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This work builds on top of mujoco-powered robosuite framework, and is designed for learning object-agnostic control policy for pick-and-place operations on a novel 6-DoF parallel manipulator using deep reinforcement learning. This pipeline utilizes DDPG combined with HER algorithm for training a robust manipulation policy.

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biorobotics/Wombat_robosuite

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🤖 Wombat_robosuite

Simulation framework for learning an object-agnostic control policy for pick-and-place operation on a novel 6-DoF parallel manipulator (aka D3 arm) using Deep reinforcement learning, specifically DDPG (Deep Deterministic Policy Gradient) + HER (Hindshight Experience Replay) algorithm.

🛠️ Installation

  • Install MuJoCo
  1. Download the MuJoCo version 2.1 binaries for Linux.
  2. Extract the downloaded mujoco210 directory into ~/.mujoco/mujoco210.
  3. Clone this repository
cd ~
git clone -b main https://github.com/biorobotics/Wombat_robosuite.git
  1. Install the dependencies
cd Wombat_robosuite/robosuite
pip3 install -r requirements.txt
  1. Test your installation with
python demos/demo_random_action.py

🔄 Pipeline

🏋️ Training (DDPG+HER)

cd ~/Wombat_robosuite/robosuite/HER+DDPG/hindsight-experience-replay-latest
python D3_train_dyn_rand.py

Weights will be stored in 'saved_models/PickPlaceiPhone'. Currently, the best trained weights are with the name 'Model_dyn_rand_epoch_50.pt'.

🎮 Demo (Testing)

cd ~/Wombat_robosuite/robosuite/HER+DDPG/hindsight-experience-replay-latest
python D3_demo_dyn_rand.py

📊 Results

🙏 Credits

This project builds upon the excellent work done in the Robosuite Framework. Many thanks to all the contributors involved.

About

This work builds on top of mujoco-powered robosuite framework, and is designed for learning object-agnostic control policy for pick-and-place operations on a novel 6-DoF parallel manipulator using deep reinforcement learning. This pipeline utilizes DDPG combined with HER algorithm for training a robust manipulation policy.

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