235 lines
7.8 KiB
Python
235 lines
7.8 KiB
Python
#
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# Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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#
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# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
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# property and proprietary rights in and to this material, related
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# documentation and any modifications thereto. Any use, reproduction,
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# disclosure or distribution of this material and related documentation
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# without an express license agreement from NVIDIA CORPORATION or
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# its affiliates is strictly prohibited.
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#
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# Standard Library
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import time
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# Third Party
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import torch
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# CuRobo
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from curobo.geom.types import WorldConfig
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from curobo.types.base import TensorDeviceType
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from curobo.types.math import Pose
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from curobo.types.robot import RobotConfig
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from curobo.util_file import get_robot_configs_path, get_world_configs_path, join_path, load_yaml
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from curobo.wrap.reacher.ik_solver import IKSolver, IKSolverConfig
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torch.backends.cudnn.benchmark = True
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torch.backends.cuda.matmul.allow_tf32 = True
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torch.backends.cudnn.allow_tf32 = True
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def demo_basic_ik():
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tensor_args = TensorDeviceType()
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config_file = load_yaml(join_path(get_robot_configs_path(), "ur10e.yml"))
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urdf_file = config_file["robot_cfg"]["kinematics"][
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"urdf_path"
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] # Send global path starting with "/"
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base_link = config_file["robot_cfg"]["kinematics"]["base_link"]
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ee_link = config_file["robot_cfg"]["kinematics"]["ee_link"]
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robot_cfg = RobotConfig.from_basic(urdf_file, base_link, ee_link, tensor_args)
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ik_config = IKSolverConfig.load_from_robot_config(
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robot_cfg,
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None,
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rotation_threshold=0.05,
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position_threshold=0.005,
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num_seeds=20,
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self_collision_check=False,
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self_collision_opt=False,
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tensor_args=tensor_args,
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use_cuda_graph=True,
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)
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ik_solver = IKSolver(ik_config)
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# print(kin_state)
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for _ in range(10):
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q_sample = ik_solver.sample_configs(100)
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kin_state = ik_solver.fk(q_sample)
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goal = Pose(kin_state.ee_position, kin_state.ee_quaternion)
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st_time = time.time()
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result = ik_solver.solve_batch(goal)
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torch.cuda.synchronize()
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print(
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"Success, Solve Time(s), hz ",
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torch.count_nonzero(result.success).item() / len(q_sample),
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result.solve_time,
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q_sample.shape[0] / (time.time() - st_time),
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torch.mean(result.position_error),
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torch.mean(result.rotation_error),
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)
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def demo_full_config_collision_free_ik():
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tensor_args = TensorDeviceType()
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world_file = "collision_cage.yml"
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robot_file = "franka.yml"
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robot_cfg = RobotConfig.from_dict(
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load_yaml(join_path(get_robot_configs_path(), robot_file))["robot_cfg"]
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)
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world_cfg = WorldConfig.from_dict(load_yaml(join_path(get_world_configs_path(), world_file)))
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ik_config = IKSolverConfig.load_from_robot_config(
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robot_cfg,
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world_cfg,
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rotation_threshold=0.05,
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position_threshold=0.005,
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num_seeds=20,
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self_collision_check=True,
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self_collision_opt=True,
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tensor_args=tensor_args,
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use_cuda_graph=True,
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# use_fixed_samples=True,
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)
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ik_solver = IKSolver(ik_config)
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# print(kin_state)
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print("Running Single IK")
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for _ in range(10):
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q_sample = ik_solver.sample_configs(1)
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kin_state = ik_solver.fk(q_sample)
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goal = Pose(kin_state.ee_position, kin_state.ee_quaternion)
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st_time = time.time()
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result = ik_solver.solve_batch(goal)
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torch.cuda.synchronize()
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total_time = (time.time() - st_time) / q_sample.shape[0]
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print(
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"Success, Solve Time(s), Total Time(s)",
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torch.count_nonzero(result.success).item(),
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result.solve_time,
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total_time,
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1.0 / total_time,
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torch.mean(result.position_error) * 100.0,
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torch.mean(result.rotation_error) * 100.0,
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)
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exit()
