README.md

Denavit–Hartenberg (DH) parameters for behavior cloning

Denavit-Hartenberg (DH) parameters are a set of four parameters which allow us to attach reference frames to links in a kinematic chain. We utilize this differentiable formulation for improving transfer in behavior cloning between robots with the same or different kinematic configurations.

train a new RL model:

python train_rl.py --cfg experiments/base_dm.cfg

evaluate and plot the latest model from a training directory:

python train_rl.py --eval --frames --load_model results/base_easy_01111_reacher_1_00/ --frames

This will result in plots for reward & target position, and a video of the episodes written as a derivitive of the most recent .pt file in the --load_model directory.

evaluate and plot a specific model:

python train_rl.py --eval --frames --load_model results/base_easy_01111_reacher_1_00/base_0000355000.pt

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train a behavior cloning model which predicts relative JOINT_POSITIONS for a given state

with DH on the end effector or target link:

python train_bc.py --load_replay <path-to-rl-eval-replay-buffer>.pkl

on the joint angles:

python train_bc.py --load_replay <path-to-rl-eval-replay-buffer>.pkl --loss "angle"

now plot and evaluate the behavior cloning

NOTE: This is not really relevant with dm_control - but used with robosuite and JOINT_POSITION controller

python train_bc.py --load_model <path-to-saved-bc-model> --eval --frames

Adding Robot Configurations to dm_control

We utilize some non-standard dm_control robots for experiments. To work with these, copy the contents of the "robots" directory to your dm_control installation:

cd DH
cp robots/dm_control/*.* ../dm_control/dm_control/suite/ 
cd ../dm_control 
pip install .

Then you can experiment with these models as you would other robots.

For instance, to train an rl reacher agent with links which are double the size of the standard reacher:

cd DH
python train_rl.py --cfg experiments/reacher_double.cfg