Brain Corp robotic motion planning environment

This is a python package that provides a robotic planning environment with an interface that is similar to OpenAI gym.

The task is navigation of 2d large robots in tight spaces.

env = RandomMiniEnv()
obs = env.reset()


while not done:
    action = planner.plan(obs)
    observation, reward, done, info = env.step(action)

Statefullness of the env

You can save and restore the full state of the environment. This is useful for example for Monte Carlo simulation, where you need to run many rollouts from one state.

The syntax works as follows

state = env.get_state()

# do sth with the env, try out some plan
while sth():
    env.act(some_action)

# restore the env to the previous state
env.set_state(state)

Pickling, Saving, Loading

All pieces of the framework can be rendered to basic python types (int, float, dict, the most complicated numpy.ndarray). What is more, the objects can be constructed back from this representation in a completely idempotent way.

This way you can use pickle to save / load whatever you want.

Tour of the main classes

Observation from envs.base.obs represents the type of observation that is returned from the environment.
Action from envs.base.action represents the action that is passed for execution to the environment
PlanEnv from envs.base.env is the class that represents the environment itself.
State from envs.base.env is the class that represents the state of the environment
ContinuousRewardProvider from envs.base.reward is a class that interprets what robot has done in the environment and assigns rewars for it

Runners

Please see the scripts in scripts for examples how to run the environment.

Types of environments

The base class is envs.base.env.PlanEnv You need to supply a path to follow and costmap that represents obstacles.

An example of this is given in the script scripts.env_runners.rw_randomized_corridor_3_boxes, where we load a custom costmap and path based on percepts from a real robot.

There are additional classes that supply these path and costmaps in special ways:

envs.mini_env.RandomMiniEnv - randomized, synthetic 'parallel parking' small square environment with one obstacle, where you have to reach next pose that can be close to you, but can have an ankward path to it.
envs.synth_turn_env.AisleTurnEnv - a randomized synthetic environment where you have to follow a path turnning into an aisle.

Relation to OpenAI gym

A frequently asked question we get is

why not just subclass gym.Env? This is because gym depends on scipy. At Brain Corp, we choose not to use scipy.

As far as we can see from the code, OpenAI gym is on the road to remove this dependency. Hopefully then we will subclass it fully.