diff --git a/README.md b/README.md
index bb5e57a..bc87725 100644
--- a/README.md
+++ b/README.md
@@ -68,17 +68,17 @@ $ wget --recursive --no-parent --no-host-directories --relative --reject="index.
 ### 🖥️ Simulation
 To reproduce our simulation benchmark results, install our conda environment on a Linux machine with Nvidia GPU. On Ubuntu 20.04 you need to install the following apt packages for mujoco:
 ```console
-sudo apt install -y libosmesa6-dev libgl1-mesa-glx libglfw3 patchelf
+$ sudo apt install -y libosmesa6-dev libgl1-mesa-glx libglfw3 patchelf
 ```
 
 We recommend [Mambaforge](https://github.com/conda-forge/miniforge#mambaforge) instead of the standard anaconda distribution for faster installation: 
 ```console
-mamba env create -f conda_environment.yaml
+$ mamba env create -f conda_environment.yaml
 ```
 
 but you can use conda as well: 
 ```console
-conda env create -f conda_environment.yaml
+$ conda env create -f conda_environment.yaml
 ```
 
 The `conda_environment_macos.yaml` file is only for development on MacOS and does not have full support for benchmarks.
@@ -357,6 +357,20 @@ data/pusht_cchi_v7_replay.zarr
 
 Each array in `data` stores one data field from all episodes concatenated along the first dimension (time). The `meta/episode_ends` array stores the end index for each episode along the fist dimension.
 
+### `SharedMemoryRingBuffer`
+The [`SharedMemoryRingBuffer`](./diffusion_policy/shared_memory/shared_memory_ring_buffer.py) is a lock-free FILO data structure used extensively in our [real robot implementation](./diffusion_policy/real_world) to utilize multiple CPU cores while avoiding pickle serialization and locking overhead for `multiprocessing.Queue`. 
+
+As an example, we would like to get the most recent `To` frames from 5 RealSense cameras. We launch 1 realsense SDK/pipeline per process using [`SingleRealsense`](./diffusion_policy/real_world/single_realsense.py), each continuously writes the captured images into a `SharedMemoryRingBuffer` shared with the main process. We can very quickly get the last `To` frames in the main process due to the FILO nature of `SharedMemoryRingBuffer`.
+
+We also implemented [`SharedMemoryQueue`](./diffusion_policy/shared_memory/shared_memory_queue.py) for FIFO, which is used in [`RTDEInterpolationController`](./diffusion_policy/real_world/rtde_interpolation_controller.py).
+
+### `RealEnv`
+In contrast to [OpenAI Gym](https://gymnasium.farama.org/), our polices interact with the environment asynchronously. In [`RealEnv`](./diffusion_policy/real_world/real_env.py), the `step` method in `gym` is split into two methods: `get_obs` and `exec_actions`. 
+
+The `get_obs` method returns the latest observation from `SharedMemoryRingBuffer` as well as their corresponding timestamps. This method can be call at any time during an evaluation episode.
+
+The `exec_actions` method accepts a sequence of actions and timestamps for the expected time of execution for each step. Once called, the actions are simply enqueued to the `RTDEInterpolationController`, and the method returns without blocking for execution.
+
 ## 🩹 Adding a Task
 Read and imitate:
 * `diffusion_policy/dataset/pusht_image_dataset.py`
@@ -372,3 +386,17 @@ Read and imitate:
 * `diffusion_policy/config/train_diffusion_unet_image_workspace.yaml`
 
 Make sure your workspace yaml's `_target_` points to the new workspace class you created.
+
+## 🏷️ License
+This repository is released under the MIT license. See [LICENSE](LICENSE) for additional details.
+
+## 🙏 Acknowledgement
+* Our [`ConditionalUnet1D`](./diffusion_policy/model/diffusion/conditional_unet1d.py) implementation is adapted from [Planning with Diffusion](https://github.com/jannerm/diffuser).
+* Our [`TransformerForDiffusion`](./diffusion_policy/model/diffusion/transformer_for_diffusion.py) implementation is adapted from [MinGPT](https://github.com/karpathy/minGPT).
+* The [BET](./diffusion_policy/model/bet) baseline is adapted from [its original repo](https://github.com/notmahi/bet).
+* The [IBC](./diffusion_policy/policy/ibc_dfo_lowdim_policy.py) baseline is adapted from [Kevin Zakka's reimplementation](https://github.com/kevinzakka/ibc).
+* The [Robomimic](https://github.com/ARISE-Initiative/robomimic) tasks and [`ObservationEncoder`](https://github.com/ARISE-Initiative/robomimic/blob/master/robomimic/models/obs_nets.py) are used extensively in this project.
+* The [Push-T](./diffusion_policy/env/pusht) task is adapted from [IBC](https://github.com/google-research/ibc).
+* The [Block Pushing](./diffusion_policy/env/block_pushing) task is adapted from [BET](https://github.com/notmahi/bet) and [IBC](https://github.com/google-research/ibc).
+* The [Kitchen](./diffusion_policy/env/kitchen) task is adapted from [BET](https://github.com/notmahi/bet) and [Relay Policy Learning](https://github.com/google-research/relay-policy-learning).
+* Our [shared_memory](./diffusion_policy/shared_memory) data structures are heavily inspired by [shared-ndarray2](https://gitlab.com/osu-nrsg/shared-ndarray2).