mirror of
https://github.com/wassname/Run-Skeleton-Run.git
synced 2026-06-27 20:19:46 +08:00
Kolesnikov Sergey
337 lines
13 KiB
Python
337 lines
13 KiB
Python
from __future__ import division
|
|
import numpy as np
|
|
from collections import OrderedDict
|
|
|
|
|
|
def get_state_names(all=False, obst=False):
|
|
names = ['pelvis_' + n for n in ('rot', 'x', 'y')]
|
|
names += ['pelvis_vel_' + n for n in ('rot', 'x', 'y')]
|
|
names += ['hip_right', 'knee_right', 'ankle_right', 'hip_left', 'knee_left', 'ankle_left']
|
|
names += ['hip_right_vel', 'knee_right_vel', 'ankle_right_vel', 'hip_left_vel', 'knee_left_vel', 'ankle_left_vel']
|
|
names += ['mass_x', 'mass_y']
|
|
names += ['mass_x_vel', 'mass_y_vel']
|
|
|
|
if all:
|
|
names += [b + '_' + i for b in ['head', 'pelvis2', 'torso', 'toes_left',
|
|
'toes_right', 'talus_left', 'talus_right'] for i in
|
|
['x', 'y']]
|
|
else:
|
|
names += [b + '_' + i for b in ['head', 'torso', 'toes_left', 'toes_right',
|
|
'talus_left', 'talus_right'] for i in
|
|
['x', 'y']]
|
|
|
|
names += ['muscle_left', 'muscle_right']
|
|
if obst:
|
|
names += ['obst_dist', 'obst_y', 'obst_r']
|
|
return names
|
|
|
|
|
|
def get_names_to_center(centr):
|
|
if centr == 'pelvis':
|
|
pelvis_or_mass = 'mass'
|
|
elif centr == 'mass':
|
|
pelvis_or_mass = 'pelvis'
|
|
else:
|
|
raise ValueError('centr should be in [mass or pelvis], not {}'.format(centr))
|
|
return [b + '_x' for b in ['head', pelvis_or_mass, 'torso', 'toes_left',
|
|
'toes_right', 'talus_left', 'talus_right']]
|
|
|
|
|
|
def get_bodies_names():
|
|
return [b + '_' + i for b in ['head', 'torso', 'toes_left', 'toes_right', 'talus_left', 'talus_right']
|
|
for i in ['x', 'y']]
|
|
|
|
|
|
def get_names_obstacles():
|
|
return ['toes_left', 'toes_right', 'talus_left', 'talus_right']
|
|
|
|
|
|
def calculate_velocity(cur, prev):
|
|
if prev is None:
|
|
return np.zeros_like(cur)
|
|
return 100.*(cur - prev)
|
|
|
|
|
|
def _get_pattern_idxs(lst, pattern):
|
|
idxs = [i for i, x in enumerate(lst) if pattern in x]
|
|
return idxs
|
|
|
|
|
|
class State(object):
|
|
def __init__(self, obstacles_mode='bodies_dist', obst_grid_dist=1,
|
|
grid_points=100, predict_bodies=True, add_step=True, osb_first=False):
|
|
assert obstacles_mode in ['exclude', 'grid', 'bodies_dist', 'standard']
|
|
|
|
self.state_idxs = [i for i, n in enumerate(get_state_names(True, True)) if n not in ['pelvis2_x', 'pelvis2_y']]
|
|
self.state_names = get_state_names()
|
|
self.step = 0
|
|
self.add_step = add_step
|
|
self.osb_first = osb_first
|
|
self.obstacles_mode = obstacles_mode
|
|
self.obstacles = OrderedDict()
|
|
|
|
self.obst_names = []
|
|
if obstacles_mode == 'standard':
|
|
self.obst_names = ['obst_dist', 'obst_y', 'obst_r']
|
|
elif obstacles_mode == 'grid':
|
|
self.obst_names = ['obst_grid_{}'.format(i) for i in range(grid_points)]
|
|
self.obst_grid_dist = obst_grid_dist
|
|
