Project: situations

A classifier decides what the robot does next.

8.8LearningRobot club30 min

Everything in this module, put to work. A wall lies across the mat with a gap at each end, and the goal zone is on the far side. The robot has no map. It has a classifier that says what is in front of it, and a program that decides what to do about it.

The idea

Classify as you go, and act on the label:

open: drive forward.
wall-ahead: slide sideways along the wall to find its end.
gap-left or gap-right: the wall ends here. Line up with the gap and go through.
Past the wall: head for the goal.

# the two lines every program starts with: the commands, then the robot
from bugbot import *
connect()

def level_rows():
    # rows 2 and 3: the 16 readings that look straight ahead
    return tof_grid()[16:32]

def nearest(sample, data):
    # the label of the recorded sample most like this one (smallest sum of squared differences)
    best_label, best_d = None, 1e18
    for feats, label in data:
        d = sum((a - b) ** 2 for a, b in zip(feats, sample))
        if d < best_d:
            best_label, best_d = label, d
    return best_label
DATA = [
    ([54, 52, 51, 51, 51, 51, 52, 54, 54, 52, 51, 51, 51, 51, 52, 54], 'open'),
    ([16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16], 'wall-ahead'),
    ([36, 47, 46, 46, 46, 16, 16, 16, 36, 47, 46, 46, 46, 16, 16, 16], 'gap-left'),
    ([16, 16, 16, 46, 46, 46, 47, 36, 16, 16, 16, 46, 46, 46, 47, 36], 'gap-right'),
    ([400, 400, 61, 61, 61, 61, 400, 400, 96, 62, 61, 61, 61, 61, 62, 64], 'open'),
    ([52, 50, 49, 49, 49, 49, 50, 52, 51, 50, 49, 49, 49, 49, 50, 51], 'open'),
    ([39, 38, 37, 37, 37, 37, 38, 39, 39, 38, 37, 37, 37, 37, 38, 39], 'open'),
    ([400, 400, 61, 61, 61, 61, 400, 400, 64, 62, 61, 61, 61, 61, 62, 64], 'open'),
    ([52, 50, 49, 49, 49, 49, 50, 52, 51, 50, 49, 49, 49, 49, 50, 51], 'open'),
    ([39, 38, 37, 37, 37, 37, 38, 39, 39, 38, 37, 37, 37, 37, 38, 39], 'open'),
    ([400, 400, 61, 61, 61, 61, 400, 400, 64, 62, 61, 61, 61, 61, 62, 96], 'open'),
    ([52, 50, 49, 49, 49, 49, 50, 52, 51, 50, 49, 49, 49, 49, 50, 51], 'open'),
    ([39, 38, 37, 37, 37, 37, 38, 39, 39, 38, 37, 37, 37, 37, 38, 39], 'open'),
    ([26, 25, 25, 25, 25, 25, 25, 26, 26, 25, 25, 25, 25, 25, 25, 26], 'wall-ahead'),
    ([18, 17, 17, 17, 17, 17, 17, 18, 17, 17, 17, 17, 17, 17, 17, 17], 'wall-ahead'),
    ([9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9], 'wall-ahead'),
    ([26, 25, 25, 25, 25, 25, 25, 26, 26, 25, 25, 25, 25, 25, 25, 26], 'wall-ahead'),
    ([18, 17, 17, 17, 17, 17, 17, 18, 17, 17, 17, 17, 17, 17, 17, 17], 'wall-ahead'),
    ([9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9], 'wall-ahead'),
    ([26, 25, 25, 25, 25, 25, 25, 26, 26, 25, 25, 25, 25, 25, 25, 26], 'wall-ahead'),
    ([18, 17, 17, 17, 17, 17, 17, 18, 17, 17, 17, 17, 17, 17, 17, 17], 'wall-ahead'),
    ([9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9], 'wall-ahead'),
    ([24, 34, 55, 59, 59, 59, 29, 30, 24, 33, 55, 59, 59, 59, 29, 30], 'gap-left'),
    ([24, 34, 51, 51, 51, 51, 25, 22, 24, 33, 51, 51, 51, 51, 25, 22], 'gap-left'),
    ([24, 34, 43, 43, 43, 43, 44, 18, 24, 33, 43, 43, 43, 43, 44, 18], 'gap-left'),
    ([42, 58, 59, 59, 29, 29, 29, 30, 42, 58, 59, 59, 29, 29, 29, 30], 'gap-left'),
    ([42, 52, 51, 51, 21, 21, 21, 22, 42, 52, 51, 51, 21, 21, 21, 22], 'gap-left'),
    ([42, 44, 43, 43, 13, 13, 13, 13, 42, 44, 43, 43, 13, 13, 13, 13], 'gap-left'),
    ([30, 29, 29, 29, 59, 59, 58, 42, 30, 29, 29, 29, 59, 59, 58, 42], 'gap-right'),
    ([22, 21, 21, 21, 51, 51, 52, 42, 22, 21, 21, 21, 51, 51, 52, 42], 'gap-right'),
    ([13, 13, 13, 13, 43, 43, 44, 42, 13, 13, 13, 13, 43, 43, 44, 42], 'gap-right'),
    ([30, 29, 59, 59, 59, 55, 34, 24, 30, 29, 59, 59, 59, 55, 33, 24], 'gap-right'),
    ([22, 25, 51, 51, 51, 51, 34, 24, 22, 25, 51, 51, 51, 51, 33, 24], 'gap-right'),
    ([18, 44, 43, 43, 43, 43, 34, 24, 18, 44, 43, 43, 43, 43, 33, 24], 'gap-right'),
]

