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66c9c8a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 | # Copyright (c) 2022 NVIDIA CORPORATION. All rights reserved.
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
###########################################################################
# Example Sim Quadruped
#
# Shows how to set up a simulation of a rigid-body quadruped articulation
# from a URDF using the wp.sim.ModelBuilder().
# Note this example does not include a trained policy.
#
###########################################################################
import math
import os
import numpy as np
import warp as wp
import warp.sim
import warp.sim.render
wp.init()
# Taken from env/environment.py
def compute_env_offsets(num_envs, env_offset=(5.0, 0.0, 5.0), up_axis="Y"):
# compute positional offsets per environment
env_offset = np.array(env_offset)
nonzeros = np.nonzero(env_offset)[0]
num_dim = nonzeros.shape[0]
if num_dim > 0:
side_length = int(np.ceil(num_envs ** (1.0 / num_dim)))
env_offsets = []
else:
env_offsets = np.zeros((num_envs, 3))
if num_dim == 1:
for i in range(num_envs):
env_offsets.append(i * env_offset)
elif num_dim == 2:
for i in range(num_envs):
d0 = i // side_length
d1 = i % side_length
offset = np.zeros(3)
offset[nonzeros[0]] = d0 * env_offset[nonzeros[0]]
offset[nonzeros[1]] = d1 * env_offset[nonzeros[1]]
env_offsets.append(offset)
elif num_dim == 3:
for i in range(num_envs):
d0 = i // (side_length * side_length)
d1 = (i // side_length) % side_length
d2 = i % side_length
offset = np.zeros(3)
offset[0] = d0 * env_offset[0]
offset[1] = d1 * env_offset[1]
offset[2] = d2 * env_offset[2]
env_offsets.append(offset)
env_offsets = np.array(env_offsets)
min_offsets = np.min(env_offsets, axis=0)
correction = min_offsets + (np.max(env_offsets, axis=0) - min_offsets) / 2.0
if isinstance(up_axis, str):
up_axis = "XYZ".index(up_axis.upper())
correction[up_axis] = 0.0 # ensure the envs are not shifted below the ground plane
env_offsets -= correction
return env_offsets
class Example:
def __init__(self, stage=None, num_envs=1, enable_rendering=True, print_timers=True):
self.device = wp.get_device()
self.num_envs = num_envs
articulation_builder = wp.sim.ModelBuilder()
wp.sim.parse_urdf(
os.path.join(os.path.dirname(__file__), "assets/quadruped.urdf"),
articulation_builder,
xform=wp.transform([0.0, 0.7, 0.0], wp.quat_from_axis_angle(wp.vec3(1.0, 0.0, 0.0), -math.pi * 0.5)),
floating=True,
density=1000,
armature=0.01,
stiffness=120,
damping=1,
shape_ke=1.0e4,
shape_kd=1.0e2,
shape_kf=1.0e2,
shape_mu=0.0,
limit_ke=1.0e4,
limit_kd=1.0e1,
)
builder = wp.sim.ModelBuilder()
self.sim_time = 0.0
self.frame_dt = 1.0 / 100.0
episode_duration = 5.0 # seconds
self.episode_frames = int(episode_duration / self.frame_dt)
self.sim_substeps = 5
self.sim_dt = self.frame_dt / self.sim_substeps
offsets = compute_env_offsets(num_envs)
for i in range(num_envs):
builder.add_builder(articulation_builder, xform=wp.transform(offsets[i], wp.quat_identity()))
builder.joint_q[-12:] = [0.2, 0.4, -0.6, -0.2, -0.4, 0.6, -0.2, 0.4, -0.6, 0.2, -0.4, 0.6]
builder.joint_target[-12:] = [0.2, 0.4, -0.6, -0.2, -0.4, 0.6, -0.2, 0.4, -0.6, 0.2, -0.4, 0.6]
np.set_printoptions(suppress=True)
# finalize model
self.model = builder.finalize()
self.model.ground = True
self.model.joint_attach_ke = 16000.0
self.model.joint_attach_kd = 200.0
self.integrator = wp.sim.XPBDIntegrator()
self.enable_rendering = enable_rendering
self.renderer = None
if self.enable_rendering:
self.renderer = wp.sim.render.SimRenderer(self.model, stage)
self.print_timers = print_timers
self.state_0 = self.model.state()
self.state_1 = self.model.state()
wp.sim.eval_fk(self.model, self.model.joint_q, self.model.joint_qd, None, self.state_0)
self.use_graph = wp.get_device().is_cuda
self.graph = None
if self.use_graph:
# create update graph
wp.capture_begin(self.device)
try:
self.update()
finally:
self.graph = wp.capture_end(self.device)
def update(self):
with wp.ScopedTimer("simulate", active=True, print=self.print_timers):
if not self.use_graph or self.graph is None:
for _ in range(self.sim_substeps):
self.state_0.clear_forces()
wp.sim.collide(self.model, self.state_0)
self.integrator.simulate(self.model, self.state_0, self.state_1, self.sim_dt)
self.state_0, self.state_1 = self.state_1, self.state_0
else:
wp.capture_launch(self.graph)
if not wp.get_device().is_capturing:
self.sim_time += self.frame_dt
def render(self, is_live=False):
if self.enable_rendering:
with wp.ScopedTimer("render", active=True, print=self.print_timers):
time = 0.0 if is_live else self.sim_time
self.renderer.begin_frame(time)
self.renderer.render(self.state_0)
self.renderer.end_frame()
def run(self):
profiler = {}
with wp.ScopedTimer("simulate", detailed=False, print=False, active=True, dict=profiler):
for _ in range(self.episode_frames):
self.update()
self.render()
wp.synchronize()
if self.enable_rendering:
self.renderer.save()
avg_time = np.array(profiler["simulate"]).mean() / self.episode_frames
avg_steps_second = 1000.0 * float(self.num_envs) / avg_time
print(f"envs: {self.num_envs} steps/second {avg_steps_second} avg_time {avg_time}")
return 1000.0 * float(self.num_envs) / avg_time
if __name__ == "__main__":
profile = False
if profile:
env_count = 2
env_times = []
env_size = []
for i in range(15):
example = Example(num_envs=env_count, enable_rendering=False, print_timers=False)
steps_per_second = example.run()
env_size.append(env_count)
env_times.append(steps_per_second)
env_count *= 2
# dump times
for i in range(len(env_times)):
print(f"envs: {env_size[i]} steps/second: {env_times[i]}")
# plot
import matplotlib.pyplot as plt
plt.figure(1)
plt.plot(env_size, env_times)
plt.xscale("log")
plt.xlabel("Number of Envs")
plt.yscale("log")
plt.ylabel("Steps/Second")
plt.show()
else:
stage = os.path.join(os.path.dirname(__file__), "outputs/example_sim_quadruped.usd")
example = Example(stage, num_envs=25, enable_rendering=True)
example.run()
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