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tn_do_task_eval.py
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tn_do_task_eval.py
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#!/usr/bin/env python
from __future__ import division
import pprint
import argparse
import eval_util, sim_util, util
from rapprentice import tps_registration, planning
from rapprentice import registration, colorize, berkeley_pr2, \
animate_traj, ros2rave, plotting_openrave, task_execution, \
tps, func_utils, resampling, ropesim, rope_initialization, clouds
from rapprentice import math_utils as mu
from rapprentice.yes_or_no import yes_or_no
import pdb, time
import trajoptpy, openravepy
from rope_qlearn import get_closing_pts, get_closing_inds
from knot_classifier import isKnot as is_knot, calculateCrossings
import os, os.path, numpy as np, h5py
from numpy import asarray
import atexit
import importlib
from itertools import combinations
import IPython as ipy
import random
import hashlib
from tn_rapprentice import registration as tn_registration
from tn_eval.tps_utils import find_all_normals_naive
from tn_rapprentice.krig_utils import find_rope_normals, find_rope_tangents
from tn_rapprentice import krig_utils as ku
from tn_eval import tps_utils
from tn_rapprentice.registration import tps_rpm_curvature_prior1
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
COLLISION_DIST_THRESHOLD = 0.0
MAX_ACTIONS_TO_TRY = 10 # Number of actions to try (ranked by cost), if TrajOpt trajectory is infeasible
TRAJOPT_MAX_ACTIONS = 5 # Number of actions to compute full feature (TPS + TrajOpt) on
WEIGHTS = np.array([-1])
DS_SIZE = .025
class GlobalVars:
unique_id = 0
actions = None
actions_cache = None
# change to krig?
tps_errors_top10 = []
trajopt_errors_top10 = []
actions_ds_clouds = {}
rope_nodes_crossing_info = {}
def get_action_cloud(sim_env, action, args_eval):
rope_nodes = get_action_rope_nodes(sim_env, action, args_eval)
cloud = ropesim.observe_cloud(rope_nodes, sim_env.sim.rope_params.radius, upsample_rad=args_eval.upsample_rad)
return cloud
def get_action_cloud_ds(sim_env, action, args_eval):
if args_eval.downsample:
if action not in GlobalVars.actions_ds_clouds:
GlobalVars.actions_ds_clouds[action] = clouds.downsample(get_action_cloud(sim_env, action, args_eval), DS_SIZE)
return GlobalVars.actions_ds_clouds[action]
else:
return get_action_cloud(sim_env, action, args_eval)
def get_action_rope_nodes(sim_env, action, args_eval):
rope_nodes = GlobalVars.actions[action]['cloud_xyz'][()]
return ropesim.observe_cloud(rope_nodes, sim_env.sim.rope_params.radius, upsample=args_eval.upsample)
def color_cloud(xyz, endpoint_inds, endpoint_color = np.array([0,0,1]), non_endpoint_color = np.array([1,0,0])):
xyzrgb = np.zeros((len(xyz),6))
xyzrgb[:,:3] = xyz
xyzrgb[endpoint_inds,3:] = np.tile(endpoint_color, (endpoint_inds.sum(), 1))
xyzrgb[~endpoint_inds,3:] = np.tile(non_endpoint_color, ((~endpoint_inds).sum(), 1))
return xyzrgb
def redprint(msg):
print colorize.colorize(msg, "red", bold=True)
def yellowprint(msg):
print colorize.colorize(msg, "yellow", bold=True)
def draw_grid(sim_env, old_xyz, f, color = (1,1,0,1)):
grid_means = .5 * (old_xyz.max(axis=0) + old_xyz.min(axis=0))
grid_mins = grid_means - (old_xyz.max(axis=0) - old_xyz.min(axis=0))
grid_maxs = grid_means + (old_xyz.max(axis=0) - old_xyz.min(axis=0))
return plotting_openrave.draw_grid(sim_env.env, f.transform_points, grid_mins, grid_maxs + np.array([0,0,0]), xres = .1, yres = .1, zres = .04, color = color)
def draw_axis(sim_env, hmat):
handles = []
handles.append(sim_env.env.drawarrow(hmat[:3,3], hmat[:3,3]+hmat[:3,0]/10.0, 0.005, (1,0,0,1)))
handles.append(sim_env.env.drawarrow(hmat[:3,3], hmat[:3,3]+hmat[:3,1]/10.0, 0.005, (0,1,0,1)))
handles.append(sim_env.env.drawarrow(hmat[:3,3], hmat[:3,3]+hmat[:3,2]/10.0, 0.005, (0,0,1,1)))
return handles
def draw_finger_pts_traj(sim_env, flr2finger_pts_traj, color):
handles = []
for finger_lr, pts_traj in flr2finger_pts_traj.items():
for pts in pts_traj:
handles.append(sim_env.env.drawlinestrip(np.r_[pts, pts[0][None,:]], 1, color))
return handles
def register_tps(sim_env, state, action, args_eval, interest_pts = None, closing_hmats = None, closing_finger_pts = None):
old_cloud = get_action_cloud_ds(sim_env, action, args_eval)
new_cloud = state[1]
if args_eval.reg_type == 'segment' or 'segment-normal':
old_rope_nodes = get_action_rope_nodes(sim_env, action, args_eval)
state_id, new_cloud, new_rope_nodes, _ = state
def plot_cb(rope_nodes0, rope_nodes1, cloud0, cloud1, corr_nm, corr_nm_aug, f, pts_segmentation_inds0, pts_segmentation_inds1):
from rapprentice.plotting_plt import plot_tps_registration_segment_proj_2d
import matplotlib.pyplot as plt
plot_tps_registration_segment_proj_2d(rope_nodes0, rope_nodes1, cloud0, cloud1, corr_nm, corr_nm_aug, f, pts_segmentation_inds0, pts_segmentation_inds1)
if closing_hmats is not None:
plt.subplot(223, aspect='equal')
interest_pts_inds = np.zeros(len(cloud0), dtype=bool)
for hmat in closing_hmats.values():
interest_pts_inds += np.apply_along_axis(np.linalg.norm, 1, cloud0 - hmat[:3,3]) < 0.025
cloud1_resampled = corr_nm_aug.dot(cloud1)
plt.scatter(cloud1_resampled[interest_pts_inds,0], cloud1_resampled[interest_pts_inds,1], c='none', edgecolors='b', marker='o', s=15)
warped_cloud0 = f.transform_points(cloud0)