# drive forward at 60 (keeps going until the next command)
forward(60)
# do this 6 times (tick counts from 0)
for tick in range(6):
    # pause 0.5 s (the robot keeps doing what it was told)
    wait(0.5)
    # where am I? (cm from where I started)
    x, y = position()
    print("y =", round(y), "->", nearest(level_rows(), DATA))
# all motors off
stop()

Run this in the simulator

The 34-sample dataset from lesson 8.4 makes the calls: open until about 30 cm from the wall, then wall-ahead.

Decide once

A classifier answers every tick, and near the end of the wall its answer can flicker between gap-left and gap-right as the view changes. A robot that changes its plan every tenth of a second slides left, then right, and goes nowhere. So when a gap label appears, remember which one in a variable, and from then on follow the plan, not the label. Keep printing what the model says: you need at least five model says: <label> lines, and they are how you see what it was thinking.

The geometry

Relative to the start, the wall is 60 cm ahead and runs from 36 cm to your left to 36 cm to your right. Each gap is 14 cm wide, so a robot going through with its centre 42 cm to the side has 3 cm to spare on each side. The goal zone is centred 82 cm ahead. go_to from Module 5 will hold a straight line through the gap and take you to the goal after it.

Task: situations

Get from the start to the goal zone without touching anything, using the classifier to decide what to do, printing model says: <label> at least five times on the way.

# the two lines every program starts with: the commands, then the robot
from bugbot import *
connect()
def level_rows():
    # rows 2 and 3: the 16 readings that look straight ahead
    return tof_grid()[16:32]

