Parallel DDP for GPU/CPU

Releases

v0.1: WAFR 2018 Release holds the code for "A Performance Analysis of Differential Dynamic Programming on a GPU."

v0.2: ICRA 2019 Release extends the previous work by integrating LCM for hardware experiments and cleaning up the code base / interface. An extended abstract describing the hardware experiments can be found here.

Stucture of this Repository

• config.h defines all of the default settings (parallel level, plant, etc.) for an experiment and imports all of the various helper functions and files from the following folders as needed
• /examples/* holds the scripts that run the WAFR examples and LCM examples (see the comment at the top of each file for the compilation instructions)
• /plants/* holds custom rigid body dynamics and/or analytical dynamics and cost functions for currently supported plants
• /DDPHelpers/* holds most of the functions for DDP as inlined templated CUDA header files
• /utils/* holds a variety of support code for matrix multiplication, discrete time integrators, thread/CUDA support, etc.
• /test/* holds a variety of testing scripts for various function calls and derivatives (see the comment at the top of each file for the compilation instructions)
• /lcmtypes/* holds LCM types for multi-computer / hardware communication

Dependencies

• CUDA needs to be installed as code needs to be compiled with the NVCC comiler. Currently, this code has been tested with CUDA 9 and X.
• For multi-computer / hardware MPC code there is an additional communicaiton dependency: LCM.

Instalation Tips for CUDA

https://askubuntu.com/questions/1077061/how-do-i-install-nvidia-and-cuda-drivers-into-ubuntu https://www.tensorflow.org/install/gpu

To use with the Drake Kuka Simulator

1. Install this fork of drake: https://github.com/plancherb1/drake
2. You need to put in you .bashrc export DRAKE_PATH_ROOT=<path_to_drake> Then the scripts in the utils folder should launch the drake visualizer and simulator

Known Bugs / Ongoing Development / Roadmap

• On roadmap to develop a CPU/GPU hybrid (only the gradients on the GPU) and a fully serial CPU version without any instruction level parallelism
• GPU RBDYN for Kuka only works in Euler mode -- need to introduce loops and reduce shared memory for Midpoint and RK3 (or use a brand new GPU which has double the shared memory) -- potential to also optimize the gradient calc to require less shared memory
• CPU MPC suffers from resource contention when trajRunner and Goal are on same computer -- need to improve and provide seperate compile paths -- also CPU MPC Parallel Line Search has a subtle bug (in iLQR is identical to serial but diverges in MPC -- need to debug)
• Constraint handling / penalities need further development - would like to add full AL constraints and/or projection methods
• Final cost shift is in development and non-functional (tied to frequency and not last goal change / shift count)
• SLQ implementation is currently broken (and EE version needs a cost kernel)
• EEVel rpy derivatives are currently broken (may explore forced finite diff)
• BFGS iters may improve / stabilize the EEPos/Vel cost and should be explored
• Square root implementation of DDP should add numerical stability and should be explored
• Want to develop URDF > transforms and inertias tool for Arm
• Would be nice to add a runtime and not compile time switch for Hardware vs. Sim mode and for level of parallelism