fancy_gym/README.md

ALR Robotics Control 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 Dynamic Movement Primitives (DMPs) and Probabilistic Movement Primitives (DetPMP, we only consider the mean usually).

Step-based 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

Motion Primitive Environments (Episodic environments)

Unlike step-based environments, these motion primitive (MP) environments are closer to stochastic search and what can be found in robotics. They always execute a full trajectory, which is computed by a Dynamic Motion Primitive (DMP) or Probabilitic Motion Primitive (DetPMP) and translated into individual actions with a controller, e.g. a PD controller. The actual Controller, however, depends on the type of environment, i.e. position, velocity, or torque controlled. The goal is to learn the parametrization of the motion primitives in order to generate a suitable trajectory. MP This can also be done in a contextual setting, where all changing elements of the task are exposed once in the beginning. This requires to find a new parametrization for each trajectory. All environments provide the full cumulative episode reward and additional information about early terminations, e.g. due to collisions.

Classic Control

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

OpenAI gym Environments

These environments are wrapped-versions of their OpenAI-gym counterparts.

Name	Description	Trajectory Horizon	Action Dimension	Context Dimension
ContinuousMountainCarDetPMP-v0	A DetPmP wrapped version of the ContinuousMountainCar-v0 environment.	100	1
ReacherDetPMP-v2	A DetPmP wrapped version of the Reacher-v2 environment.	50	2
FetchSlideDenseDetPMP-v1	A DetPmP wrapped version of the FetchSlideDense-v1 environment.	50	4
FetchReachDenseDetPMP-v1	A DetPmP wrapped version of the FetchReachDense-v1 environment.	50	4

Deep Mind Control Suite Environments

These environments are wrapped-versions of their Deep Mind Control Suite (DMC) counterparts. Given most task can be solved in shorter horizon lengths than the original 1000 steps, we often shorten the episodes for those task.

Name	Description	Trajectory Horizon	Action Dimension	Context Dimension
dmc_ball_in_cup-catch_detpmp-v0	A DetPmP wrapped version of the "catch" task for the "ball_in_cup" environment.	50	10	2
dmc_ball_in_cup-catch_dmp-v0	A DMP wrapped version of the "catch" task for the "ball_in_cup" environment.	50	10	2
dmc_reacher-easy_detpmp-v0	A DetPmP wrapped version of the "easy" task for the "reacher" environment.	1000	10	4
dmc_reacher-easy_dmp-v0	A DMP wrapped version of the "easy" task for the "reacher" environment.	1000	10	4
dmc_reacher-hard_detpmp-v0	A DetPmP wrapped version of the "hard" task for the "reacher" environment.	1000	10	4
dmc_reacher-hard_dmp-v0	A DMP wrapped version of the "hard" task for the "reacher" environment.	1000	10	4

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