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ManiGaussian: Dynamic Gaussian Splatting for Multi-task Robotic Manipulation

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arxiv 2403.08321 v2 pith:VE5DOTBK submitted 2024-03-13 cs.RO cs.CV

Guanxing Lu , Shiyi Zhang , Ziwei Wang , Changliu Liu , Jiwen Lu , Yansong Tang This is my paper
classification cs.ROcs.CV
keywords gaussiandynamicmanigaussianmanipulationroboticsplattingframeworkscene
verification ladder T0 review T1 audit T2 compute T3 formal
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Performing language-conditioned robotic manipulation tasks in unstructured environments is highly demanded for general intelligent robots. Conventional robotic manipulation methods usually learn semantic representation of the observation for action prediction, which ignores the scene-level spatiotemporal dynamics for human goal completion. In this paper, we propose a dynamic Gaussian Splatting method named ManiGaussian for multi-task robotic manipulation, which mines scene dynamics via future scene reconstruction. Specifically, we first formulate the dynamic Gaussian Splatting framework that infers the semantics propagation in the Gaussian embedding space, where the semantic representation is leveraged to predict the optimal robot action. Then, we build a Gaussian world model to parameterize the distribution in our dynamic Gaussian Splatting framework, which provides informative supervision in the interactive environment via future scene reconstruction. We evaluate our ManiGaussian on 10 RLBench tasks with 166 variations, and the results demonstrate our framework can outperform the state-of-the-art methods by 13.1\% in average success rate. Project page: https://guanxinglu.github.io/ManiGaussian/.

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Cited by 6 Pith papers

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  1. EvoScene-VLA: Evolving Scene Beliefs Inside the Action Decoder for Chunked Robot Control

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    EvoScene-VLA maintains an action-updated scene prior across control chunks in VLA policies, raising success rates on RoboTwin tasks from 87.2% to 89.1% fixed and 86.1% to 88.5% randomized while outperforming baselines...

  2. Structured 4D Latent Predictive Model for Robot Planning

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    A 4D latent predictive model encodes scenes holistically to generate 3D-consistent futures that an inverse dynamics module converts into robot actions, outperforming video-based planners on manipulation tasks.

  3. PhysEdit: Physically-Consistent Region-Aware Image Editing via Adaptive Spatio-Temporal Reasoning

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  4. FlowRAM: Grounding Flow Matching Policy with Region-Aware Mamba Framework for Robotic Manipulation

    cs.RO 2025-06 conditional novelty 6.0 of 10

    FlowRAM pairs a shrinking 3D attention region with flow-matching action generation and a Mamba fusion model, setting new RLBench state-of-the-art results.

  5. GAF: Gaussian Action Field as a 4D Representation for Dynamic World Modeling in Robotic Manipulation

    cs.RO 2025-06 unverdicted novelty 6.0 of 10

    GAF creates 4D dynamic scene models by adding motion to 3D Gaussians, enabling better reconstruction and 7.3% higher success in robotic tasks.

  6. A Survey on 3D Gaussian Splatting

    cs.CV 2024-01 unverdicted novelty 2.0 of 10

    A survey compiling principles, applications, benchmarks, and challenges of 3D Gaussian Splatting for explicit 3D scene representation.

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