plt.scatter(warped_cloud0[interest_pts_inds,0], warped_cloud0[interest_pts_inds,1], c='none', edgecolors='g', marker='o', s=15)
if interest_pts is not None and len(interest_pts) > 0:
plt.subplot(221, aspect='equal')
pts = np.array(interest_pts)
plt.scatter(pts[:,0], pts[:,1], marker='x')
plt.subplot(222, aspect='equal')
pts = f.transform_points(pts)
plt.scatter(pts[:,0], pts[:,1], marker='x')
if closing_hmats is not None:
plt.subplot(221, aspect='equal')
for hmat in closing_hmats.values():
plt.arrow(hmat[0,3], hmat[1,3], hmat[0,0]/10.0, hmat[1,0]/10.0, fc='r', ec='r')
plt.arrow(hmat[0,3], hmat[1,3], hmat[0,1]/10.0, hmat[1,1]/10.0, fc='g', ec='g')
plt.arrow(hmat[0,3], hmat[1,3], hmat[0,2]/10.0, hmat[1,2]/10.0, fc='b', ec='b')
for i_plot in range(2,5):
plt.subplot(2,2,i_plot, aspect='equal')
for hmat in f.transform_hmats(np.array(closing_hmats.values())):
plt.arrow(hmat[0,3], hmat[1,3], hmat[0,0]/10.0, hmat[1,0]/10.0, fc='r', ec='r')
plt.arrow(hmat[0,3], hmat[1,3], hmat[0,1]/10.0, hmat[1,1]/10.0, fc='g', ec='g')
plt.arrow(hmat[0,3], hmat[1,3], hmat[0,2]/10.0, hmat[1,2]/10.0, fc='b', ec='b')
if closing_finger_pts is not None:
import matplotlib
plt.subplot(221, aspect='equal')
lines = []
for pts_list in closing_finger_pts.values():
for pts in pts_list:
lines.append(np.r_[pts, pts[0][None,:]][:,:2])
# lines.append(pts[np.array([True,False,False,True])][:,:2])
lc = matplotlib.collections.LineCollection(lines, colors=(1,0,0), lw=2)
ax = plt.gca()
ax.add_collection(lc)
lines = []
for pts_list in closing_finger_pts.values():
for i_pt in range(4):
closing_pts = mu.interp2d(np.linspace(0,1,20), np.arange(2), np.r_[pts_list[0][i_pt,:][None,:], pts_list[1][3-i_pt,:][None,:]])
lines.append(closing_pts[:,:2])
lc = matplotlib.collections.LineCollection(lines, colors=(1,0,0), lw=1)
ax = plt.gca()
ax.add_collection(lc)
# closing_pts = []
# for pts_list in closing_finger_pts.values():
# for i_pt in range(4):
# closing_pts.append(mu.interp2d(np.linspace(0,1,10), np.arange(2), np.r_[pts_list[0][i_pt,:][None,:], pts_list[1][3-i_pt,:][None,:]]))
# closing_pts = np.concatenate(np.asarray(closing_pts))
# plt.scatter(closing_pts[:,0], closing_pts[:,1], c=(1,0,0), edgecolors=(1,0,0), marker=',', s=1)
lines = []
for pts_list in closing_finger_pts.values():
for pts in pts_list:
pts = f.transform_points(pts)
lines.append(np.r_[pts, pts[0][None,:]][:,:2])
for i_plot in range(2,5):
plt.subplot(2,2,i_plot, aspect='equal')
lc = matplotlib.collections.LineCollection(lines, colors=(0,1,0), lw=2)
ax = plt.gca()
ax.add_collection(lc)
lines = []
for pts_list in closing_finger_pts.values():
for i_pt in range(4):
closing_pts = mu.interp2d(np.linspace(0,1,20), np.arange(2), np.r_[pts_list[0][i_pt,:][None,:], pts_list[1][3-i_pt,:][None,:]])
closing_pts = f.transform_points(closing_pts)
lines.append(closing_pts[:,:2])
for i_plot in range(2,5):
plt.subplot(2,2,i_plot, aspect='equal')
lc = matplotlib.collections.LineCollection(lines, colors=(0,1,0), lw=1)
ax = plt.gca()
ax.add_collection(lc)
plt.show()
x_weights = np.ones(len(old_cloud)) * 1.0/len(old_cloud)
f, corr = tps_registration.tps_segment_registration(old_rope_nodes, new_rope_nodes,
cloud0 = old_cloud, cloud1 = new_cloud, corr_tile_pattern = np.eye(args_eval.upsample_rad),
reg=np.array([0.00015, 0.00015, 0.0015]), x_weights=x_weights, plotting=False, plot_cb=plot_cb)
if corr is not None:
corr = tps_registration.tile(corr, np.eye(args_eval.upsample_rad))
elif args_eval.reg_type == 'rpm':
vis_cost_xy = tps_registration.ab_cost(old_cloud, new_cloud) if args_eval.use_color else None
f, corr = tps_registration.tps_rpm(old_cloud[:,:3], new_cloud[:,:3], vis_cost_xy=vis_cost_xy, user_data={'old_cloud':old_cloud, 'new_cloud':new_cloud})
elif args_eval.reg_type == 'bij':
vis_cost_xy = tps_registration.ab_cost(old_cloud, new_cloud) if args_eval.use_color else None
x_nd = old_cloud[:,:3]
y_md = new_cloud[:,:3]
scaled_x_nd, src_params = registration.unit_boxify(x_nd)
scaled_y_md, targ_params = registration.unit_boxify(y_md)
x_weights = np.ones(len(old_cloud)) * 1.0/len(old_cloud)
f,g = registration.tps_rpm_bij(scaled_x_nd, scaled_y_md, rot_reg=np.r_[1e-4, 1e-4, 1e-1], n_iter=50, reg_init=10, reg_final=.1, x_weights=x_weights, outlierfrac=1e-2, vis_cost_xy=vis_cost_xy)
corr = f._corr
bend_coef = f._bend_coef
rot_coef = f._rot_coef
wt_n = f._wt_n
f = registration.unscale_tps(f, src_params, targ_params)
f._bend_coef = bend_coef
f._rot_coef = rot_coef
f._wt_n = wt_n
return f, corr
def register_tps_cheap(sim_env, state, action, args_eval):
old_cloud = get_action_cloud_ds(sim_env, action, args_eval)
new_cloud = state[1]
if args_eval.reg_type == 'segment':
old_rope_nodes = get_action_rope_nodes(sim_env, action, args_eval)
old_rope_nodes_hash = hashlib.sha1(old_rope_nodes).hexdigest()
if action not in GlobalVars.rope_nodes_crossing_info:
if action not in GlobalVars.actions_cache:
action_group = GlobalVars.actions_cache.create_group(action)
else:
action_group = GlobalVars.actions_cache[action]
if old_rope_nodes_hash not in action_group:
action_rope_nodes_group = action_group.create_group(old_rope_nodes_hash)
crossings, crossings_links_inds, cross_pairs, rope_closed = calculateCrossings(old_rope_nodes)
action_rope_nodes_group['rope_nodes'] = old_rope_nodes
if crossings: action_rope_nodes_group['crossings'] = crossings
if crossings_links_inds: action_rope_nodes_group['crossings_links_inds'] = crossings_links_inds
