ALR Environments

This repository collects custom Robotics environments not included in benchmark suites like OpenAI gym, rllab, etc. Creating a custom (Mujoco) gym environment can be done according to this guide. For stochastic search problems with gym interface use the Rosenbrock-v0 reference implementation. We also support to solve environments with DMPs. When adding new DMP tasks check the ViaPointReacherDMP-v0 reference implementation. When simply using the tasks, you can also leverage the wrapper class DmpWrapper to turn normal gym environments in to DMP tasks.

Environments

Currently we have the following environments:

Mujoco

Name	Description	Horizon	Action Dimension	Observation Dimension
ALRReacher-v0	Modified (5 links) Mujoco gym's Reacher-v2 (2 links)	200	5	21
ALRReacherSparse-v0	Same as ALRReacher-v0, but the distance penalty is only provided in the last time step.	200	5	21
ALRReacherSparseBalanced-v0	Same as ALRReacherSparse-v0, but the end-effector has to remain upright.	200	5	21
ALRLongReacher-v0	Modified (7 links) Mujoco gym's Reacher-v2 (2 links)	200	7	27
ALRLongReacherSparse-v0	Same as ALRLongReacher-v0, but the distance penalty is only provided in the last time step.	200	7	27
ALRLongReacherSparseBalanced-v0	Same as ALRLongReacherSparse-v0, but the end-effector has to remain upright.	200	7	27
ALRBallInACupSimple-v0	Ball-in-a-cup task where a robot needs to catch a ball attached to a cup at its end-effector.	4000	3	wip
ALRBallInACup-v0	Ball-in-a-cup task where a robot needs to catch a ball attached to a cup at its end-effector	4000	7	wip
ALRBallInACupGoal-v0	Similiar to ALRBallInACupSimple-v0 but the ball needs to be caught at a specified goal position	4000	7	wip

Classic Control

Name	Description	Horizon	Action Dimension	Observation Dimension
SimpleReacher-v0	Simple reaching task (2 links) without any physics simulation. Provides no reward until 150 time steps. This allows the agent to explore the space, but requires precise actions towards the end of the trajectory.	200	2	9
LongSimpleReacher-v0	Simple reaching task (5 links) without any physics simulation. Provides no reward until 150 time steps. This allows the agent to explore the space, but requires precise actions towards the end of the trajectory.	200	5	18
ViaPointReacher-v0	Simple reaching task leveraging a via point, which supports self collision detection. Provides a reward only at 100 and 199 for reaching the viapoint and goal point, respectively.	200	5	18
HoleReacher-v0	5 link reaching task where the end-effector needs to reach into a narrow hole without collding with itself or walls	200	5	18

DMP Environments

These environments are closer to stochastic search. They always execute a full trajectory, which is computed by a DMP and executed by a controller, e.g. a PD controller. The goal is to learn the parameters of this DMP to generate a suitable trajectory. All environments provide the full episode reward and additional information about early terminations, e.g. due to collisions.

Name	Description	Horizon	Action Dimension	Context Dimension
ViaPointReacherDMP-v0	A DMP provides a trajectory for the ViaPointReacher-v0 task.	200	25
HoleReacherFixedGoalDMP-v0	A DMP provides a trajectory for the HoleReacher-v0 task with a fixed goal attractor.	200	25
HoleReacherDMP-v0	A DMP provides a trajectory for the HoleReacher-v0 task. The goal attractor needs to be learned.	200	30
ALRBallInACupSimpleDMP-v0	A DMP provides a trajectory for the ALRBallInACupSimple-v0 task where only 3 joints are actuated.	4000	15
ALRBallInACupDMP-v0	A DMP provides a trajectory for the ALRBallInACup-v0 task.	4000	35
ALRBallInACupGoalDMP-v0	A DMP provides a trajectory for the ALRBallInACupGoal-v0 task.	4000	35	3

Stochastic Search

Name	Description	Horizon	Action Dimension	Observation Dimension
Rosenbrock{dim}-v0	Gym interface for Rosenbrock function. {dim} is one of 5, 10, 25, 50 or 100.	1	{dim}	0

Install

1. Clone the repository

git clone git@github.com:ALRhub/alr_envs.git

2. Go to the folder

cd alr_envs

3. Install with

pip install -e . 

4. Use (see example.py):

import gym

env = gym.make('alr_envs:SimpleReacher-v0')
state = env.reset()

for i in range(10000):
    state, reward, done, info = env.step(env.action_space.sample())
    if i % 5 == 0:
        env.render()

    if done:
        state = env.reset()

For an example using a DMP wrapped env and asynchronous sampling look at mp_env_async_sampler.py