add v2.0

README.md

@@ -1,82 +1,40 @@
-# DeepClawBenchmark
+![DeepClaw-Logo](docs/asset/fig-DeepClaw.png)
 
-paper | poster | video
+# The DeepClaw Benchmark
 
-The DeepClaw benchmark is a framework for establishing a reproducible and shareable benchmarking for dexterous manipulation. DeepClaw benchmark provides a standardized dexterous manipulation pipeline consisting of four subtasks: localization, recognition, grasp planning, and motion planning. It also provide necessary components to benchmark manipulations including hardware drivers, data I/O utilities, baseline algorithm modules and evaluation metrics.
+The DeepClaw is a benchmarking model zoo that functions as a Reconfigurable Robotic Manipulation System for Robot Learning. The main homepage can be found at [here](https://bionicdl-sustech.github.io/DeepClawBenchmark/). This is the GitHub repository of DeepClaw source code, including instructions for installing and using DeepClaw.
 
-The DeepClaw has been used extensively to benchmark a series of manipulation tasks including claw machine, jigsaw game and TicTacToe. The source codes of these experiments are placed under /examples.
-![](https://github.com/bionicdl-sustech/DeepClawBenchmark/blob/master/Documents/Figs/deepclaw-framework.png)
+## Resources
 
-## Quick Start
+- Documentation: https://bionicdl-sustech.github.io/DeepClawBenchmark/
+- Paper explaining DeepClaw: [arXiv:2005.02588 [cs.RO]](https://arxiv.org/abs/2005.02588)
+- Papers using DeepClaw: 
+    - [arXiv:2003.01584 [cs.RO]](https://arxiv.org/abs/2003.01584)
+    - [arXiv:2003.01583 [cs.RO]](https://arxiv.org/abs/2003.01583)
+    - [arXiv:2003.01582 [cs.RO]](https://arxiv.org/abs/2003.01582)
 
-### Prerequisites
+## Code Organization
 
-DeepClaw framework has only been tested with *Python 2.7* and *Ubuntu 16.04 LTS*. We recommend using a virtual environment (such as virtualenv) to manage DeepClaw.
+The DeepClaw code is organized as follows:
 
-Install virtualenv.
+    configs/                    configuration for robotic station for manipulation tasks.
+    deepclaw/drivers/           drivers for various robotic hardware, i.e. ur, franka, aubo.
+    deepclaw/models/            model zoo for segmentation, classification, pick planning, and motion planning.
+    deepclaw/utils/             server setup with dockers and client setup for laptops (x86) and jetson (arm).
+    projects/proj_TrashSorting  a sample project to run deepclaw for sorting trash.
+    datasets/trash              description of trash sorting dataset
+    docs/                       description of this document as a manual.
+    data/trash                  data on trash sorting
 
-```shell
-$ pip install -U virtualenv
-```
-
-Create a new virtual environment.
-
-```shell
-$ virtualenv -p /usr/bin/python2.7 ./DCvenv
-```
-
-Activate or retreat from virtual environment.
-
-```shell
-$ source ./DCvenv/bin/activate # activate virtual environment
-$ deactivate # retreat from virtual environment
-```
-
-### Installation
-
-Clone or download DeepClaw from Github.
-
-```shell
-$ git clone https://github.com/bionicdl-sustech/DeepClawBenchmark.git
-$ cd ./DeepClawBenchmark
-```
-
-Install Prerequisites:
-
-```shell
-$ pip install -r requirements.txt
-```
-Build libfranka server
-```shell
-$ cd ./DeepClawBenchmark/driver/arms/Franka/libfraka_server
-$ mkdir build
-$ cd build
-$ cmake ..
-$ make
-```
-### Verify Installation
-Run calibration task with your drivers, for example, UR10e, HandE, Kinect and so on.
+## Bibliography
 
-```shell
-$ python main.py ur10e hande kinect-azure calibration true
 ```
-
-There also are some test cases for testing your installation and calibration.
-
-[Test cases](https://github.com/bionicdl-sustech/DeepClawBenchmark/blob/master/Documents/TestCases.md)
-
-## <a name="tasks">Tasks</a>
-We have implemented some tasks using DeepClaw with classical algorithm modules:
-- Task Family 1: [Jigsaw](https://github.com/bionicdl-sustech/DeepClawBenchmark/blob/master/Documents/Jigsaw_task/task_description.md)
-- Task Family 2: Tic-tac-toe Game
-- Task Family 3: Toy Claw Machine
-
-Find the task description template [here](https://github.com/bionicdl-sustech/DeepClawBenchmark/blob/master/Documents/Task-Description-Template.md).
-And we encourage developers to create new tasks ([how to create](https://github.com/bionicdl-sustech/DeepClawBenchmark/blob/python2.7/documents/How-to-Create-Task.md)).
-## Algorithm Modules
-We also provide modules pool for developers to assembly their own manipulation tasks.
-
-Find all modules description [here](https://github.com/bionicdl-sustech/DeepClawBenchmark/tree/python2.7/modules).
-And how to create a new module.
-## References
-[1] O. Kroemer, S. Niekum, and G. Konidaris, “A review of robot learning for manipulation: Challenges, representations, and algorithms,”arXiv preprintarXiv:1907.03146, 2019.
+@misc{wan2020deepclaw,
+    title={DeepClaw: A Robotic Hardware Benchmarking Platform for Learning Object Manipulation},
+    author={Fang Wan and Haokun Wang and Xiaobo Liu and Linhan Yang and Chaoyang Song},
+    year={2020},
+    eprint={2005.02588},
+    archivePrefix={arXiv},
+    primaryClass={cs.RO}
+}
+```