if cross_pairs: action_rope_nodes_group['cross_pairs'] = list(cross_pairs)
action_rope_nodes_group['rope_closed'] = rope_closed
else:
action_rope_nodes_group = action_group[old_rope_nodes_hash]
assert np.all(old_rope_nodes == action_rope_nodes_group['rope_nodes'][()])
crossings = action_rope_nodes_group['crossings'][()] if 'crossings' in action_rope_nodes_group else []
crossings_links_inds = action_rope_nodes_group['crossings_links_inds'][()] if 'crossings_links_inds' in action_rope_nodes_group else []
cross_pairs = set([tuple(p) for p in action_rope_nodes_group['cross_pairs']]) if 'cross_pairs' in action_rope_nodes_group else set([])
rope_closed = action_rope_nodes_group['rope_closed'][()]
GlobalVars.rope_nodes_crossing_info[action] = (old_rope_nodes, crossings, crossings_links_inds, cross_pairs, rope_closed)
state_id, new_cloud, new_rope_nodes = state
if state_id not in GlobalVars.rope_nodes_crossing_info:
crossings, crossings_links_inds, cross_pairs, rope_closed = calculateCrossings(new_rope_nodes)
GlobalVars.rope_nodes_crossing_info[state_id] = (new_rope_nodes, crossings, crossings_links_inds, cross_pairs, rope_closed)
def plot_cb(rope_nodes0, rope_nodes1, corr_nm, f, pts_segmentation_inds0, pts_segmentation_inds1):
from rapprentice.plotting_plt import plot_tps_registration_segment_proj_2d
import matplotlib.pyplot as plt
plot_tps_registration_segment_proj_2d(rope_nodes0, rope_nodes1, corr_nm, f, pts_segmentation_inds0, pts_segmentation_inds1)
plt.show()
x_weights = np.ones(len(old_cloud)) * 1.0/len(old_cloud)
f, corr = tps_registration.tps_segment_registration(GlobalVars.rope_nodes_crossing_info[action], GlobalVars.rope_nodes_crossing_info[state_id],
cloud0 = old_cloud, cloud1 = new_cloud, corr_tile_pattern = np.eye(args_eval.upsample_rad),
reg=np.array([0.00015, 0.00015, 0.0015]), x_weights=x_weights, plotting=False, plot_cb=plot_cb)
#ipy.embed()
elif args_eval.reg_type == 'segment-normal':
old_rope_nodes = get_action_rope_nodes(sim_env, action, args_eval)
old_rope_nodes_hash = hashlib.sha1(old_rope_nodes).hexdigest()
if action not in GlobalVars.rope_nodes_crossing_info:
if action not in GlobalVars.actions_cache:
action_group = GlobalVars.actions_cache.create_group(action)
else:
action_group = GlobalVars.actions_cache[action]
if old_rope_nodes_hash not in action_group:
action_rope_nodes_group = action_group.create_group(old_rope_nodes_hash)
crossings, crossings_links_inds, cross_pairs, rope_closed = calculateCrossings(old_rope_nodes)
action_rope_nodes_group['rope_nodes'] = old_rope_nodes
if crossings: action_rope_nodes_group['crossings'] = crossings
if crossings_links_inds: action_rope_nodes_group['crossings_links_inds'] = crossings_links_inds
if cross_pairs: action_rope_nodes_group['cross_pairs'] = list(cross_pairs)
action_rope_nodes_group['rope_closed'] = rope_closed
else:
action_rope_nodes_group = action_group[old_rope_nodes_hash]
assert np.all(old_rope_nodes == action_rope_nodes_group['rope_nodes'][()])
crossings = action_rope_nodes_group['crossings'][()] if 'crossings' in action_rope_nodes_group else []
crossings_links_inds = action_rope_nodes_group['crossings_links_inds'][()] if 'crossings_links_inds' in action_rope_nodes_group else []
cross_pairs = set([tuple(p) for p in action_rope_nodes_group['cross_pairs']]) if 'cross_pairs' in action_rope_nodes_group else set([])
rope_closed = action_rope_nodes_group['rope_closed'][()]
GlobalVars.rope_nodes_crossing_info[action] = (old_rope_nodes, crossings, crossings_links_inds, cross_pairs, rope_closed)
state_id, new_cloud, new_rope_nodes = state
if state_id not in GlobalVars.rope_nodes_crossing_info:
crossings, crossings_links_inds, cross_pairs, rope_closed = calculateCrossings(new_rope_nodes)
GlobalVars.rope_nodes_crossing_info[state_id] = (new_rope_nodes, crossings, crossings_links_inds, cross_pairs, rope_closed)
def plot_cb(rope_nodes0, rope_nodes1, corr_nm, f, pts_segmentation_inds0, pts_segmentation_inds1):
from rapprentice.plotting_plt import plot_tps_registration_segment_proj_2d
import matplotlib.pyplot as plt
plot_tps_registration_segment_proj_2d(rope_nodes0, rope_nodes1, corr_nm, f, pts_segmentation_inds0, pts_segmentation_inds1)
plt.show()
x_weights = np.ones(len(old_cloud)) * 1.0/len(old_cloud)
_, corr = tps_registration.tps_segment_registration(GlobalVars.rope_nodes_crossing_info[action], GlobalVars.rope_nodes_crossing_info[state_id],
cloud0 = old_cloud, cloud1 = new_cloud, corr_tile_pattern = np.eye(args_eval.upsample_rad),
reg=np.array([0.00015, 0.00015, 0.0015]), x_weights=x_weights, plotting=False, plot_cb=plot_cb)
#ipy.embed()
if corr is not None:
seg_info = GlobalVars.actions[action]
closing_inds = get_closing_inds(seg_info)
closing_hmats = {}
for lr in closing_inds:
if closing_inds[lr] != -1:
closing_hmats[lr] = seg_info["%s_gripper_tool_frame"%lr]['hmat'][closing_inds[lr]]
interest_pts_inds = np.zeros(len(old_cloud), dtype=bool)
try:
interest_pts_inds += np.apply_along_axis(np.linalg.norm, 1, old_cloud - closing_hmats['l'][:3,3]) < 0.05
except:
print None #'I need to figure out how to use try without except'
try:
interest_pts_inds += np.apply_along_axis(np.linalg.norm, 1, old_cloud - closing_hmats['r'][:3,3]) < 0.05
except:
print None #'I need to figure out how to use try without except'
#ipy.embed()
x_na = old_cloud
y_ng = corr.dot(new_cloud)
