ManiGaussian: Dynamic Gaussian Splatting for Multi-task Robotic Manipulation
- URL: http://arxiv.org/abs/2403.08321v2
- Date: Thu, 18 Jul 2024 04:45:47 GMT
- Title: ManiGaussian: Dynamic Gaussian Splatting for Multi-task Robotic Manipulation
- Authors: Guanxing Lu, Shiyi Zhang, Ziwei Wang, Changliu Liu, Jiwen Lu, Yansong Tang,
- Abstract summary: Conventional robotic manipulation methods usually learn semantic representation of the observation for prediction.
We propose a dynamic Gaussian Splatting method named ManiGaussian for multi-temporal robotic manipulation.
Our framework can outperform the state-of-the-art methods by 13.1% in average success rate.
- Score: 58.615616224739654
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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/.
Related papers
- DeSiRe-GS: 4D Street Gaussians for Static-Dynamic Decomposition and Surface Reconstruction for Urban Driving Scenes [71.61083731844282]
We present DeSiRe-GS, a self-supervised gaussian splatting representation.
It enables effective static-dynamic decomposition and high-fidelity surface reconstruction in complex driving scenarios.
arXiv Detail & Related papers (2024-11-18T05:49:16Z) - GaussianPrediction: Dynamic 3D Gaussian Prediction for Motion Extrapolation and Free View Synthesis [71.24791230358065]
We introduce a novel framework that empowers 3D Gaussian representations with dynamic scene modeling and future scenario synthesis.
GaussianPrediction can forecast future states from any viewpoint, using video observations of dynamic scenes.
Our framework shows outstanding performance on both synthetic and real-world datasets, demonstrating its efficacy in predicting and rendering future environments.
arXiv Detail & Related papers (2024-05-30T06:47:55Z) - Active Exploration for Robotic Manipulation [40.39182660794481]
This paper proposes a model-based active exploration approach that enables efficient learning in sparse-reward robotic manipulation tasks.
We evaluate our proposed algorithm in simulation and on a real robot, trained from scratch with our method.
arXiv Detail & Related papers (2022-10-23T18:07:51Z) - ProcTHOR: Large-Scale Embodied AI Using Procedural Generation [55.485985317538194]
ProcTHOR is a framework for procedural generation of Embodied AI environments.
We demonstrate state-of-the-art results across 6 embodied AI benchmarks for navigation, rearrangement, and arm manipulation.
arXiv Detail & Related papers (2022-06-14T17:09:35Z) - Factored World Models for Zero-Shot Generalization in Robotic
Manipulation [7.258229016768018]
We learn to generalize over robotic pick-and-place tasks using object-factored world models.
We use a residual stack of graph neural networks that receive action information at multiple levels in both their node and edge neural networks.
We show that an ensemble of our models can be used to plan for tasks involving up to 12 pick and place actions using search.
arXiv Detail & Related papers (2022-02-10T21:26:11Z) - Learning Human Motion Prediction via Stochastic Differential Equations [19.30774202476477]
We propose a novel approach in modeling the motion prediction problem based on differential equations and path integrals.
It achieves a 12.48% accuracy improvement over current state-of-the-art methods in average.
arXiv Detail & Related papers (2021-12-21T11:55:13Z) - Nonprehensile Riemannian Motion Predictive Control [57.295751294224765]
We introduce a novel Real-to-Sim reward analysis technique to reliably imagine and predict the outcome of taking possible actions for a real robotic platform.
We produce a closed-loop controller to reactively push objects in a continuous action space.
We observe that RMPC is robust in cluttered as well as occluded environments and outperforms the baselines.
arXiv Detail & Related papers (2021-11-15T18:50:04Z) - Few-Shot Visual Grounding for Natural Human-Robot Interaction [0.0]
We propose a software architecture that segments a target object from a crowded scene, indicated verbally by a human user.
At the core of our system, we employ a multi-modal deep neural network for visual grounding.
We evaluate the performance of the proposed model on real RGB-D data collected from public scene datasets.
arXiv Detail & Related papers (2021-03-17T15:24:02Z) - Deep Imitation Learning for Bimanual Robotic Manipulation [70.56142804957187]
We present a deep imitation learning framework for robotic bimanual manipulation.
A core challenge is to generalize the manipulation skills to objects in different locations.
We propose to (i) decompose the multi-modal dynamics into elemental movement primitives, (ii) parameterize each primitive using a recurrent graph neural network to capture interactions, and (iii) integrate a high-level planner that composes primitives sequentially and a low-level controller to combine primitive dynamics and inverse kinematics control.
arXiv Detail & Related papers (2020-10-11T01:40:03Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.