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print("Running Batch IK (10 goals)")
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q_sample = ik_solver.sample_configs(10)
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kin_state = ik_solver.fk(q_sample)
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goal = Pose(kin_state.ee_position, kin_state.ee_quaternion)
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for _ in range(3):
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st_time = time.time()
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result = ik_solver.solve_batch(goal)
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torch.cuda.synchronize()
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print(
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"Success, Solve Time(s), Total Time(s)",
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torch.count_nonzero(result.success).item() / len(q_sample),
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result.solve_time,
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time.time() - st_time,
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)
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print("Running Goalset IK (10 goals in 1 set)")
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q_sample = ik_solver.sample_configs(10)
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kin_state = ik_solver.fk(q_sample)
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goal = Pose(kin_state.ee_position.unsqueeze(0), kin_state.ee_quaternion.unsqueeze(0))
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for _ in range(3):
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st_time = time.time()
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result = ik_solver.solve_goalset(goal)
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torch.cuda.synchronize()
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print(
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"Success, Solve Time(s), Total Time(s)",
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torch.count_nonzero(result.success).item() / len(result.success),
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result.solve_time,
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time.time() - st_time,
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)
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print("Running Batch Goalset IK (10 goals in 10 sets)")
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q_sample = ik_solver.sample_configs(100)
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kin_state = ik_solver.fk(q_sample)
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goal = Pose(
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kin_state.ee_position.view(10, 10, 3).contiguous(),
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kin_state.ee_quaternion.view(10, 10, 4).contiguous(),
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)
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for _ in range(3):
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st_time = time.time()
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result = ik_solver.solve_batch_goalset(goal)
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torch.cuda.synchronize()
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print(
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"Success, Solve Time(s), Total Time(s)",
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torch.count_nonzero(result.success).item() / len(result.success.view(-1)),
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result.solve_time,
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time.time() - st_time,
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)
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def demo_full_config_batch_env_collision_free_ik():
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tensor_args = TensorDeviceType()
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world_file = ["collision_test.yml", "collision_cubby.yml"]
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robot_file = "franka.yml"
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robot_cfg = RobotConfig.from_dict(
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load_yaml(join_path(get_robot_configs_path(), robot_file))["robot_cfg"]
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)
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world_cfg = [
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WorldConfig.from_dict(load_yaml(join_path(get_world_configs_path(), x))) for x in world_file
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]
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ik_config = IKSolverConfig.load_from_robot_config(
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robot_cfg,
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world_cfg,
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rotation_threshold=0.05,
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position_threshold=0.005,
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num_seeds=100,
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self_collision_check=True,
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self_collision_opt=True,
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tensor_args=tensor_args,
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use_cuda_graph=False,
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# use_fixed_samples=True,
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)
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ik_solver = IKSolver(ik_config)
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q_sample = ik_solver.sample_configs(len(world_file))
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kin_state = ik_solver.fk(q_sample)
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goal = Pose(kin_state.ee_position, kin_state.ee_quaternion)
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print("Running Batch Env IK")
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for _ in range(3):
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st_time = time.time()
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result = ik_solver.solve_batch_env(goal)
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print(result.success)
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torch.cuda.synchronize()
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print(
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"Success, Solve Time(s), Total Time(s)",
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torch.count_nonzero(result.success).item() / len(q_sample),
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result.solve_time,
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time.time() - st_time,
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)
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q_sample = ik_solver.sample_configs(10 * len(world_file))
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kin_state = ik_solver.fk(q_sample)
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goal = Pose(
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kin_state.ee_position.view(len(world_file), 10, 3),
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kin_state.ee_quaternion.view(len(world_file), 10, 4),
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)
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print("Running Batch Env Goalset IK")
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for _ in range(3):
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st_time = time.time()
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result = ik_solver.solve_batch_env_goalset(goal)
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torch.cuda.synchronize()
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print(
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"Success, Solve Time(s), Total Time(s)",
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torch.count_nonzero(result.success).item() / len(result.success.view(-1)),
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result.solve_time,
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time.time() - st_time,
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)
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if __name__ == "__main__":
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demo_basic_ik()
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# demo_full_config_collision_free_ik()
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# demo_full_config_batch_env_collision_free_ik()
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