This repository contains a number of challenging environments based on OpenAI's Gym hand manipulation tasks, all of which are challenging for current RL and trajectory optimisation techniques. The GIFs of example solutions for some of the environments come from a soon-to-be published custom trajectory optimisation technique I have developed.

Requirements: Mujoco-py and Gym Once these are installed, run pip install dexterous-gym. Alternatively, clone the repository and run pip install -e . from within the main directory.

import gym
import dexterous_gym
env = gym.make("EggCatchUnderarm-v0")

See dexterous_gym/__init__.py for full list of registered environments. See dexterous_gym/examples/test_all_envs.py to run all available environments with random actions.

Variants: "PenSpin-v0".

This is a simple modification of the OpenAI gym HandPen environment where all we do is change the observation space to remove any notion of a goal and then define a custom reward function. This reward function encourages the pen to be spun whilst remaining horizontal.

A majority of the environments are goal-based, and have a similar API to the openAI Gym manipulation environments (observations are dictionaries with "observation", "achieved_goal", "desired_goal"). In the case of the two object environments "achieved_goal" and "desired_goal" are also dictionaries with entries "object_1" and "object_2". All environments come with a standard dense-reward setting (where the reward is defined in terms of the positional and rotational distance between the achieved goal(s) and the desired goal(s) as well as a sparse setting, where the reward is -1.0 until the goal(s) are achieved (at which point the reward is 0.0).