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Pytorch implementation of diffusion models on Lie Groups for 6D grasp pose generation https://sites.google.com/view/se3dif/home

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To resolve issue #4 - added consistent use of `batch_size` and `device` variables - added sampling loop to support desired n_grasps - added `device` and `batch_size` as cli args -...

The scripts in [`scripts/sample`](scripts/sample) do not use n_grasps from the cli arguments. `n_grasps` and `n_envs` in not used. Instead it always generate number of grasps equal to batch size specified...

Hi @TheCamusean ! Thank you for sharing your awesome work!! Do you have an example of Algorithm 4 in the paper with a full robot arm? Thank you, Juan

Good morning: I'm folllowing proposed steps to generate the dataset starting from ShapeNetSem. When running grasp_difussion/scripts/create_data/simplify.sh changing the paths, an error arose: ``` 100f39dce7690f59efb94709f30ce0d2.obj manifold 100f39dce7690f59efb94709f30ce0d2.obj temp.watertight.obj 20000 simplify: /Manifold/3rd/igl/per_vertex_point_to_plane_quadrics.cpp:83:...

Hello author @TheCamusean This is really a great job, but I have some questions about the loss function. What is the difference between ProjectedSE3DenoisingLoss and SE3DenoisingLoss. The loss mentioned in...