xtangents = find_rope_tangents(x_na)
ytangents = find_rope_tangents(y_ng)
xnormals = xtangents.copy()
ynormals = ytangents.copy()
xnormals[:,0], xnormals[:,1] = -xnormals[:,1], xnormals[:,0]
ynormals[:,0], ynormals[:,1] = -ynormals[:,1], ynormals[:,0]
#Epts = np.r_[x_na, x_na]
#exs = np.r_[xtangents, xnormals]
#eys = np.r_[ytangents, ynormals]
Epts = x_na[interest_pts_inds]
exs = xnormals[interest_pts_inds]
eys = ynormals[interest_pts_inds]
normal_coef = args_eval.normal_coef
f = tn_registration.fit_KrigingSpline(x_na, Epts, exs, y_ng, eys, bend_coef = .1, normal_coef = normal_coef)
else:
f = None
elif args_eval.reg_type == 'rpm':
vis_cost_xy = tps_registration.ab_cost(old_cloud, new_cloud) if args_eval.use_color else None
f, corr = tps_registration.tps_rpm(old_cloud[:,:3], new_cloud[:,:3], n_iter=14, em_iter=1, vis_cost_xy=vis_cost_xy, user_data={'old_cloud':old_cloud, 'new_cloud':new_cloud})
elif args_eval.reg_type == 'bij':
vis_cost_xy = tps_registration.ab_cost(old_cloud, new_cloud) if args_eval.use_color else None
x_nd = old_cloud[:,:3]
y_md = new_cloud[:,:3]
scaled_x_nd, src_params = registration.unit_boxify(x_nd)
scaled_y_md, targ_params = registration.unit_boxify(y_md)
f,g = registration.tps_rpm_bij(scaled_x_nd, scaled_y_md, rot_reg=1e-3, n_iter=10, vis_cost_xy=vis_cost_xy)
corr = f._corr
elif args_eval.reg_type == 'curve-rpm':
EM_iter = 1
beta = 20 #20 works for 90 rotation
wsize = .1
T_init = .04
T_final = .00004
bend_init = 1 # 1e2 works for 90 rotation
bend_final = .001
DS_SIZE = .01
wsize = args_eval.wsize
x_nd = old_cloud[:, :3]
big_old_cloud = GlobalVars.actions[action]['old_cloud_xyz'][()]
y_md = new_cloud[:, :3]
big_new_cloud, endpoint_inds = state[3]
np.set_printoptions(threshold=np.nan)
big_new_cloud_ds = clouds.downsample(big_new_cloud, DS_SIZE)
#ipy.embed()
f , corr = tn_registration.tps_rpm_curvature_prior1(x_nd, y_md, orig_source = big_old_cloud, orig_target = big_new_cloud_ds, n_iter = 14, EM_iter = EM_iter, T_init = T_init, T_final = T_final, bend_init = bend_init, bend_final = bend_final, wsize = wsize, beta = beta)
return f, corr
def compute_trans_traj(sim_env, state_or_get_state_fn, action, i_step, args_eval, transferopt=None, animate=False, interactive=False, simulate=True, replay_full_trajs=None):
alpha = args_eval.alpha
beta_pos = args_eval.beta_pos
beta_rot = args_eval.beta_rot
gamma = args_eval.gamma
windowsize = args_eval.windowsize
normal_coef = args_eval.normal_coef
if transferopt is None:
transferopt = args_eval.transferopt
seg_info = GlobalVars.actions[action]
if simulate:
sim_util.reset_arms_to_side(sim_env)
cloud_dim = 6 if args_eval.use_color else 3
old_cloud = get_action_cloud_ds(sim_env, action, args_eval)[:,:cloud_dim]
big_old_cloud = GlobalVars.actions[action]['old_cloud_xyz'][()]
old_rope_nodes = get_action_rope_nodes(sim_env, action, args_eval)
n, d = old_cloud.shape
closing_inds = get_closing_inds(seg_info)
closing_hmats = {}
for lr in closing_inds:
if closing_inds[lr] != -1:
closing_hmats[lr] = seg_info["%s_gripper_tool_frame"%lr]['hmat'][closing_inds[lr]]
miniseg_intervals = []
for lr in 'lr':
miniseg_intervals.extend([(i_miniseg_lr, lr, i_start, i_end) for (i_miniseg_lr, (i_start, i_end)) in enumerate(zip(*sim_util.split_trajectory_by_lr_gripper(seg_info, lr)))])
# sort by the start of the trajectory, then by the length (if both trajectories start at the same time, the shorter one should go first), and then break ties by executing the right trajectory first
miniseg_intervals = sorted(miniseg_intervals, key=lambda (i_miniseg_lr, lr, i_start, i_end): (i_start, i_end-i_start, {'l':'r', 'r':'l'}[lr]))
miniseg_interval_groups = []
for (curr_miniseg_interval, next_miniseg_interval) in zip(miniseg_intervals[:-1], miniseg_intervals[1:]):
curr_i_miniseg_lr, curr_lr, curr_i_start, curr_i_end = curr_miniseg_interval
next_i_miniseg_lr, next_lr, next_i_start, next_i_end = next_miniseg_interval
if len(miniseg_interval_groups) > 0 and curr_miniseg_interval in miniseg_interval_groups[-1]:
continue
curr_gripper_open = sim_util.binarize_gripper(seg_info["%s_gripper_joint"%curr_lr][curr_i_end])
next_gripper_open = sim_util.binarize_gripper(seg_info["%s_gripper_joint"%next_lr][next_i_end])
miniseg_interval_group = [curr_miniseg_interval]
if not curr_gripper_open and not next_gripper_open and curr_lr != next_lr and curr_i_start < next_i_end and next_i_start < curr_i_end:
miniseg_interval_group.append(next_miniseg_interval)
miniseg_interval_groups.append(miniseg_interval_group)
success = True
feasible = True
misgrasp = False
full_trajs = []
obj_values = []
for i_miniseg_group, miniseg_interval_group in enumerate(miniseg_interval_groups):
if type(state_or_get_state_fn) == tuple:
state = state_or_get_state_fn
else:
state = state_or_get_state_fn(sim_env)
if state is None: break
_, new_cloud, new_rope_nodes, (big_new_cloud, endpoint_inds) = state
new_cloud = new_cloud[:,:cloud_dim]
#big_new_cloud, endpoint_inds = sim_env.sim.raycast_cloud(endpoints=3)
handles = []
if animate:
# color code: r demo, y transformed, g transformed resampled, b new
handles.append(sim_env.env.plot3(old_cloud[:,:3], 2, (1,0,0)))
handles.append(sim_env.env.plot3(new_cloud[:,:3], 2, new_cloud[:,3:] if args_eval.use_color else (0,0,1)))
sim_env.viewer.Step()
if not simulate or replay_full_trajs is None: # we are not simulating, we still want to compute the costs
