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Balakumar Sundaralingam
2023-10-26 04:17:19 -07:00
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#
# Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
# property and proprietary rights in and to this material, related
# documentation and any modifications thereto. Any use, reproduction,
# disclosure or distribution of this material and related documentation
# without an express license agreement from NVIDIA CORPORATION or
# its affiliates is strictly prohibited.
#
# Standard Library
import time
# Third Party
import torch
# CuRobo
from curobo.geom.types import WorldConfig
from curobo.types.base import TensorDeviceType
from curobo.types.math import Pose
from curobo.types.robot import RobotConfig
from curobo.util_file import get_robot_configs_path, get_world_configs_path, join_path, load_yaml
from curobo.wrap.reacher.ik_solver import IKSolver, IKSolverConfig
torch.backends.cudnn.benchmark = True
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True
def demo_basic_ik():
tensor_args = TensorDeviceType()
config_file = load_yaml(join_path(get_robot_configs_path(), "franka.yml"))
urdf_file = config_file["robot_cfg"]["kinematics"][
"urdf_path"
] # Send global path starting with "/"
base_link = config_file["robot_cfg"]["kinematics"]["base_link"]
ee_link = config_file["robot_cfg"]["kinematics"]["ee_link"]
robot_cfg = RobotConfig.from_basic(urdf_file, base_link, ee_link, tensor_args)
ik_config = IKSolverConfig.load_from_robot_config(
robot_cfg,
None,
rotation_threshold=0.05,
position_threshold=0.005,
num_seeds=20,
self_collision_check=False,
self_collision_opt=False,
tensor_args=tensor_args,
use_cuda_graph=False,
)
ik_solver = IKSolver(ik_config)
# print(kin_state)
for _ in range(10):
q_sample = ik_solver.sample_configs(5000)
kin_state = ik_solver.fk(q_sample)
goal = Pose(kin_state.ee_position, kin_state.ee_quaternion)
st_time = time.time()
result = ik_solver.solve_batch(goal)
torch.cuda.synchronize()
print(
"Success, Solve Time(s), Total Time(s) ",
torch.count_nonzero(result.success).item() / len(q_sample),
result.solve_time,
q_sample.shape[0] / (time.time() - st_time),
torch.mean(result.position_error) * 100.0,
torch.mean(result.rotation_error) * 100.0,
)
def demo_full_config_collision_free_ik():
tensor_args = TensorDeviceType()
world_file = "collision_cage.yml"
robot_file = "franka.yml"
robot_cfg = RobotConfig.from_dict(
load_yaml(join_path(get_robot_configs_path(), robot_file))["robot_cfg"]
)
world_cfg = WorldConfig.from_dict(load_yaml(join_path(get_world_configs_path(), world_file)))
ik_config = IKSolverConfig.load_from_robot_config(
robot_cfg,
world_cfg,
rotation_threshold=0.05,
position_threshold=0.005,
num_seeds=20,
self_collision_check=True,
self_collision_opt=True,
tensor_args=tensor_args,
use_cuda_graph=True,
# use_fixed_samples=True,
)
ik_solver = IKSolver(ik_config)
# print(kin_state)
print("Running Single IK")
for _ in range(10):
q_sample = ik_solver.sample_configs(5000)
kin_state = ik_solver.fk(q_sample)
goal = Pose(kin_state.ee_position, kin_state.ee_quaternion)
st_time = time.time()
result = ik_solver.solve_batch(goal)
torch.cuda.synchronize()
total_time = (time.time() - st_time) / q_sample.shape[0]
print(
"Success, Solve Time(s), Total Time(s)",
torch.count_nonzero(result.success).item() / len(q_sample),
result.solve_time,