self.obst_grid_points = grid_points
|
|
self.obst_grid_size = obst_grid_dist * 2 / grid_points
|
|
elif obstacles_mode == 'bodies_dist':
|
|
self._obst_names = get_names_obstacles()
|
|
for i in range(3):
|
|
for n in self._obst_names:
|
|
self.obst_names.append('{}_{}_obst_x_start'.format(n, i))
|
|
self.obst_names.append('{}_{}_obst_x_end'.format(n, i))
|
|
self.obst_names.append('{}_{}_obst_y'.format(n, i))
|
|
self.obst_names.append('is_obstacle')
|
|
|
|
if self.add_step:
|
|
self.state_names.append('step')
|
|
|
|
self.predict_bodies = predict_bodies
|
|
self.bodies_idxs_x = [self.state_names.index(n) for n in get_bodies_names() if n.endswith('_x')]
|
|
self.bodies_idxs_y = [self.state_names.index(n) for n in get_bodies_names() if n.endswith('_y')]
|
|
self.bodies_idxs = self.bodies_idxs_x + self.bodies_idxs_y
|
|
self.mass_x_idx = self.state_names.index('mass_x')
|
|
self.mass_y_idx = self.state_names.index('mass_y')
|
|
|
|
self.state_names_out = self.state_names
|
|
self._set_left_right()
|
|
|
|
def _set_left_right(self):
|
|
self.left_idxs = _get_pattern_idxs(self.state_names, '_left')
|
|
self.right_idxs = _get_pattern_idxs(self.state_names, '_right')
|
|
|
|
def reset(self):
|
|
self.step = 0
|
|
self.prev_orig = None
|
|
self.prev_pred = None
|
|
self.obstacles = OrderedDict()
|
|
|
|
def _predict_bodies(self, state):
|
|
state = np.copy(state)
|
|
|
|
if self.step > 0:
|
|
|
|
def update_bodies(cur, prev_orig, prev_pred, d):
|
|
flt = cur == prev_orig
|
|
cur[flt] = prev_pred[flt] + d
|
|
|
|
# does not matter orig or pred
|
|
dx = state[self.mass_x_idx] - self.prev_orig[self.mass_x_idx]
|
|
dy = state[self.mass_y_idx] - self.prev_orig[self.mass_y_idx]
|
|
|
|
cur_bodies_x = state[self.bodies_idxs_x]
|
|
cur_bodies_y = state[self.bodies_idxs_y]
|
|
|
|
# need for filter
|
|
prev_orig_bodies_x = self.prev_orig[self.bodies_idxs_x]
|
|
prev_orig_bodies_y = self.prev_orig[self.bodies_idxs_y]
|
|
|
|
# need for updating
|
|
prev_pred_bodies_x = self.prev_pred[self.bodies_idxs_x]
|
|
prev_pred_bodies_y = self.prev_pred[self.bodies_idxs_y]
|
|
|
|
update_bodies(cur_bodies_x, prev_orig_bodies_x, prev_pred_bodies_x, dx)
|
|
update_bodies(cur_bodies_y, prev_orig_bodies_y, prev_pred_bodies_y, dy)
|
|
|
|
state[self.bodies_idxs_x] = cur_bodies_x
|
|
state[self.bodies_idxs_y] = cur_bodies_y
|
|
return state
|
|
|
|
def _add_obstacle(self, state):
|
|
pelvis_x = state[1]
|
|
obstacle_x = state[-3]
|
|
|
|
if obstacle_x != 100:
|
|
obstacle_x += pelvis_x
|
|
if round(obstacle_x, 5) not in self.obstacles:
|
|
self.obstacles[round(obstacle_x, 5)] = [obstacle_x, state[-2], state[-1]]
|
|
#print('obstacles {}, step {}'.format(self.obstacles.keys(), self.step))
|
|
if len(self.obstacles) > 3:
|
|
Warning('more than 3 obstacles')
|
|
|
|
def _get_obstacle_state_reward(self, state):
|
|
is_obst = float(state[-3] != 100)
|
|
|
|
if self.obstacles_mode == 'exclude':
|
|
return [is_obst], 0.
