This repository provides the official implementation of the RialTo system, as proposed in Reconciling Reality through Simulation: A Real-to-Sim-to-Real approach for Robust Manipulation The manuscript is available on arXiv. See the project page
If you use this codebase, please cite
@article{torne2024reconciling,
title={Reconciling Reality through Simulation: A Real-to-Sim-to-Real Approach for Robust Manipulation},
author={Torne, Marcel and Simeonov, Anthony and Li, Zechu and Chan, April and Chen, Tao and Gupta, Abhishek and Agrawal, Pulkit},
journal={arXiv preprint arXiv:2403.03949},
year={2024}
}
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Download omniverse: https://www.nvidia.com/en-us/omniverse/
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Install isaac-sim 2022.2.1 (from omniverse launcher)
Launch isaac-sim to complete the installation
git clone [email protected]:NVIDIA-Omniverse/orbit.git
git checkout f2d97bdcddb3005d17d0ebd1546c7064bc7ae8bc
export ISAACSIM_PATH=<path to isaacsim>
# enter the cloned repository
cd orbit
# create a symbolic link
ln -s ${ISAACSIM_PATH} _isaac_sim
Create conda environment from orbit
./orbit.sh --conda isaac-sim
conda activate isaac-sim
orbit -i
orbit -e
Make sure orbit was correctly set up:
python -c "import omni.isaac.orbit; print('Orbit configuration is now complete.')"
- Clone repo
git clone [email protected]:real-to-sim-to-real/RialToPolicyLearning.git
git checkout dev-massive-huge
cd PolicyLearning
pip install -r requirements.txt
pip install torch-scatter -f https://data.pyg.org/whl/torch-1.13.0+cu117.html --no-index
pip install torch-cluster -f https://data.pyg.org/whl/torch-1.13.0+cu117.html --no-index
conda install conda-build
conda develop dependencies
You should be ready to go :) Test it our running the teleoperation script below
- Create your environment using the RialTo GUI
- booknshelf
python collect_demos_teleop_franka.py --env_name=isaac-env --img_width=1024 --img_height=1024 --demo_folder=booknshelvenew --max_path_length=150 --extra_params=booknshelve,booknshelve,booknshelve_debug_mid_randomness --usd_path=/home/marcel/USDAssets/scenes --offset=1
- mugandshelf
python collect_demos_teleop_franka.py --env_name=isaac-env --img_width=1024 --img_height=1024 --demo_folder=mugandshelfnew2 --max_path_length=150 --extra_params=mugandshelf,mugandshelf_mid_rot_randomness --usd_path=/home/marcel/USDAssets/scenes
- cabinet
python collect_demos_teleop_franka.py --env_name=isaac-env --img_width=1024 --img_height=1024 --demo_folder=cabinet --max_path_length=150 --extra_params=cabinet,cabinet_mid_randomness --usd_path=/home/marcel/USDAssets/scenes
- drawer
python collect_demos_teleop_franka.py --env_name=isaac-env --img_width=1024 --img_height=1024 --demo_folder=drawertras --max_path_length=150 --extra_params=wooden_drawer_bigger,drawer_debug_high_randomness --usd_path=/home/marcel/USDAssets/scenes
- dishinrack
python collect_demos_teleop_franka.py --env_name=isaac-env --img_width=1024 --img_height=1024 --demo_folder=dishinrackv3 --max_path_length=200 --extra_params=dishnrackv2,dishnrack_high_randomness,no_action_rand
- kitchentoaster
python collect_demos_teleop_franka.py --env_name=isaac-env --img_width=1024 --img_height=1024 --demo_folder=kitchentoaster --max_path_length=150 --extra_params=kitchentoaster,kitchentoaster_randomness,no_action_rand
- cabinet
python launch_ppo.py --num_envs=4096 --n_steps=90 --max_path_length=90 --usd_path=/home/marcel/USDAssets/scenes --bc_loss --bc_coef=0.1 --filename=isaac-envcabinet --extra_params=cabinet,cabinet_mid_rot_randomness,no_action_rand --datafolder=demos --num_demos=12 --ppo_batch_size=31257 --run_path=locobot-learn/cabinet.usdppo-finetune/rw4wp46f --model_name=model_policy_567 --from_ppo
- mugandshelf
python launch_ppo.py --num_envs=4096 --n_steps=150 --max_path_length=150 --bc_loss --bc_coef=0.1 --filename=isaac-envmugandshelf --extra_params=mugandshelf,mugandshelf_mid_rot_randomness,no_action_rand --datafolder=demos --num_demos=6 --ppo_batch_size=31257 --model_name=model_policy_279 --run_path=locobot-learn/mugandshelf.usdppo-finetune/ef5nbwyb --from_ppo