group_full_trajs = []
for (i_miniseg_lr, lr, i_start, i_end) in miniseg_interval_group:
manip_name = {"l":"leftarm", "r":"rightarm"}[lr]
ee_link_name = "%s_gripper_tool_frame"%lr
################################
redprint("Generating %s arm joint trajectory for part %i"%(lr, i_miniseg_lr))
ipy.embed()
# figure out how we're gonna resample stuff
old_arm_traj = asarray(seg_info[manip_name][i_start - int(i_start > 0):i_end+1])
#ipy.embed()
if not sim_util.arm_moved(old_arm_traj):
continue
old_finger_traj = sim_util.gripper_joint2gripper_l_finger_joint_values(seg_info['%s_gripper_joint'%lr][i_start - int(i_start > 0):i_end+1])[:,None]
JOINT_LENGTH_PER_STEP = .1
_, timesteps_rs = sim_util.unif_resample(old_arm_traj, JOINT_LENGTH_PER_STEP)
### Generate fullbody traj
old_arm_traj_rs = mu.interp2d(timesteps_rs, np.arange(len(old_arm_traj)), old_arm_traj)
old_rope_nodes = get_action_rope_nodes(sim_env, action, args_eval)
x_weights = np.ones(len(old_cloud)) * 1.0/len(old_cloud)
#ipy.embed()
#xtangents = find_rope_tangents(x_na)
#ytangents = find_rope_tangents(y_ng)
if args_eval.use_normals:
_, corr = register_tps_cheap(sim_env, state, action, args_eval)
bend_coef = args_eval.bend_coef
rot_coef = 1e-5
wsize = args_eval.wsize
pld = args_eval.pld
wt_n = x_weights.copy()
if corr is None:
success = True
break
x_na = old_cloud
y_ng = (corr/corr.sum(axis=1)[:,None]).dot(new_cloud)
interest_pts_inds = np.zeros(len(old_cloud), dtype=bool)
radius = 5e-4
while not np.any(interest_pts_inds) and radius < .5:
if lr in closing_hmats:
interest_pts_inds += np.apply_along_axis(np.linalg.norm, 1, old_cloud - closing_hmats[lr][:3,3]) < radius
radius += 5e-4
print radius
if np.any(interest_pts_inds):
assert len(x_na[interest_pts_inds]) == 1
interest_pts_tangents = np.zeros((len(x_na)), dtype=bool)
if lr in closing_hmats:
interest_pts_tangents += np.apply_along_axis(np.linalg.norm, 1, old_cloud - closing_hmats[lr][:3, 3]) < .05
#ipy.embed()
# need to upsample for pld to work on big_old_cloud
#source_normals = find_all_normals_naive(old_cloud[interest_pts_tangents], big_old_cloud, wsize = wsize, project_lower_dim = pld)
source_normals = find_all_normals_naive(old_cloud[interest_pts_tangents][:, :2], big_old_cloud[:,:2], wsize = wsize, project_lower_dim = False)
source_normals = np.c_[source_normals, np.zeros(len(source_normals))]
target_normals = find_all_normals_naive(y_ng[interest_pts_tangents][:, :2], big_new_cloud[:, :2], wsize = wsize, project_lower_dim = False)
target_normals = np.c_[target_normals, np.zeros(len(target_normals))]
wt_n = x_weights.copy()
#### flip_normals might be weird
target_normals = tps_utils.flip_normals(x_na, x_na[interest_pts_tangents], source_normals, y_ng, target_normals, bend_coef = .1)
znormals = np.zeros((x_na[interest_pts_tangents].shape))
znormals[:,2] = np.ones((x_na[interest_pts_tangents].shape[0]))
Epts = np.r_[x_na[interest_pts_tangents], x_na[interest_pts_tangents]]
exs = np.r_[source_normals, znormals]
eys = np.r_[target_normals, znormals]
interest_pts_inds1 = np.r_[interest_pts_inds, np.zeros(2*len(x_na[interest_pts_tangents]), dtype=bool)]
wt_nt = wt_n.copy()
wt_nt[interest_pts_inds] *= 1
wt_nn = np.r_[wt_n, wt_nt[interest_pts_tangents], wt_nt[interest_pts_tangents]]
interest_pts_for_loop = interest_pts_inds[interest_pts_tangents]
#xnormal = xtangents[interest_pts_inds].copy()
#xnormal[:,1], xnormal[:,0] = xnormal[:,0], -xnormal[:,1]
#ynormal = ytangents[interest_pts_inds].copy()
#ynormal[:,1], ynormal[:,0] = ynormal[:,0], -ynormal[:,1]
#exs[interest_pts_inds] = xnormal
#eys[interest_pts_inds] = ynormal
#ipy.embed()
f = tn_registration.fit_KrigingSpline_Interest(x_na, Epts, exs, y_ng, eys, bend_coef = bend_coef, normal_coef = normal_coef, interest_pts_inds = interest_pts_inds1, wt_n = wt_nn)
if np.any(interest_pts_inds):
for _ in range(5):
#ipy.embed()
interest_pts_errs = tps_utils.angle_difference(f.transform_normals(x_na[interest_pts_for_loop,:], exs[interest_pts_for_loop, :]), eys[interest_pts_for_loop,:].T)
print interest_pts_errs
if interest_pts_errs < 30:
break
redprint("TPS fitting: Gripper is approaching from bad angle. Increasing penalty for these weights.")
wt_nt[interest_pts_inds] += .1
wt_nn = np.r_[wt_n, wt_nt[interest_pts_tangents], wt_nt[interest_pts_tangents]]
f = tn_registration.fit_KrigingSpline_Interest(x_na, Epts, exs, y_ng, eys, bend_coef = bend_coef, normal_coef = normal_coef, interest_pts_inds = interest_pts_inds1, wt_n = wt_nn)
else:
f, corr = register_tps(sim_env, state, action, args_eval)
if f is None: break
x_na = old_cloud
y_ng = (corr/corr.sum(axis=1)[:,None]).dot(new_cloud)
bend_coef = f._bend_coef
rot_coef = f._rot_coef
wt_n = f._wt_n.copy()
interest_pts_inds = np.zeros(len(old_cloud), dtype=bool)
if lr in closing_hmats:
interest_pts_inds += np.apply_along_axis(np.linalg.norm, 1, old_cloud - closing_hmats[lr][:3,3]) < 0.05
interest_pts_err_tol = 0.0025
max_iters = 5 if transferopt != "pose" else 0
penalty_factor = 10.0
if np.any(interest_pts_inds):
for _ in range(max_iters):
interest_pts_errs = np.apply_along_axis(np.linalg.norm, 1, (f.transform_points(x_na[interest_pts_inds,:]) - y_ng[interest_pts_inds,:]))
if np.all(interest_pts_errs < interest_pts_err_tol):
break
redprint("TPS fitting: The error of the interest points is above the tolerance. Increasing penalty for these weights.")