def nearest(sample, data):
    # the label of the recorded sample most like this one (smallest sum of squared differences)
    best_label, best_d = None, 1e18
    for feats, label in data:
        d = sum((a - b) ** 2 for a, b in zip(feats, sample))
        if d < best_d:
            best_label, best_d = label, d
    return best_label
DATA = [
    ([54, 52, 51, 51, 51, 51, 52, 54, 54, 52, 51, 51, 51, 51, 52, 54], 'open'),
    ([16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16], 'wall-ahead'),
    ([36, 47, 46, 46, 46, 16, 16, 16, 36, 47, 46, 46, 46, 16, 16, 16], 'gap-left'),
    ([16, 16, 16, 46, 46, 46, 47, 36, 16, 16, 16, 46, 46, 46, 47, 36], 'gap-right'),
    ([400, 400, 61, 61, 61, 61, 400, 400, 96, 62, 61, 61, 61, 61, 62, 64], 'open'),
    ([52, 50, 49, 49, 49, 49, 50, 52, 51, 50, 49, 49, 49, 49, 50, 51], 'open'),
    ([39, 38, 37, 37, 37, 37, 38, 39, 39, 38, 37, 37, 37, 37, 38, 39], 'open'),
    ([400, 400, 61, 61, 61, 61, 400, 400, 64, 62, 61, 61, 61, 61, 62, 64], 'open'),
    ([52, 50, 49, 49, 49, 49, 50, 52, 51, 50, 49, 49, 49, 49, 50, 51], 'open'),
    ([39, 38, 37, 37, 37, 37, 38, 39, 39, 38, 37, 37, 37, 37, 38, 39], 'open'),
    ([400, 400, 61, 61, 61, 61, 400, 400, 64, 62, 61, 61, 61, 61, 62, 96], 'open'),
    ([52, 50, 49, 49, 49, 49, 50, 52, 51, 50, 49, 49, 49, 49, 50, 51], 'open'),
    ([39, 38, 37, 37, 37, 37, 38, 39, 39, 38, 37, 37, 37, 37, 38, 39], 'open'),
    ([26, 25, 25, 25, 25, 25, 25, 26, 26, 25, 25, 25, 25, 25, 25, 26], 'wall-ahead'),
    ([18, 17, 17, 17, 17, 17, 17, 18, 17, 17, 17, 17, 17, 17, 17, 17], 'wall-ahead'),
    ([9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9], 'wall-ahead'),
    ([26, 25, 25, 25, 25, 25, 25, 26, 26, 25, 25, 25, 25, 25, 25, 26], 'wall-ahead'),
    ([18, 17, 17, 17, 17, 17, 17, 18, 17, 17, 17, 17, 17, 17, 17, 17], 'wall-ahead'),
    ([9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9], 'wall-ahead'),
    ([26, 25, 25, 25, 25, 25, 25, 26, 26, 25, 25, 25, 25, 25, 25, 26], 'wall-ahead'),
    ([18, 17, 17, 17, 17, 17, 17, 18, 17, 17, 17, 17, 17, 17, 17, 17], 'wall-ahead'),
    ([9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9], 'wall-ahead'),
    ([24, 34, 55, 59, 59, 59, 29, 30, 24, 33, 55, 59, 59, 59, 29, 30], 'gap-left'),
    ([24, 34, 51, 51, 51, 51, 25, 22, 24, 33, 51, 51, 51, 51, 25, 22], 'gap-left'),
    ([24, 34, 43, 43, 43, 43, 44, 18, 24, 33, 43, 43, 43, 43, 44, 18], 'gap-left'),
    ([42, 58, 59, 59, 29, 29, 29, 30, 42, 58, 59, 59, 29, 29, 29, 30], 'gap-left'),
    ([42, 52, 51, 51, 21, 21, 21, 22, 42, 52, 51, 51, 21, 21, 21, 22], 'gap-left'),
    ([42, 44, 43, 43, 13, 13, 13, 13, 42, 44, 43, 43, 13, 13, 13, 13], 'gap-left'),
    ([30, 29, 29, 29, 59, 59, 58, 42, 30, 29, 29, 29, 59, 59, 58, 42], 'gap-right'),
    ([22, 21, 21, 21, 51, 51, 52, 42, 22, 21, 21, 21, 51, 51, 52, 42], 'gap-right'),
    ([13, 13, 13, 13, 43, 43, 44, 42, 13, 13, 13, 13, 43, 43, 44, 42], 'gap-right'),
    ([30, 29, 59, 59, 59, 55, 34, 24, 30, 29, 59, 59, 59, 55, 33, 24], 'gap-right'),
    ([22, 25, 51, 51, 51, 51, 34, 24, 22, 25, 51, 51, 51, 51, 33, 24], 'gap-right'),
    ([18, 44, 43, 43, 43, 43, 34, 24, 18, 44, 43, 43, 43, 43, 33, 24], 'gap-right'),
]
# do this 50 times (tick counts from 0)
for tick in range(50):
    print('model says:', nearest(level_rows(), DATA))
    # drive forward at 60 (keeps going until the next command)
    forward(60)
    # pause 0.1 s (the robot keeps doing what it was told)
    wait(0.1)
# all motors off
stop()

Challenges

Go through the right-hand gap instead.
Use the network from lesson 8.4 as the classifier instead of nearest.
Start the robot facing the wall from the other side (edit the scene in free play) and get back.