|
|
elif self.obstacles_mode == 'standard':
|
|
if not is_obst:
|
|
return [-1., 0., 0., is_obst], 0.
|
|
obst_features = np.clip(state[-3:], -10., 10.)
|
|
return np.append(obst_features, is_obst), 0.
|
|
elif self.obstacles_mode == 'gird':
|
|
mass_x = state[self.state_names.index('mass_x')]
|
|
obst_grid = np.zeros(self.obst_grid_points)
|
|
for k, v in self.obstacles.iteritems():
|
|
obst_x, obst_y, obst_r = v
|
|
obst_h = obst_y + obst_r
|
|
obst_left = int(np.ceil((obst_x - mass_x - obst_r) / self.obst_grid_size) + self.obst_grid_points // 2)
|
|
obst_right = int(np.ceil((obst_x - mass_x + obst_r) / self.obst_grid_size) + self.obst_grid_points // 2)
|
|
obst_left = max(obst_left, 0)
|
|
obst_right = max(obst_right, -1)
|
|
obst_grid[obst_left:obst_right + 1] = obst_h
|
|
obst_features = np.append(obst_grid, is_obst)
|
|
return obst_features, 0
|
|
else:
|
|
obst_state = []
|
|
obst_reward = 0
|
|
for i in range(3):
|
|
if i >= len(self.obstacles):
|
|
for n in self._obst_names:
|
|
body_y = state[self.state_names.index(n + '_y')]
|
|
obst_state.extend([10, 10, body_y])
|
|
else:
|
|
v = self.obstacles.values()[i]
|
|
obst_x, obst_y, obst_r = v
|
|
obst_h = obst_y + obst_r
|
|
obst_x_start = obst_x - obst_r
|
|
obst_x_end = obst_x + obst_r
|
|
for n in self._obst_names:
|
|
body_x = state[self.state_names.index(n + '_x')]
|
|
body_y = state[self.state_names.index(n + '_y')]
|
|
obst_state.append(obst_x_start - body_x)
|
|
obst_state.append(obst_x_end - body_x)
|
|
obst_state.append(body_y - obst_h)
|
|
if obst_reward >= 0 and body_x >= (obst_x_start - obst_r/2) \
|
|
and (body_x <= obst_x_end+obst_r/2) and (obst_h + obst_r/2) >= body_y:
|
|
obst_reward = -0.5
|
|
obst_state.append(is_obst)
|
|
return np.asarray(obst_state), obst_reward
|
|
|
|
def process(self, state):
|
|
state = np.asarray(state)
|
|
state = state[self.state_idxs]
|
|
|
|
if self.osb_first and self.step == 0:
|
|
state[-3:] = [100, 0, 0]
|
|
|
|
self._add_obstacle(state)
|
|
obst_state, obst_reward = self._get_obstacle_state_reward(state)
|
|
state_orig = state[:-3]
|
|
|
|
if self.add_step:
|
|
state_orig = np.append(state_orig, 1. * self.step / 1000)
|
|
|
|
if self.predict_bodies:
|
|
state = self._predict_bodies(state_orig)
|
|
else:
|
|
state = state_orig
|
|
|
|
self.step += 1
|
|
self.prev_orig = state_orig
|
|
self.prev_pred = np.copy(state)
|
|
|
|
return (state, obst_state), obst_reward
|
|
|
|
def flip_state(self, state, copy=True):
|
|
assert np.ndim(state) == 1
|
|
state = np.asarray(state)
|
|
state = self.flip_states(state.reshape(1, -1), copy)
|
|
return state.ravel()
|
|
|
|
def flip_states(self, states, copy=True):
|
|
assert np.ndim(states) == 2
|
|
states = np.asarray(states)
|
|
if copy:
|
|
states = states.copy()
|
|
left = states[:, self.left_idxs]
|
|
right = states[:, self.right_idxs]
|
|
states[:, self.left_idxs] = right
|
|
states[:, self.right_idxs] = left
|
|
return states
|
|
|
|
@property
|
|
def state_size(self):
|
|
return len(self.state_names_out) + len(self.obst_names)
|
|
|
|
|
|
class StateVel(State):
|
