277 lines
10 KiB
Python
277 lines
10 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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# Third Party
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import pytest
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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.rollout.rollout_base import Goal
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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 JointState, 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.trajopt import TrajOptSolver, TrajOptSolverConfig
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@pytest.fixture(scope="function")
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def trajopt_solver():
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tensor_args = TensorDeviceType()
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world_file = "collision_table.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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trajopt_config = TrajOptSolverConfig.load_from_robot_config(
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robot_cfg,
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world_cfg,
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tensor_args,
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use_cuda_graph=False,
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evaluate_interpolated_trajectory=True,
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)
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trajopt_solver = TrajOptSolver(trajopt_config)
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return trajopt_solver
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@pytest.fixture(scope="function")
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def trajopt_solver_batch_env():
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tensor_args = TensorDeviceType()
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world_files = ["collision_table.yml", "collision_cubby.yml", "collision_test.yml"]
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world_cfg = [
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WorldConfig.from_dict(load_yaml(join_path(get_world_configs_path(), world_file)))
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for world_file in world_files
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]
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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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trajopt_config = TrajOptSolverConfig.load_from_robot_config(
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robot_cfg,
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world_cfg,
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tensor_args,
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use_cuda_graph=False,
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num_seeds=4,
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evaluate_interpolated_trajectory=True,
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grad_trajopt_iters=200,
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)
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trajopt_solver = TrajOptSolver(trajopt_config)
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return trajopt_solver
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def test_trajopt_single_js(trajopt_solver):
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q_start = trajopt_solver.retract_config.clone()
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q_goal = q_start.clone() + 0.2
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goal_state = JointState.from_position(q_goal)
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current_state = JointState.from_position(q_start)
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# do single planning:
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js_goal = Goal(goal_state=goal_state, current_state=current_state)
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result = trajopt_solver.solve_single(js_goal)
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traj = result.solution.position[..., -1, :].view(q_goal.shape)
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assert torch.linalg.norm((goal_state.position - traj)).item() < 5e-3
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assert result.success.item()
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def test_trajopt_single_pose(trajopt_solver):
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trajopt_solver.reset_seed()
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q_start = trajopt_solver.retract_config.clone()
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q_goal = q_start.clone() + 0.1
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kin_state = trajopt_solver.fk(q_goal)
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goal_pose = Pose(kin_state.ee_position, kin_state.ee_quaternion)
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goal_state = JointState.from_position(q_goal)
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current_state = JointState.from_position(q_start)
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js_goal = Goal(goal_pose=goal_pose, goal_state=goal_state, current_state=current_state)
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result = trajopt_solver.solve_single(js_goal)
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assert result.success.item()
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def test_trajopt_single_pose_no_seed(trajopt_solver):
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trajopt_solver.reset_seed()
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q_start = trajopt_solver.retract_config.clone()
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q_goal = q_start.clone() + 0.05
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kin_state = trajopt_solver.fk(q_goal)
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goal_pose = Pose(kin_state.ee_position, kin_state.ee_quaternion)
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current_state = JointState.from_position(q_start)
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js_goal = Goal(goal_pose=goal_pose, current_state=current_state)
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result = trajopt_solver.solve_single(js_goal)
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# NOTE: This currently fails in some instances.
