From simulation to real world using deep generative models
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data-collection
gaussian-process
normalization
old_code
pusher
pusher3dof
reacher
reading
real-robot
results
scripts
simple_joints_lstm
swordfight
test
trained_models
utils
.gitignore
08-train-lstm-simplus.py
11-train-lstm-withsim.py
README.md
__init__.py
compare_trajectories_pusher.py
fig-results-real.png
lstm_train_v4_ergoreachersimple.py
overfit_one_episode.py
pusher_real_only.py
reacher_lstm_train.py
real_lstm_train_v4.py
real_lstm_train_v4_nosim.py
real_lstm_train_v4_slow.py
real_lstm_train_v5_directstate.py
real_lstm_train_v5_directstate_slow.py
striker_lstm_old.py
striker_real_only.py
thrower_lstm.py
thrower_real_only.py
train_mlp_pusher.py
train_simple_joints_lstm_fl_real3_bullet.py
train_simple_joints_lstm_fl_real3_bullet_compare.py
train_simple_joints_lstm_fl_real3_bullet_nosim.py
train_simple_joints_lstm_fl_real3_bullet_slow.py
zoneout.py

README.md

Sim-to-Real

From simulation to real world using deep generative models

For pix2pix:

To do: _ add skip connections for a U-Net like architecture _ correct initialization _ try LSGAN, WGAN+GP