real-robots 0.1.14

Robots that learn to interact with the environment autonomously

real-robots

Robots that learn to interact with the environment autonomously

Installation

pip install -U real_robots

If everything went well, then you should be able to run :

real-robots-demo

and it should (eventually) open up a small window with a little robotic arm doing random stuff.

Usage

import gym
import numpy as np
import time
import real_robots
from real_robots.policy import BasePolicy

class RandomPolicy(BasePolicy):
    def __init__(self, action_space):
        self.action_space = action_space
        self.action = np.zeros(action_space.shape[0])
        self.action += -np.pi*0.5

    def step(self, observation, reward, done):
        self.action += 0.4*np.pi*np.random.randn(self.action_space.shape[0])
        return self.action

env = gym.make("REALRobot-v0")
pi = RandomPolicy(env.action_space)
env.render("human")

observation = env.reset()
reward, done = 0, False
for t in range(40):
    time.sleep(1./1000.)
    action = pi.step(observation, reward, done)
    observation, reward, done, info = env.step(action)
    print(t, reward)

Local Evaluation

import gym
import numpy as np
import real_robots
from real_robots.policy import BasePolicy

class RandomPolicy(BasePolicy):
    def __init__(self, action_space):
        self.action_space = action_space
        self.action = np.zeros(action_space.shape[0])
        self.action += -np.pi*0.5

    def step(self, observation, reward, done):
        self.action += 0.4*np.pi*np.random.randn(self.action_space.shape[0])
        return self.action

result, detailed_scores = real_robots.evaluate(
                RandomPolicy,
                intrinsic_timesteps=40,
                extrinsic_timesteps=40,
                extrinsic_trials=5,
                visualize=True,
                goals_dataset_path="./goals.npy.npz",
            )
#  NOTE : You can find a sample goals.npy.npz file at
#
#  https://aicrowd-production.s3.eu-central-1.amazonaws.com/misc/REAL-Robots/goals.npy.npz
print(result)
# {'score_total': 0.2327459533906755, 'score_2D': 0.6982378601720265, 'score_2.5D': 0, 'score_3D': 0}
print(detailed_scores)
# {'2D': [0.6646365565451159, 0.6632591441787807, 0.7569003923985664, 0.7167885964780916, 0.6896046112595778]}

See also our FAQ.

• Free software: MIT license

Features

• TODO

Authors

• Francesco Mannella
• Emilio Cartoni
• Sharada Mohanty