Franka Kitchen#

Multitask environment in which a 9-DoF Franka robot is placed in a kitchen containing several common household items. The goal of each task is to interact with the items in order to reach a desired goal configuration.

The tasks can be selected when the environment is initialized passing a list of tasks to the tasks_to_complete argument as follows:

import gymnasium as gym

env = gym.make('FrankaKitchen-v1', tasks_to_complete=['microwave', 'kettle'])

The possible tasks to complete are:



bottom burner

twist control knob to activate bottom left burner in the stove.

top burner

twist control knob to activate top left burner in the stove.

light switch

move a lever switch to turn on a light over the burners.

slide cabinet

slide open the cabinet door.

hinge cabinet

open a hinge cabinet door.


open the microwave door.


move the kettle from the bottom burner to the top burner.


These environments were first introduced in “Relay Policy Learning: Solving Long-Horizon Tasks via Imitation and Reinforcement Learning” by Abhishek Gupta, Vikash Kumar, Corey Lynch, Sergey Levine, Karol Hausman, and later modified in “D4RL: Datasets for Deep Data-Driven Reinforcement Learning” by Justin Fu, Aviral Kumar, Ofir Nachum, George Tucker, Sergey Levine. Both publications can be cited as follows:

  title={Relay policy learning: Solving long-horizon tasks via imitation and reinforcement learning},
  author={Gupta, Abhishek and Kumar, Vikash and Lynch, Corey and Levine, Sergey and Hausman, Karol},
  journal={arXiv preprint arXiv:1910.11956},
    title={D4RL: Datasets for Deep Data-Driven Reinforcement Learning},
    author={Justin Fu and Aviral Kumar and Ofir Nachum and George Tucker and Sergey Levine},