cheneating716 / diverse-and-stable-grasp

This repository shares the code to replicate result from the paper Synthesizing Diverse and Physically Stable Grasps with Arbitrary HandStructures using Differentiable Force Closure Estimation

diverse-and-stable-grasp

This repository shares the code to replicate results from the paper Synthesizing Diverse and Physically Stable Grasps with Arbitrary Hand Structures using Differentiable Force Closure Estimation [arxiv] [project]

We tested our code with Python 3.8, PyTorch 1.9 and CUDA 11.1. However, the code should work with any recent version of PyTorch.

Dependencies

• Numpy
• Trimesh
• Plotly
• PyTorch
• manopth

Download data

• Signup and download the license-protected hand model file MANO_RIGHT.pkl from [http://mano.is.tue.mpg.de] and place it in data/mano/.
• Download DeepSDF model weights and other related files from Google Drive and extract into data/

Run

Run python synthesis.py to run our grasp synthesis algorithm with 1024 parallel syntheses, a MANO hand, and spheres with random radius. Synthesized examples that satisfy the constraints in Eq. 11 are stored in synthesis/. The demo code synthesis.py supports the following arguments:

• --batch_size: number of parallel syntheses. Default: 1024
• --n_contact: number of contact points. Default: 5
• --max_physics: number of optimization steps. Default: 10000
• --max_refine: number of refinement steps. Set to 0 to turn off refinement. Default: 1000
• --hand_model: choice of ['mano', 'mano_fingertip']. Default: 'mano'
• --obj_model: choice of ['bottle', 'sphere']. Default: 'bottle'
• --langevin_probability: chance of choosing Langevin dynamics over contact point sampling in optimization steps. Default: 0.85
• --hprior_weight: weight of $E_\mathrm{prior}$. Default: 1
• --noise_size: size of noise used in Langevin dynamics. Default: 0.1
• --mano_path: path to MANO parameters. Default: 'data/mano'
• --output_dir: path to store synthesis results. Default: 'synthesis'