- booknshelve
python launch_ppo.py --num_envs=4096 --n_steps=130 --max_path_length=130 --extra_params=booknshelve,booknshelve,booknshelve_debug_high_rot_randomness,no_action_rand --bc_loss --bc_coef=0.1 --filename=booknshelvesreal2sim --datafolder=/data/pulkitag/data/marcel/data --num_demos=14 --ppo_batch_size=31257 --num_envs=2048
- kitchen toaster
python launch_ppo.py --num_envs=4096 --n_steps=130 --max_path_length=130 --extra_params=kitchentoaster,kitchentoaster_randomness,no_action_rand --bc_loss --bc_coef=0.1 --filename=isaac-envkitchentoaster --datafolder=demos --num_demos=12 --ppo_batch_size=31257 --num_envs=2048 --usd_path=/home/marcel/USDAssets/scenes
- dishsinklab
python launch_ppo.py --num_envs=4096 --n_steps=110 --max_path_length=110 --extra_params=dishsinklab,dishsinklab_low_randomness,no_action_rand --bc_loss --bc_coef=0.1 --filename=isaac-envdishsinklablow --datafolder=demos --num_demos=15 --ppo_batch_size=31257 --num_envs=2048 --usd_path=/home/marcel/USDAssets/scenes
python distillation.py --extra_params=booknshelve,booknshelve,booknshelve_debug_high_rot_randomness,no_action_rand --max_path_length=130 --filename=trash --run_path=locobot-learn/booknshelve.usdppo-finetune/ruk0ihdc --model_name=policy_finetune_step_210 --datafolder=/data/pulkitag/data/marcel/data/ --visualize_traj --max_demos=10 --sensors=rgb,pointcloud
- booknshelve
python distillation.py --extra_params=booknshelve,booknshelve,booknshelve_debug_high_rot_randomness,no_action_rand --max_path_length=130 --filename=booknshelvenewsynthetic --run_path=locobot-learn/booknshelve.usdppo-finetune/ruk0ihdc --model_name=policy_finetune_step_210 --datafolder=/data/pulkitag/data/marcel/data/ --use_synthetic_pcd --max_demos=15000 --eval_freq=1 --use_state --visualize_traj
- mugandshelf
python distillation.py --max_path_length=150 --extra_params=mugandshelf,mugandshelf_mid_rot_randomness,no_action_rand --filename=mugandshelfsynthetic --run_path=locobot-learn/mugandshelf.usdppo-finetune/ke2wi6xb --model_name=policy_finetune_step_369 --datafolder=/data/pulkitag/data/marcel/data/ --use_synthetic_pcd --max_demos=15000 --eval_freq=1 --use_state --visualize_traj
- cabinet
python distillation.py --max_path_length=90 --extra_params=cabinet,cabinet_mid_rot_randomness,no_action_rand --filename=cabinetsynthetic --run_path=locobot-learn/cabinet.usdppo-finetune/uqn3jbmg --model_name=policy_finetune_step_393 --datafolder=/data/pulkitag/data/marcel/data/ --use_synthetic_pcd --max_demos=15000 --eval_freq=1 --use_state --visualize_traj
- mugupright
python distillation.py --max_path_length=100 --extra_params=mugupright,mugupright_mid_randomness,no_action_rand --filename=muguprightsynthetic --run_path=locobot-learn/mugupright.usdppo-finetune/5vocshfn --model_name=model_policy_830 --from_ppo --datafolder=/data/pulkitag/data/marcel/data/ --use_synthetic_pcd --max_demos=15000 --eval_freq=1 --use_state --visualize_traj --usd_path=/home/marcel/USDAssets/scenes
- cabinet
python distillation.py --max_path_length=90 --extra_params=cabinet,cabinet_mid_rot_randomness,no_action_rand --filename=cabinetsimpcd --run_path=locobot-learn/cabinet.usdppo-finetune/uqn3jbmg --model_name=policy_finetune_step_393 --datafolder=/data/pulkitag/data/marcel/data/ --max_demos=2500 --eval_freq=1 --use_state --visualize_traj --policy_batch_size=32 --num_envs=9 --run_path_student=locobot-learn/distillation_cabinet/47r0waz0 --model_name_student=policy_distill_step_71
- mugupright
python distillation.py --max_path_length=100 --extra_params=mugupright,mugupright_mid_randomness,no_action_rand --filename=muguprightsimpcd --run_path=locobot-learn/mugupright.usdppo-finetune/5vocshfn --model_name=model_policy_830 --from_ppo --datafolder=/home/marcel/data/ --max_demos=2500 --eval_freq=1 --use_state --visualize_traj --num_envs=12 --usd_path=/home/marcel/USDAssets/scenes --run_path_student=locobot-learn/distillation_mugupright/tjlds4ds --model_name_student=policy_distill_step_407