wt_n[interest_pts_inds] *= penalty_factor
f = registration.fit_ThinPlateSpline(x_na, y_ng, bend_coef, rot_coef, wt_n)
if animate:
handles.append(sim_env.env.plot3(f.transform_points(old_cloud[:,:3]), 2, old_cloud[:,3:] if args_eval.use_color else (1,1,0)))
new_cloud_rs = corr.dot(new_cloud)
handles.append(sim_env.env.plot3(new_cloud_rs[:,:3], 2, new_cloud_rs[:,3:] if args_eval.use_color else (0,1,0)))
handles.extend(draw_grid(sim_env, old_cloud[:,:3], f))
sim_env.viewer.Step()
#ipy.embed()
old_ee_traj = asarray(seg_info["%s_gripper_tool_frame"%lr]['hmat'][i_start - int(i_start > 0):i_end+1])
transformed_ee_traj = f.transform_hmats(old_ee_traj)
transformed_ee_traj_rs = np.asarray(resampling.interp_hmats(timesteps_rs, np.arange(len(transformed_ee_traj)), transformed_ee_traj))
if animate:
handles.append(sim_env.env.drawlinestrip(old_ee_traj[:,:3,3], 2, (1,0,0)))
handles.append(sim_env.env.drawlinestrip(transformed_ee_traj[:,:3,3], 2, (1,1,0)))
handles.append(sim_env.env.drawlinestrip(transformed_ee_traj_rs[:,:3,3], 2, (0,1,0)))
sim_env.viewer.Step()
print "planning pose trajectory following"
dof_inds = sim_util.dof_inds_from_name(sim_env.robot, manip_name)
joint_ind = sim_env.robot.GetJointIndex("%s_shoulder_lift_joint"%lr)
init_arm_traj = old_arm_traj_rs.copy()
init_arm_traj[:,dof_inds.index(joint_ind)] = sim_env.robot.GetDOFLimits([joint_ind])[0][0]
# might need to change this to krig
new_arm_traj, obj_value, pose_errs = planning.plan_follow_traj(sim_env.robot, manip_name, sim_env.robot.GetLink(ee_link_name), transformed_ee_traj_rs, init_arm_traj,
start_fixed=i_miniseg_lr!=0,
beta_pos=beta_pos, beta_rot=beta_rot)
if transferopt == 'finger' or transferopt == 'joint':
old_ee_traj_rs = np.asarray(resampling.interp_hmats(timesteps_rs, np.arange(len(old_ee_traj)), old_ee_traj))
old_finger_traj_rs = mu.interp2d(timesteps_rs, np.arange(len(old_finger_traj)), old_finger_traj)
flr2old_finger_pts_traj_rs = sim_util.get_finger_pts_traj(sim_env, lr, (old_ee_traj_rs, old_finger_traj_rs))
flr2transformed_finger_pts_traj_rs = {}
flr2finger_link = {}
flr2finger_rel_pts = {}
for finger_lr in 'lr':
flr2transformed_finger_pts_traj_rs[finger_lr] = f.transform_points(np.concatenate(flr2old_finger_pts_traj_rs[finger_lr], axis=0)).reshape((-1,4,3))
flr2finger_link[finger_lr] = sim_env.robot.GetLink("%s_gripper_%s_finger_tip_link"%(lr,finger_lr))
flr2finger_rel_pts[finger_lr] = sim_util.get_finger_rel_pts(finger_lr)
if animate:
handles.extend(draw_finger_pts_traj(sim_env, flr2old_finger_pts_traj_rs, (1,0,0)))
handles.extend(draw_finger_pts_traj(sim_env, flr2transformed_finger_pts_traj_rs, (0,1,0)))
sim_env.viewer.Step()
# enable finger DOF and extend the trajectories to include the closing part only if the gripper closes at the end of this minisegment
next_gripper_open = sim_util.binarize_gripper(seg_info["%s_gripper_joint"%lr][i_end+1]) if i_end+1 < len(seg_info["%s_gripper_joint"%lr]) else True
if not sim_env.sim.is_grabbing_rope(lr) and not next_gripper_open:
manip_name = manip_name + "+" + "%s_gripper_l_finger_joint"%lr
old_finger_closing_traj_start = old_finger_traj_rs[-1][0]
old_finger_closing_traj_target = sim_util.get_binary_gripper_angle(sim_util.binarize_gripper(seg_info["%s_gripper_joint"%lr][i_end+1]))
old_finger_closing_traj_rs = np.linspace(old_finger_closing_traj_start, old_finger_closing_traj_target, np.ceil(abs(old_finger_closing_traj_target - old_finger_closing_traj_start) / .02))[:,None]
closing_n_steps = len(old_finger_closing_traj_rs)
old_ee_closing_traj_rs = np.tile(old_ee_traj_rs[-1], (closing_n_steps,1,1))
flr2old_finger_pts_closing_traj_rs = sim_util.get_finger_pts_traj(sim_env, lr, (old_ee_closing_traj_rs, old_finger_closing_traj_rs))
init_traj = np.r_[np.c_[new_arm_traj, old_finger_traj_rs],
np.c_[np.tile(new_arm_traj[-1], (closing_n_steps,1)), old_finger_closing_traj_rs]]
flr2transformed_finger_pts_closing_traj_rs = {}
for finger_lr in 'lr':
flr2old_finger_pts_traj_rs[finger_lr] = np.r_[flr2old_finger_pts_traj_rs[finger_lr], flr2old_finger_pts_closing_traj_rs[finger_lr]]
flr2transformed_finger_pts_closing_traj_rs[finger_lr] = f.transform_points(np.concatenate(flr2old_finger_pts_closing_traj_rs[finger_lr], axis=0)).reshape((-1,4,3))
flr2transformed_finger_pts_traj_rs[finger_lr] = np.r_[flr2transformed_finger_pts_traj_rs[finger_lr],
flr2transformed_finger_pts_closing_traj_rs[finger_lr]]
if animate:
handles.extend(draw_finger_pts_traj(sim_env, flr2old_finger_pts_closing_traj_rs, (1,0,0)))
handles.extend(draw_finger_pts_traj(sim_env, flr2transformed_finger_pts_closing_traj_rs, (0,1,0)))
#ipy.embed()
#sim_env.viewer.Idle()
sim_env.viewer.Step()
else:
init_traj = new_arm_traj
print "planning finger points trajectory following"
# change to krig
new_traj, obj_value, pose_errs = planning.plan_follow_finger_pts_traj(sim_env.robot, manip_name, flr2finger_link, flr2finger_rel_pts, flr2transformed_finger_pts_traj_rs, init_traj,
start_fixed=i_miniseg_lr!=0,
beta_pos=beta_pos, gamma=gamma)
if transferopt == 'joint':
print "planning joint TPS and finger points trajectory following"
#change to krig
new_traj, f, new_N_z, \
obj_value, rel_pts_costs, tps_cost = planning.joint_fit_tps_follow_finger_pts_traj(sim_env.robot, manip_name, flr2finger_link, flr2finger_rel_pts, flr2old_finger_pts_traj_rs, new_traj,
x_na, y_ng, bend_coef, rot_coef, wt_n, old_N_z=None,
start_fixed=i_miniseg_lr!=0,
alpha=alpha, beta_pos=beta_pos, gamma=gamma)
"""
if np.any(interest_pts_inds):
for _ in range(max_iters):
interest_pts_errs = np.apply_along_axis(np.linalg.norm, 1, (f.transform_points(x_na[interest_pts_inds,:]) - y_ng[interest_pts_inds,:]))
if np.all(interest_pts_errs < interest_pts_err_tol):
break
redprint("Joint TPS fitting: The error of the interest points is above the tolerance. Increasing penalty for these weights.")
