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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 | # 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 Rigid Kinematics
#
# Tests rigid body forward and backwards kinematics through the
# wp.sim.eval_ik() and wp.sim.eval_fk() methods.
#
###########################################################################
import os
import numpy as np
import warp as wp
import warp.sim
import warp.sim.render
wp.init()
TARGET = wp.constant(wp.vec3(2.0, 1.0, 0.0))
@wp.kernel
def compute_loss(body_q: wp.array(dtype=wp.transform), body_index: int, loss: wp.array(dtype=float)):
x = wp.transform_get_translation(body_q[body_index])
delta = x - TARGET
loss[0] = wp.dot(delta, delta)
@wp.kernel
def step_kernel(x: wp.array(dtype=float), grad: wp.array(dtype=float), alpha: float):
tid = wp.tid()
# gradient descent step
x[tid] = x[tid] - grad[tid] * alpha
class Example:
def __init__(self, stage, device=None, verbose=False):
self.verbose = verbose
self.frame_dt = 1.0 / 60.0
self.render_time = 0.0
builder = wp.sim.ModelBuilder()
builder.add_articulation()
chain_length = 4
chain_width = 1.0
for i in range(chain_length):
if i == 0:
parent = -1
parent_joint_xform = wp.transform([0.0, 0.0, 0.0], wp.quat_identity())
else:
parent = builder.joint_count - 1
parent_joint_xform = wp.transform([chain_width, 0.0, 0.0], wp.quat_identity())
# create body
b = builder.add_body(origin=wp.transform([i, 0.0, 0.0], wp.quat_identity()), armature=0.1)
builder.add_joint_revolute(
parent=parent,
child=b,
axis=(0.0, 0.0, 1.0),
parent_xform=parent_joint_xform,
child_xform=wp.transform_identity(),
limit_lower=-np.deg2rad(60.0),
limit_upper=np.deg2rad(60.0),
target_ke=0.0,
target_kd=0.0,
limit_ke=30.0,
limit_kd=30.0,
)
if i == chain_length - 1:
# create end effector
builder.add_shape_sphere(pos=wp.vec3(0.0, 0.0, 0.0), radius=0.1, density=10.0, body=b)
else:
# create shape
builder.add_shape_box(
pos=wp.vec3(chain_width * 0.5, 0.0, 0.0), hx=chain_width * 0.5, hy=0.1, hz=0.1, density=10.0, body=b
)
self.device = wp.get_device(device)
# finalize model
self.model = builder.finalize(self.device)
self.model.ground = False
self.state = self.model.state()
self.renderer = wp.sim.render.SimRenderer(self.model, stage, scaling=50.0)
# optimization variables
self.loss = wp.zeros(1, dtype=float, device=self.device)
self.model.joint_q.requires_grad = True
self.state.body_q.requires_grad = True
self.loss.requires_grad = True
self.train_rate = 0.01
def update(self):
tape = wp.Tape()
with tape:
wp.sim.eval_fk(self.model, self.model.joint_q, self.model.joint_qd, None, self.state)
wp.launch(
compute_loss,
dim=1,
inputs=[self.state.body_q, len(self.state.body_q) - 1, self.loss],
device=self.device,
)
tape.backward(loss=self.loss)
if self.verbose:
print(self.loss)
print(tape.gradients[self.model.joint_q])
# gradient descent
wp.launch(
step_kernel,
dim=len(self.model.joint_q),
inputs=[self.model.joint_q, tape.gradients[self.model.joint_q], self.train_rate],
device=self.device,
)
# zero gradients
tape.zero()
def render(self):
self.renderer.begin_frame(self.render_time)
self.renderer.render(self.state)
self.renderer.render_sphere(name="target", pos=TARGET, rot=wp.quat_identity(), radius=0.1)
self.renderer.end_frame()
self.render_time += self.frame_dt
if __name__ == "__main__":
stage_path = os.path.join(os.path.dirname(__file__), "outputs/example_sim_fk_grad.usd")
example = Example(stage_path, device=wp.get_preferred_device(), verbose=True)
train_iters = 512
for _ in range(train_iters):
example.update()
example.render()
example.renderer.save()
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