ManiGaussian: Dynamic Gaussian Splatting for Multi-task Robotic
  Manipulation
Guanxing Lu Shiyi Zhang Ziwei Wang Changliu Liu Jiwen Lu Yansong Tang
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.