wt_n[interest_pts_inds] *= penalty_factor
# change to krig
new_traj, f, new_N_z, \
obj_value, rel_pts_costs, tps_cost = planning.joint_fit_tps_follow_finger_pts_traj(sim_env.robot, manip_name, flr2finger_link, flr2finger_rel_pts, flr2old_finger_pts_traj_rs, new_traj,
x_na, y_ng, bend_coef, rot_coef, wt_n, old_N_z=new_N_z,
start_fixed=i_miniseg_lr!=0,
alpha=alpha, beta_pos=beta_pos, gamma=gamma)
"""
else:
if args_eval.use_normals:
obj_value += alpha * planning.krig_obj(f, x_na, y_ng, exs, eys, Epts, bend_coef, rot_coef, wt_nn)
else:
obj_value += alpha * planning.tps_obj(f, x_na, y_ng, bend_coef, rot_coef, wt_n)
if animate:
flr2new_transformed_finger_pts_traj_rs = {}
for finger_lr in 'lr':
flr2new_transformed_finger_pts_traj_rs[finger_lr] = f.transform_points(np.concatenate(flr2old_finger_pts_traj_rs[finger_lr], axis=0)).reshape((-1,4,3))
handles.extend(draw_finger_pts_traj(sim_env, flr2new_transformed_finger_pts_traj_rs, (0,1,1)))
sim_env.viewer.Step()
else:
new_traj = new_arm_traj
obj_values.append(obj_value)
f._bend_coef = bend_coef
f._rot_coef = rot_coef
f._wt_n = wt_n
full_traj = (new_traj, sim_util.dof_inds_from_name(sim_env.robot, manip_name))
group_full_trajs.append(full_traj)
if animate:
handles.append(sim_env.env.drawlinestrip(sim_util.get_ee_traj(sim_env, lr, full_traj)[:,:3,3], 2, (0,0,1)))
flr2new_finger_pts_traj = sim_util.get_finger_pts_traj(sim_env, lr, full_traj)
handles.extend(draw_finger_pts_traj(sim_env, flr2new_finger_pts_traj, (0,0,1)))
sim_env.viewer.Step()
full_traj = sim_util.merge_full_trajs(group_full_trajs)
else:
full_traj = replay_full_trajs[i_miniseg_group]
full_trajs.append(full_traj)
if not simulate:
if not eval_util.traj_is_safe(sim_env, full_traj, COLLISION_DIST_THRESHOLD, upsample=100):
return np.inf
else:
continue
for (i_miniseg_lr, lr, _, _) in miniseg_interval_group:
redprint("Executing %s arm joint trajectory for part %i"%(lr, i_miniseg_lr))
if len(full_traj[0]) > 0:
if not eval_util.traj_is_safe(sim_env, full_traj, COLLISION_DIST_THRESHOLD, upsample=100):
redprint("Trajectory not feasible")
feasible = False
success = False
else: # Only execute feasible trajectories
first_miniseg = True
for (i_miniseg_lr, _, _, _) in miniseg_interval_group:
first_miniseg &= i_miniseg_lr == 0
if len(full_traj[0]) > 0:
success &= sim_util.sim_full_traj_maybesim(sim_env, full_traj, animate=animate, interactive=interactive, max_cart_vel_trans_traj=.05 if first_miniseg else .02)
#sim_env.viewer.Idle()
if not success: break
for (i_miniseg_lr, lr, i_start, i_end) in miniseg_interval_group:
next_gripper_open = sim_util.binarize_gripper(seg_info["%s_gripper_joint"%lr][i_end+1]) if i_end+1 < len(seg_info["%s_gripper_joint"%lr]) else True
curr_gripper_open = sim_util.binarize_gripper(seg_info["%s_gripper_joint"%lr][i_end])
if not sim_util.set_gripper_maybesim(sim_env, lr, next_gripper_open, curr_gripper_open, animate=animate):
redprint("Grab %s failed" % lr)
misgrasp = True
success = False
#ipy.embed()
if not success: break
if not simulate:
return np.sum(obj_values)
sim_env.sim.settle(animate=animate)
sim_env.sim.release_rope('l')
sim_env.sim.release_rope('r')
sim_util.reset_arms_to_side(sim_env)
if animate:
sim_env.viewer.Step()
return success, feasible, misgrasp, full_trajs
#change to krig
def regcost_feature_fn(sim_env, state, action, args_eval):
# change to krig
f, corr = register_tps_cheap(sim_env, state, action, args_eval)
if f is None:
cost = np.inf
else:
# change to krig
if args_eval.reg_type == 'segment-normal':
#ipy.embed()
seg_info = GlobalVars.actions[action]
closing_inds = get_closing_inds(seg_info)
closing_hmats = {}
for lr in closing_inds:
if closing_inds[lr] != -1:
closing_hmats[lr] = seg_info["%s_gripper_tool_frame"%lr]['hmat'][closing_inds[lr]]
old_cloud = get_action_cloud_ds(sim_env, action, args_eval)
new_cloud = state[1]
interest_pts_inds = np.zeros(len(old_cloud), dtype=bool)
try:
interest_pts_inds += np.apply_along_axis(np.linalg.norm, 1, old_cloud - closing_hmats['l'][:3,3]) < 0.05
except:
print None #'I need to figure out how to use try without except'
try:
interest_pts_inds += np.apply_along_axis(np.linalg.norm, 1, old_cloud - closing_hmats['r'][:3,3]) < 0.05
except:
print None #'I need to figure out how to use try without except'
x_na = old_cloud
y_ng = corr.dot(new_cloud)
xtangents = find_rope_tangents(x_na)
ytangents = find_rope_tangents(y_ng)
xnormals = xtangents.copy()
ynormals = ytangents.copy()
xnormals[:,0], xnormals[:,1] = -xnormals[:,1], xnormals[:,0]
ynormals[:,0], ynormals[:,1] = -ynormals[:,1], ynormals[:,0]
#Epts = np.r_[x_na, x_na]
#exs = np.r_[xtangents, xnormals]
#eys = np.r_[ytangents, ynormals]
Epts = x_na[interest_pts_inds]
exs = xnormals[interest_pts_inds]
eys = ynormals[interest_pts_inds]
normal_coef = args_eval.normal_coef
weights = np.ones(((len(x_na)+len(exs)), 1))
weights[len(x_na):] *= 1
cost = planning.krig_obj(f, x_na, y_ng, exs, eys, Epts, 5, 1e-5, weights)
else:
cost = registration.tps_reg_cost(f)
return np.array([float(cost)]) # no need to normalize since bending cost is independent of number of points
def regcost_trajopt_feature_fn(sim_env, state, action, args_eval):
obj_values_sum = compute_trans_traj(sim_env, state, action, None, args_eval, simulate=False, transferopt='finger')
return np.array([obj_values_sum])
def jointopt_feature_fn(sim_env, state, action, args_eval):
obj_values_sum = compute_trans_traj(sim_env, state, action, None, args_eval, simulate=False, transferopt='joint')
return np.array([obj_values_sum])
def q_value_fn(state, action, sim_env, fn):
return np.dot(WEIGHTS, fn(sim_env, state, action)) #+ w0
def select_feature_fn(warping_cost, args_eval):
if warping_cost == "regcost":
feature_fn = lambda sim_env, state, action: regcost_feature_fn(sim_env, state, action, args_eval)
elif warping_cost == "regcost-trajopt":
feature_fn = lambda sim_env, state, action: regcost_trajopt_feature_fn(sim_env, state, action, args_eval)
elif warping_cost == "jointopt":
