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README.md

Information for Simulation Setup:

1st. Step: generate training data:

cd /python_visual_mpc/visual_mpc_core/infrastructure/utility python parallel_data_collection.py <name_of_datafolder>

Note: <name_of_datafolder> is the folder-name inside lsdc/pushing_data Each of the folders in pushing_data must have a "/train" and "/test" subdirectory. The hyperparams.py file to specifies how the data-collection is done.

2nd. Step: Train video prediction Model:

See Readme.md in python_visual_mpc/video_prediction

3rd Step: Run a benchmark on the pushing task:

cd /python_visual_mpc/visual_mpc_core

python benchmarks.py <benchmark_folder_name>

Misc

In order to run rendering remotely on a different machine it is necessary to give remote users access to the x server: Log into graphical session on remote computer and give access by typing: xhost +

Also set the DISPLAY variable export DISPLAY=:0

Setup for using Rethink Sawyer:

start kinect-bridge node:

cd visual_mpc/python_visual_mpc/sawyer/visual_mpc_rospkg/launch/bridgeonly.launch ./startkinect.sh

start PD-Controller:

rosrun visual_mpc_rospkg joint_space_impedance.py

start visual-mpc-client:

rosrun visual_mpc_rospkg visual_mpc_client.py <name_of_folder_inside:visual_mpc/experiments/cem_exp/benchmarks_sawyer>

start visual-mpc-server (can be launched on newton4 or newton1):

rosrun visual_mpc_rospkg visual-mpc-server.py <name_of_folder_inside:visual_mpc/experiments/cem_exp/benchmarks_sawyer> --ngpu <number_of_gpus>

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Visual MPC implementation running on Rethink Sawyer Robot

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