STORM

Stochastic Tensor Optimization for Robot Motion - A GPU Robot Motion Toolkit

This package contains code for reactive robot motion leveraging parallel compute on the GPU.

_images/coll_demo.gif

The proposed control framework leverages MPPI (“control” block in below image) to optimize over sampled actions and their costs. The costs are computed by rolling out the forward model from the current state with the sampled actions.

_images/mpc_approach.png

Install Instructions

System Dependencies:

  • Conda version >= 4.9

  • NVIDIA driver >= 460.32

  • Cuda toolkit >= 11.0

Steps:

  1. Create a new conda environment with: conda env create -f environment.yml

  2. Install python bindings for isaacgym: https://developer.nvidia.com/isaac-gym

  3. run the following command from this directory: pip install -e .

Running Example

  1. run scripts/train_self_collision.py to get weights for robot self collision checking.

  2. Run python franka_reacher.py, which will launch isaac gym with a franka robot trying to reach a red mug. In the isaac gym gui, search for “ee_target” and toggle “Edit DOF”, now you can move the target pose by using the sliders.

Indices and tables