Training deform contactnet models

Pointnet++:

python train2.py --model deform_contactnet_pointnet2 --use_normals --log_dir deform_contactnet_pointnet2 --batch_size=8 --epoch=10 --use_wandb --gpu=1

Pointnet:

python train.py --model deform_contactnet_pointnet --use_normals --log_dir deform_contactnet_pointnet --batch_size=9 --use_wandb --epoch=10 --gpu=0

Running sequence planning

python planning.py --object=bowl_ycb --use_vis

Sponge Sim

The script provides a simple example of how to import the Sponge assets into NVIDIA Isaac Gym, launch an FEM simulation with multiple objects across multiple parallel environments, and extract useful features (net forces, nodal coordinates, and element-wise stresses).

Installation:

• Clone repo
• Download NVIDIA Isaac Gym
  • Follow provided instructions to create and activate rlgpu Conda environment for Isaac Gym
• Install h5py package via Conda

Usage:

• Execute sim_sponge.py --object="target_object" --num_envs=1 --youngs=1000
  • See code for available command line switches
• View results/object/object_youngs
  • File structure is action_success / contact_indexes / gripper_ori / normal_forces_on_nodes / press_locations / press_forces /sponge_position_at_force

FAQ:

• Error: cannot open shared object file
  • Add /home/username/anaconda3/envs/rlgpu/lib to LD_LIBRARY_PATH
• Warning: Degenerate or inverted tet
  • Safely ignore

Additional:

• For questions related to NVIDIA Isaac Gym, see official forum