|
def __init__(self, vel_states=get_bodies_names(), obstacles_mode='bodies_dist',
|
|
add_step=True, predict_bodies=True, osb_first=False):
|
|
super(StateVel, self).__init__(obstacles_mode=obstacles_mode,
|
|
predict_bodies=predict_bodies,
|
|
add_step=add_step,
|
|
osb_first=osb_first)
|
|
self.vel_idxs = [self.state_names.index(k) for k in vel_states]
|
|
self.prev_vals = None
|
|
self.state_names += [n + '_vel' for n in vel_states]
|
|
self.state_names_out = self.state_names
|
|
# left right idxs
|
|
self._set_left_right()
|
|
|
|
def reset(self):
|
|
super(StateVel, self).reset()
|
|
self.prev_vals = None
|
|
|
|
def process(self, state):
|
|
(state, obst_state), obst_reward = super(StateVel, self).process(state)
|
|
cur_vals = state[self.vel_idxs]
|
|
vel = calculate_velocity(cur_vals, self.prev_vals)
|
|
self.prev_vals = cur_vals
|
|
state = np.concatenate((state, vel, obst_state))
|
|
return state, obst_reward
|
|
|
|
|
|
class StateVelCentr(State):
|
|
def __init__(self, centr_state='pelvis_x', vel_states=get_bodies_names(),
|
|
states_to_center=get_names_to_center('pelvis'),
|
|
vel_before_centr=True, obstacles_mode='bodies_dist',
|
|
exclude_centr=False, predict_bodies=True,
|
|
add_step=True, osb_first=False):
|
|
super(StateVelCentr, self).__init__(obstacles_mode=obstacles_mode,
|
|
predict_bodies=predict_bodies,
|
|
add_step=add_step,
|
|
osb_first=osb_first)
|
|
|
|
# center
|
|
self.centr_idx = self.state_names.index(centr_state)
|
|
self.states_to_center = [self.state_names.index(k) for k in states_to_center]
|
|
# velocities
|
|
self.prev_vals = None
|
|
self.vel_idxs = [self.state_names.index(k) for k in vel_states]
|
|
self.vel_before_centr = vel_before_centr
|
|
self.state_names += [n + '_vel' for n in vel_states]
|
|
self.exclude_centr = exclude_centr
|
|
|
|
if self.exclude_centr:
|
|
self.state_names_out = self.state_names[:max(0, self.centr_idx)] + \
|
|
self.state_names[self.centr_idx + 1:]
|
|
else:
|
|
self.state_names_out = self.state_names
|
|
|
|
# left right idxs
|
|
self._set_left_right()
|
|
|
|
def _set_left_right(self):
|
|
state_names = self.state_names_out
|
|
self.left_idxs = _get_pattern_idxs(state_names, '_left')
|
|
self.right_idxs = _get_pattern_idxs(state_names, '_right')
|
|
|
|
def reset(self):
|
|
super(StateVelCentr, self).reset()
|
|
self.prev_vals = None
|
|
|
|
def process(self, state):
|
|
(state, obst_state), obst_reward = super(StateVelCentr, self).process(state)
|
|
|
|
if self.vel_before_centr:
|
|
cur_vals = state[self.vel_idxs]
|
|
vel = calculate_velocity(cur_vals, self.prev_vals)
|
|
self.prev_vals = cur_vals
|
|
state[self.states_to_center] -= state[self.centr_idx]
|
|
else:
|
|
state[self.states_to_center] -= state[self.centr_idx]
|
|
cur_vals = state[self.vel_idxs]
|
|
vel = calculate_velocity(cur_vals, self.prev_vals)
|
|
self.prev_vals = cur_vals
|
|
|
|
if self.exclude_centr:
|
|
state = np.concatenate([state[:max(0, self.centr_idx)], state[self.centr_idx+1:]])
|
|
|
|
state = np.concatenate((state, vel, obst_state))
|
|
return state, obst_reward
|