total_time,
1.0 / total_time,
torch.mean(result.position_error) * 100.0,
torch.mean(result.rotation_error) * 100.0,
)
exit()
print("Running Batch IK (10 goals)")
q_sample = ik_solver.sample_configs(10)
kin_state = ik_solver.fk(q_sample)
goal = Pose(kin_state.ee_position, kin_state.ee_quaternion)
for _ in range(3):
st_time = time.time()
result = ik_solver.solve_batch(goal)
torch.cuda.synchronize()
print(
"Success, Solve Time(s), Total Time(s)",
torch.count_nonzero(result.success).item() / len(q_sample),
result.solve_time,
time.time() - st_time,
)
print("Running Goalset IK (10 goals in 1 set)")
q_sample = ik_solver.sample_configs(10)
kin_state = ik_solver.fk(q_sample)
goal = Pose(kin_state.ee_position.unsqueeze(0), kin_state.ee_quaternion.unsqueeze(0))
for _ in range(3):
st_time = time.time()
result = ik_solver.solve_goalset(goal)
torch.cuda.synchronize()
print(
"Success, Solve Time(s), Total Time(s)",
torch.count_nonzero(result.success).item() / len(result.success),
result.solve_time,
time.time() - st_time,
)
print("Running Batch Goalset IK (10 goals in 10 sets)")
q_sample = ik_solver.sample_configs(100)
kin_state = ik_solver.fk(q_sample)
goal = Pose(
kin_state.ee_position.view(10, 10, 3).contiguous(),
kin_state.ee_quaternion.view(10, 10, 4).contiguous(),
)
for _ in range(3):
st_time = time.time()
result = ik_solver.solve_batch_goalset(goal)
torch.cuda.synchronize()
print(
"Success, Solve Time(s), Total Time(s)",
torch.count_nonzero(result.success).item() / len(result.success.view(-1)),
result.solve_time,
time.time() - st_time,
)
def demo_full_config_batch_env_collision_free_ik():
tensor_args = TensorDeviceType()
world_file = ["collision_test.yml", "collision_cubby.yml"]
robot_file = "franka.yml"
robot_cfg = RobotConfig.from_dict(
load_yaml(join_path(get_robot_configs_path(), robot_file))["robot_cfg"]
)
world_cfg = [
WorldConfig.from_dict(load_yaml(join_path(get_world_configs_path(), x))) for x in world_file
]
ik_config = IKSolverConfig.load_from_robot_config(
robot_cfg,
world_cfg,
rotation_threshold=0.05,
position_threshold=0.005,
num_seeds=100,
self_collision_check=True,
self_collision_opt=True,
tensor_args=tensor_args,
use_cuda_graph=False,
# use_fixed_samples=True,
)
ik_solver = IKSolver(ik_config)
q_sample = ik_solver.sample_configs(len(world_file))
kin_state = ik_solver.fk(q_sample)
goal = Pose(kin_state.ee_position, kin_state.ee_quaternion)
print("Running Batch Env IK")
for _ in range(3):
st_time = time.time()
result = ik_solver.solve_batch_env(goal)
print(result.success)
torch.cuda.synchronize()
print(
"Success, Solve Time(s), Total Time(s)",
torch.count_nonzero(result.success).item() / len(q_sample),
result.solve_time,
time.time() - st_time,
)
q_sample = ik_solver.sample_configs(10 * len(world_file))
kin_state = ik_solver.fk(q_sample)
goal = Pose(
kin_state.ee_position.view(len(world_file), 10, 3),
kin_state.ee_quaternion.view(len(world_file), 10, 4),
)
print("Running Batch Env Goalset IK")
for _ in range(3):
st_time = time.time()
result = ik_solver.solve_batch_env_goalset(goal)
torch.cuda.synchronize()
print(
"Success, Solve Time(s), Total Time(s)",
torch.count_nonzero(result.success).item() / len(result.success.view(-1)),
result.solve_time,
time.time() - st_time,
)
if __name__ == "__main__":
demo_basic_ik()
# demo_full_config_collision_free_ik()
# demo_full_config_batch_env_collision_free_ik()