Welcome to the exciting world of dexterous manipulation utilizing Deep Reinforcement Learning. In this blog post, we will guide you through the DAPG project, which supports advanced manipulation tasks demonstrated at RSS 2018. Let’s get started!
What is DAPG?
The DAPG (Differentiable Actor-Critic Policy Gradient) project focuses on complex dexterous manipulation tasks through deep reinforcement learning. The project is modularly organized into three repositories to foster development efficiency and knowledge sharing.
Project Structure
- mjrl: This repository contains learning algorithms used for continuous control tasks within the MuJoCo simulation. Here, you will find the NPG implementation and the DAPG algorithm.
- mj_envs: This repository provides continuous control tasks also within MuJoCo, including dexterous hand manipulation tasks.
- hand_dapg: The final repository acts as the central hub, hosting human demonstrations and pre-trained policies for various tasks.
Getting Started with DAPG
To get your hands dirty with the DAPG project, follow these steps:
- Step 1: Install mjrl using the setup instructions mentioned in the repository. It provides an Anaconda environment helpful for managing various MuJoCo tasks.
- Step 2: Clone and install mj_envs. Ensure to follow the instructions carefully, especially regarding git submodule cloning.
- Step 3: Clone the hand_dapg repository and run the following commands to visualize demonstrations and pre-trained policies:
$ cd dapg
$ python utils/visualize_demos.py --env_name relocate-v0
$ python utils/visualize_policy.py --env_name relocate-v0 --policy policies/relocate-v0.pickleUnderstanding the Code Like an Artist
Think of the commands above as directing a skilled artist who is using distinct brushes (Python scripts) to paint (visualize) various parts of a beautiful landscape (manipulation tasks). Each brushstroke (command) contributes to the overall masterpiece, allowing us to see how the dexterous hand behaves in different scenarios. Your understanding and manipulation of these brushes will determine how well the canvas (simulation environment) turns out.
Troubleshooting
During visualization, you may encounter a GLFW error, often due to mismatches in graphics drivers with mujoco-py. Fortunately, this can be resolved! You can fix this by explicitly loading the appropriate graphics drivers prior to running your scripts. Detailed information is available on the known issues page.
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Conclusion
At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.