feature_fn = lambda sim_env, state, action: jointopt_feature_fn(sim_env, state, action, args_eval)
else:
raise RuntimeError("Invalid warping cost")
return feature_fn
def get_unique_id():
GlobalVars.unique_id += 1
return GlobalVars.unique_id - 1
def get_state(sim_env, args_eval):
if args_eval.raycast:
new_cloud_rc, endpoint_inds = sim_env.sim.raycast_cloud(endpoints=3)
if new_cloud_rc.shape[0] == 0: # rope is not visible (probably because it fall off the table)
return None
new_cloud = sim_env.sim.observe_cloud(upsample=args_eval.upsample, upsample_rad=args_eval.upsample_rad)
else:
new_cloud = sim_env.sim.observe_cloud(upsample=args_eval.upsample, upsample_rad=args_eval.upsample_rad)
endpoint_inds = np.zeros(len(new_cloud), dtype=bool) # for now, args_eval.raycast=False is not compatible with args_eval.use_color=True
new_cloud_rc, endpoint_inds = None, None
if args_eval.use_color:
new_cloud = color_cloud(new_cloud, endpoint_inds)
new_cloud_ds = clouds.downsample(new_cloud, DS_SIZE) if args_eval.downsample else new_cloud
new_rope_nodes = sim_env.sim.rope.GetControlPoints()
new_rope_nodes= ropesim.observe_cloud(new_rope_nodes, sim_env.sim.rope_params.radius, upsample=args_eval.upsample)
state = ("eval_%i"%get_unique_id(), new_cloud_ds, new_rope_nodes, (new_cloud_rc, endpoint_inds))
return state
def select_best_action(sim_env, state, num_actions_to_try, feature_fn, prune_feature_fn, eval_stats, warpingcost):
print "Choosing an action"
num_top_actions = max(num_actions_to_try, TRAJOPT_MAX_ACTIONS)
#ipy.embed()
start_time = time.time()
q_values_prune = [(q_value_fn(state, action, sim_env, prune_feature_fn), action) for action in GlobalVars.actions]
agenda_top_actions = sorted(q_values_prune, key = lambda v: -v[0])[:num_top_actions]
if feature_fn == prune_feature_fn:
q_values = q_values_prune
agenda = agenda_top_actions
else:
if len(agenda_top_actions) > TRAJOPT_MAX_ACTIONS:
agenda_top_actions = agenda_top_actions[:TRAJOPT_MAX_ACTIONS]
q_values = [(q_value_fn(state, a, sim_env, feature_fn), a) for (v, a) in agenda_top_actions]
agenda = sorted(q_values, key = lambda v: -v[0])
eval_stats.action_elapsed_time += time.time() - start_time
q_values_root = [q for (q, a) in q_values]
return agenda, q_values_root
def eval_on_holdout(args, sim_env):
feature_fn = select_feature_fn(args.eval.warpingcost, args.eval)
if args.eval.warpingcost == 'regcost':
prune_feature_fn = feature_fn # so that we can check for function equality
else:
prune_feature_fn = select_feature_fn('regcost', args.eval)
holdoutfile = h5py.File(args.eval.holdoutfile, 'r')
holdout_items = eval_util.get_holdout_items(holdoutfile, args.tasks, args.taskfile, args.i_start, args.i_end)
#ipy.embed()
num_successes = 0
num_total = 0
failures =[]
for i_task, demo_id_rope_nodes in holdout_items:
redprint("task %s" % i_task)
sim_util.reset_arms_to_side(sim_env)
init_rope_nodes = demo_id_rope_nodes["rope_nodes"][:]
# don't call replace_rope and sim.settle() directly. use time machine interface for deterministic results!
time_machine = sim_util.RopeSimTimeMachine(init_rope_nodes, sim_env)
if args.animation:
sim_env.viewer.Step()
eval_util.save_task_results_init(args.resultfile, i_task, sim_env, init_rope_nodes)
for i_step in range(args.eval.num_steps):
redprint("task %s step %i" % (i_task, i_step))
sim_util.reset_arms_to_side(sim_env)
get_state_fn = lambda sim_env: get_state(sim_env, args.eval)
state = get_state_fn(sim_env)
_, new_cloud_ds, new_rope_nodes, _ = get_state_fn(sim_env)
num_actions_to_try = MAX_ACTIONS_TO_TRY if args.eval.search_until_feasible else 1
eval_stats = eval_util.EvalStats()
try:
agenda, q_values_root = select_best_action(sim_env, state, num_actions_to_try, feature_fn, prune_feature_fn, eval_stats, args.eval.warpingcost)
except:
print 'something weird with segment'
time_machine.set_checkpoint('prechoice_%i'%i_step, sim_env)
for i_choice in range(num_actions_to_try):
if agenda[i_choice][0] == -np.inf: # none of the demonstrations generalize
break
redprint("TRYING %s"%agenda[i_choice][1])
#time_machine.restore_from_checkpoint('prechoice_%i'%i_step, sim_env, sim_util.get_rope_params(args.eval.rope_params))
best_root_action = agenda[i_choice][1]
start_time = time.time()
pre_trans, pre_rots = sim_util.get_rope_transforms(sim_env)
#important ?
#try:
#ipy.embed()
eval_stats.success, eval_stats.feasible, eval_stats.misgrasp, full_trajs = compute_trans_traj(sim_env, get_state_fn, best_root_action, i_step, args.eval, animate=args.animation, interactive=args.interactive)
trans, rots = sim_util.get_rope_transforms(sim_env)
eval_stats.exec_elapsed_time += time.time() - start_time
if eval_stats.feasible: # try next action if TrajOpt cannot find feasible action
eval_stats.found_feasible_action = True
break
else:
redprint('TRYING NEXT ACTION')
#except:
redprint('something wrong in compute trans traj')
if not eval_stats.feasible: # If not feasible, restore_from_checkpoint
time_machine.restore_from_checkpoint('prechoice_%i'%i_step, sim_env, sim_util.get_rope_params(args.eval.rope_params))
print "BEST ACTION:", best_root_action
eval_util.save_task_results_step(args.resultfile, i_task, i_step, sim_env, best_root_action, q_values_root, full_trajs, eval_stats, new_cloud_ds=new_cloud_ds, new_rope_nodes=new_rope_nodes)
if not eval_stats.found_feasible_action:
# Skip to next knot tie if the action is infeasible -- since
# that means all future steps (up to 5) will have infeasible trajectories
break
if is_knot(sim_env.sim.rope.GetControlPoints()):
break;
if is_knot(sim_env.sim.rope.GetControlPoints()):
num_successes += 1
else:
failures.append(i_task)
num_total += 1
redprint('Eval Successes / Total: ' + str(num_successes) + '/' + str(num_total))
print failures
def replay_on_holdout(args, sim_env):
holdoutfile = h5py.File(args.eval.holdoutfile, 'r')
loadresultfile = h5py.File(args.replay.loadresultfile, 'r')
loadresult_items = eval_util.get_holdout_items(loadresultfile, args.tasks, args.taskfile, args.i_start, args.i_end)