- mugandshelf
python distillation.py --max_path_length=150 --extra_params=mugandshelf,mugandshelf_mid_rot_randomness,no_action_rand --filename=mugandshelfsimpcd --run_path=locobot-learn/mugandshelf.usdppo-finetune/ke2wi6xb --model_name=policy_finetune_step_369 --datafolder=/data/pulkitag/data/marcel/data/ --max_demos=2500 --eval_freq=1 --use_state --num_envs=12 --visualize_traj --model_name_student=policy_distill_step_381 --run_path_student=locobot-learn/distillation_mugandshelf/valc8x68
- booknshelve
python distillation.py --extra_params=booknshelve,booknshelve,booknshelve_debug_high_rot_randomness,no_action_rand --max_path_length=130 --filename=booknshelvenewsimpcd --run_path=locobot-learn/booknshelve.usdppo-finetune/ruk0ihdc --model_name=policy_finetune_step_210 --datafolder=/data/pulkitag/data/marcel/data/ --num_envs=9 --max_demos=2500 --eval_freq=1 --use_state --visualize_traj
python distillation.py --max_path_length=80 --extra_params=wooden_drawer_bigger,drawer_debug_extra_randomness --filename=drawerdistractorsfixedv2 --run_path=locobot-learn/drawerbiggerhandle.usdppo-finetune/cpyibf8w --model_name=model_policy_16 --from_ppo --datafolder=/data/pulkitag/data/marcel/data/ --eval_freq=1 --use_state --visualize_traj --policy_batch_size=32 --max_demos=5000 --num_envs=9 --policy_train_steps=1 --distractors=distractors_fixed --seed=2
python distillation.py --max_path_length=65 --extra_params=wooden_drawer_bigger,drawer_debug_high_randomness,no_action_rand --filename=drawerfromrealdistractors,drawerbiggerreal --run_path=locobot-learn/drawerbiggerhandle.usdppo-finetune/l3zbpqta --model_name=model_policy_196 --from_ppo --datafolder=/data/scratch-oc40/pulkitag/marcel --eval_freq=1 --use_state --visualize_traj --policy_batch_size=32 --policy_train_steps=10000 --eval_freq=3 --max_demos=500 --num_envs=5 --run_path_student=locobot-learn/distillation_drawer_bigger/7vl0dkb3 --model_name_student=policy_distill_step_17 --dagger --sampling_expert=0 --policy_batch_size=8
We use Polymetis. You would need to install polymetis on the robot side. We give the instructions on how to install and create the environments on the GPU side.
conda create -n franka-env-new-cuda python=3.8.15
conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.7 -c pytorch -c nvidia
conda install -c pytorch -c fair-robotics -c aihabitat -c conda-forge polymetis
- Clone and install airobot
- Clone and install improbable_rdt
- git clone --recurse [email protected]:anthonysimeonov/improbable_rdt.git
- git checkout lightweight-marcel
pip install torch-scatter -f https://data.pyg.org/whl/torch-1.13.0+cu117.html --no-index
pip install torch-cluster -f https://data.pyg.org/whl/torch-1.13.0+cu117.html --no-index
pip install -r franka_requirements.txt
conda activate franka-env-new-cuda
export RDT_SOURCE_DIR=~/improbable_rdt/src/rdt
export PB_PLANNING_SOURCE_DIR=~/improbable_rdt/pybullet-planning/
- dishinrack
python evaluate_policy_real.py --run_path=locobot-learn/distillation_dishnrack/zmglvp19 --model_name=policy_distill_step_67 --max_path_length=130 --cam_index=2 --extra_params=booknshelve,booknshelve,booknshelve_debug_high_rot_randomness,no_action_rand --use_state --hz=2 --gripper_force=100 --background_loop --total_loop_time=0.3 --interp_steps=30 --start_interp_offset=3
conda activate franka-env-new-cuda
export RDT_SOURCE_DIR=~/improbable_rdt/src/rdt
export PB_PLANNING_SOURCE_DIR=~/improbable_rdt/pybullet-planning/
python teleop_franka.py --demo_folder=mugandshelfreal --offset=0 --hz=2 --max_path_length=100 --extra_params=booknshelve,booknshelve_debug_low_randomness,booknshelve2
python teleop_franka.py --demo_folder=cupntrashreal --offset=0 --hz=2 --max_path_length=100 --extra_params=cupntrash,kitchentoaster_randomness,no_action_rand --num_demos=15 --cam_index=1
python teleop_franka.py --demo_folder=cupntrashreal --offset=0 --hz=2 --max_path_length=100 --extra_params=cupntrash,kitchentoaster_randomness,no_action_rand --num_demos=15 --cam_index=1