add realman shadow src

realman_src/realman_aloha/shadow_rm_act/README.md

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+# ACT: Action Chunking with Transformers
+
+### *New*: [ACT tuning tips](https://docs.google.com/document/d/1FVIZfoALXg_ZkYKaYVh-qOlaXveq5CtvJHXkY25eYhs/edit?usp=sharing)
+TL;DR: if your ACT policy is jerky or pauses in the middle of an episode, just train for longer! Success rate and smoothness can improve way after loss plateaus.
+
+#### Project Website: https://tonyzhaozh.github.io/aloha/
+
+This repo contains the implementation of ACT, together with 2 simulated environments:
+Transfer Cube and Bimanual Insertion. You can train and evaluate ACT in sim or real.
+For real, you would also need to install [ALOHA](https://github.com/tonyzhaozh/aloha).
+
+### Updates:
+You can find all scripted/human demo for simulated environments [here](https://drive.google.com/drive/folders/1gPR03v05S1xiInoVJn7G7VJ9pDCnxq9O?usp=share_link).
+
+
+### Repo Structure
+- ``imitate_episodes.py`` Train and Evaluate ACT
+- ``policy.py`` An adaptor for ACT policy
+- ``detr`` Model definitions of ACT, modified from DETR
+- ``sim_env.py`` Mujoco + DM_Control environments with joint space control
+- ``ee_sim_env.py`` Mujoco + DM_Control environments with EE space control
+- ``scripted_policy.py`` Scripted policies for sim environments
+- ``constants.py`` Constants shared across files
+- ``utils.py`` Utils such as data loading and helper functions
+- ``visualize_episodes.py`` Save videos from a .hdf5 dataset
+
+
+### Installation
+
+    conda create -n aloha python=3.8.10
+    conda activate aloha
+    pip install torchvision
+    pip install torch
+    pip install pyquaternion
+    pip install pyyaml
+    pip install rospkg
+    pip install pexpect
+    pip install mujoco==2.3.7
+    pip install dm_control==1.0.14
+    pip install opencv-python
+    pip install matplotlib
+    pip install einops
+    pip install packaging
+    pip install h5py
+    pip install ipython
+    cd act/detr && pip install -e .
+
+### Example Usages
+
+To set up a new terminal, run:
+
+    conda activate aloha
+    cd <path to act repo>
+
+### Simulated experiments
+
+We use ``sim_transfer_cube_scripted`` task in the examples below. Another option is ``sim_insertion_scripted``.
+To generated 50 episodes of scripted data, run:
+
+    python3 record_sim_episodes.py \
+    --task_name sim_transfer_cube_scripted \
+    --dataset_dir <data save dir> \
+    --num_episodes 50
+
+To can add the flag ``--onscreen_render`` to see real-time rendering.
+To visualize the episode after it is collected, run
+
+    python3 visualize_episodes.py --dataset_dir <data save dir> --episode_idx 0
+
+To train ACT:
+    
+    # Transfer Cube task
+    python3 imitate_episodes.py \
+    --task_name sim_transfer_cube_scripted \
+    --ckpt_dir <ckpt dir> \
+    --policy_class ACT --kl_weight 10 --chunk_size 100 --hidden_dim 512 --batch_size 8 --dim_feedforward 3200 \
+    --num_epochs 2000  --lr 1e-5 \
+    --seed 0
+
+
+To evaluate the policy, run the same command but add ``--eval``. This loads the best validation checkpoint.
+The success rate should be around 90% for transfer cube, and around 50% for insertion.
+To enable temporal ensembling, add flag ``--temporal_agg``.
+Videos will be saved to ``<ckpt_dir>`` for each rollout.
+You can also add ``--onscreen_render`` to see real-time rendering during evaluation.
+
+For real-world data where things can be harder to model, train for at least 5000 epochs or 3-4 times the length after the loss has plateaued.
+Please refer to [tuning tips](https://docs.google.com/document/d/1FVIZfoALXg_ZkYKaYVh-qOlaXveq5CtvJHXkY25eYhs/edit?usp=sharing) for more info.
+