Implement "continuous" ravens.

[kevinzakka]

This PR implements a continuous version of the environments and tasks, which allows us to generate dense trajectories. Specifically it contains:

• A ContinuousEnvironment class which subclasses the base environment.Environment class and eliminates the "do-it-in-1-step" PickPlace and push primitives.
• Adds a continuous kwarg to each instance of Task which should be toggled when using ContinuousEnvironment.
• Implements a continuous version of the oracle which uses planners.PickPlacePlanner for pick-and-place tasks and planners.PushPlanner for pushing tasks to generate intermediate actions given the pick and place poses generated by the discrete version of the oracle. The base Planner class is implemented via splines.

Here is some example code to illustrate the new API, which closely mimics the discrete one:

env = environment.ContinuousEnvironment(ASSETS_PATH)
task = tasks.StackBlockPyramid(continuous=True)
env.set_task(task)
env.seed(1)
agent = task.oracle(env)  # This is now an instance of `ContinuousOracle`.
obs = env.reset()

info = None
done = False
for i in range(100):
    act = agent.act(obs, info)
    obs, rew, done, info = env.step(act)
    if done:
        print(f"Done at step {i}. Exiting.")
        break

This generates the following trajectory for the StackBlockPyramid task:

https://user-images.githubusercontent.com/10518920/124039895-ac213780-d9b8-11eb-91a2-97577074bbe7.mp4

You can also increase the density of the steps generated by the oracle, which allows you to generate more intermediate observations. Here is a second demonstration generated via agent = task.oracle(env, steps_per_seg=5):

https://user-images.githubusercontent.com/10518920/124040871-9ad92a80-d9ba-11eb-9c6b-524be81f5e56.mp4

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[kevinzakka]

@googlebot I signed it!

[ayzaan]

Hey @kevinzakka, I was actually able to apply the linter fixes internally and merge the PR that way. Thanks for the contribution!