An OpenAI Gym Env for Panda
Project description
gym-panda
OpenaAI Gym Franka Emika Panda robot grasping environment implemented with PyBullet
Links
- GitHub: https://github.com/mahyaret/gym-panda
- PyPI: https://pypi.org/project/gym-panda/
- Documentation: https://www.etedal.net/2020/04/pybullet-panda_2.html
- Issue Tracker:https://github.com/mahyaret/gym-panda/issues
- Download: https://pypi.org/project/gym-panda/#files
Install
Install with pip
:
pip install gym-panda
Or, install from source:
git clone https://github.com/mahyaret/gym-panda.git
cd gym-panda
pip install .
Basic Usage
Running an environment:
import gym import gym_panda env = gym.make('panda-v0') env.reset() for _ in range(100): env.render() obs, reward, done, info = env.step( env.action_space.sample()) # take a random action env.close()
Running a PD control HACK!
import gym import gym_panda env = gym.make('panda-v0') observation = env.reset() done = False error = 0.01 fingers = 1 info = [0.7, 0, 0.1] k_p = 10 k_d = 1 dt = 1./240. # the default timestep in pybullet is 240 Hz t = 0 for i_episode in range(20): observation = env.reset() fingers = 1 for t in range(100): env.render() print(observation) dx = info[0]-observation[0] dy = info[1]-observation[1] target_z = info[2] if abs(dx) < error and abs(dy) < error and abs(dz) < error: fingers = 0 if (observation[3]+observation[4])<error+0.02 and fingers==0: target_z = 0.5 dz = target_z-observation[2] pd_x = k_p*dx + k_d*dx/dt pd_y = k_p*dy + k_d*dy/dt pd_z = k_p*dz + k_d*dz/dt action = [pd_x,pd_y,pd_z,fingers] observation, reward, done, info = env.step(action) if done: print("Episode finished after {} timesteps".format(t+1)) break env.close()
Development
- clone the repo:
git clone https://github.com/mahyaret/gym-panda.git
cd gym-panda
- Create/activate the virtual environment:
pipenv shell --python=python3.6
- Install development dependencies:
pipenv install --dev
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