picking fruit into a basket, and picking a toy into a box.
I Visual Samples for our Data Engine
The data engine is illustrated through generated visual samples and a self-occlusion experiment comparing pipelines without and with 4DGS. The 4DGS refinement setting produces fewer hallucinations and clearer visual boundaries.
- Visual quality: ENERVERSE-D samples show fewer artifacts and clearer boundaries when used as the data engine.
- Evaluation setting: The arrange-workpieces evaluation uses gears and boxes on a tabletop with frequent self-occlusions and starts from one complete head-camera video.
- 4DGS comparison: Without 4DGS, ENERVERSE-D directly generates the target-view video; with 4DGS, rendered views are refined through re-noising and feedback.