代码: ManiGaussian | Github Demo 0 A demo of ManiGaussian. 简介 我们的目标是构建通用机器人(Fly me to the moon),而多任务机械臂操纵的能力是不可或缺的。我们可以粗略地把现有的多任务机械臂操纵方法分为感知式[1]和生成式[2]。感知式方法[1]直接将机器人接收到的三维表征如多
我们设计了一个动态高斯点扩散框架,用于模拟高斯嵌入空间中多样化语义特征的传播,以及利用场景动态的语义特征来预测最佳的机器人动作。随后,我们构建了一个高斯世界模型,用于参数化动态高斯点扩散框架中的分布,通过根据当前场景和机器人动作重建未来场景来挖掘场景级动态。在各种操纵任务的广泛实验中,我们的 ManiGaussian 相...
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...
To train our ManiGaussian without semantic features and deformation predictor (the fastest version), run: bash scripts/train_and_eval_w_geo.sh ManiGaussian_BC 0,1 12345 ${exp_name} where the exp_name can be specified as you like. You can also train other baselines such as GNFACTOR_BC ...
Files main ManiGaussian_results/w_geo agents conf docs helpers scripts third_party voxel .gitignore LICENSE README.md eval.py requirements.txt run_seed_fn.py train.pyBreadcrumbs ManiGaussian / ManiGaussian_results/ Directory actions More options...
GAUSSIAN processesGAUSSIAN integersSUPPORT vector machinesDIGITAL image processingWAVELET transformsThe objective of this work is to leverage the clues obtained from mani-fold sources, to figure out the density of people existent in exceptionally dense crowded regions. The complications in crowd density ...
Gaussian Markov Random FieldSupport vector regressionThe objective of this work is to leverage the clues obtained from mani-fold sources, to figure out the density of people existent in exceptionally dense crowded regions. The complications in crowd density estimation include perspective effect, ...
大量实验表明,StyleGaussian 实现了即时 3D 风格化,具有卓越的风格化质量,同时保持实时渲染和严格的多视图一致性。 3DGS机器人与语义理解 ManiGaussian: Dynamic Gaussian Splatting for Multi-task Robotic Manipulation https://arxiv.org/abs/2403.08321 Guanxing Lu, Shiyi Zhang, Ziwei Wang, Changliu Liu, ...
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