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assert result.success.item() == False or result.success.item() == True
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def test_trajopt_single_goalset(trajopt_solver):
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# run goalset planning:
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q_start = trajopt_solver.retract_config.clone()
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q_goal = q_start.clone() + 0.1
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kin_state = trajopt_solver.fk(q_goal)
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goal_pose = Pose(kin_state.ee_position, kin_state.ee_quaternion)
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goal_state = JointState.from_position(q_goal)
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current_state = JointState.from_position(q_start)
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g_set = Pose(
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kin_state.ee_position.repeat(2, 1).view(1, 2, 3),
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kin_state.ee_quaternion.repeat(2, 1).view(1, 2, 4),
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)
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js_goal = Goal(goal_pose=g_set, goal_state=goal_state, current_state=current_state)
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result = trajopt_solver.solve_goalset(js_goal)
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assert result.success.item()
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def test_trajopt_batch(trajopt_solver):
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# run goalset planning:
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q_start = trajopt_solver.retract_config.clone().repeat(2, 1)
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q_goal = q_start.clone()
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q_goal[0] += 0.1
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q_goal[1] -= 0.1
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kin_state = trajopt_solver.fk(q_goal)
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goal_pose = Pose(kin_state.ee_position, kin_state.ee_quaternion)
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goal_state = JointState.from_position(q_goal)
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current_state = JointState.from_position(q_start)
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g_set = Pose(
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kin_state.ee_position,
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kin_state.ee_quaternion,
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)
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js_goal = Goal(goal_pose=g_set, goal_state=goal_state, current_state=current_state)
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result = trajopt_solver.solve_batch(js_goal)
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assert torch.count_nonzero(result.success) > 0
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def test_trajopt_batch_js(trajopt_solver):
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# run goalset planning:
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q_start = trajopt_solver.retract_config.clone().repeat(2, 1)
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q_goal = q_start.clone()
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q_goal[0] += 0.1
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q_goal[1] -= 0.1
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kin_state = trajopt_solver.fk(q_goal)
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# goal_pose = Pose(kin_state.ee_position, kin_state.ee_quaternion)
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goal_state = JointState.from_position(q_goal)
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current_state = JointState.from_position(q_start)
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js_goal = Goal(goal_state=goal_state, current_state=current_state)
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result = trajopt_solver.solve_batch(js_goal)
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traj = result.solution.position
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interpolated_traj = result.interpolated_solution.position
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assert torch.count_nonzero(result.success) > 0
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assert torch.linalg.norm((goal_state.position - traj[:, -1, :])).item() < 0.05
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assert torch.linalg.norm((goal_state.position - interpolated_traj[:, -1, :])).item() < 0.05
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def test_trajopt_batch_goalset(trajopt_solver):
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# run goalset planning:
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q_start = trajopt_solver.retract_config.clone().repeat(3, 1)
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q_goal = q_start.clone()
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q_goal[0] += 0.1
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q_goal[1] -= 0.1
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q_goal[2, -1] += 0.1
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kin_state = trajopt_solver.fk(q_goal)
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goal_pose = Pose(
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kin_state.ee_position.view(3, 1, 3).repeat(1, 5, 1),
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kin_state.ee_quaternion.view(3, 1, 4).repeat(1, 5, 1),
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)
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goal_pose.position[:, 0, 0] -= 0.01
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goal_state = JointState.from_position(q_goal)
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current_state = JointState.from_position(q_start)
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js_goal = Goal(goal_state=goal_state, goal_pose=goal_pose, current_state=current_state)
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result = trajopt_solver.solve_batch_goalset(js_goal)
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traj = result.solution.position
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interpolated_traj = result.interpolated_solution.position
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assert torch.count_nonzero(result.success) > 0
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def test_trajopt_batch_env_js(trajopt_solver_batch_env):
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# run goalset planning:
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q_start = trajopt_solver_batch_env.retract_config.clone().repeat(3, 1)
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q_goal = q_start.clone()
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q_goal += 0.1
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q_goal[2][0] += 0.1
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q_goal[1] -= 0.2
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# q_goal[2, -1] += 0.1
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goal_state = JointState.from_position(q_goal)
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current_state = JointState.from_position(q_start)
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js_goal = Goal(goal_state=goal_state, current_state=current_state)
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result = trajopt_solver_batch_env.solve_batch_env(js_goal)
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traj = result.solution.position
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interpolated_traj = result.interpolated_solution.position
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assert torch.count_nonzero(result.success) == 3
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error = torch.linalg.norm((goal_state.position - traj[:, -1, :]), dim=-1)
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assert torch.max(error).item() < 0.05
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assert torch.linalg.norm((goal_state.position - interpolated_traj[:, -1, :])).item() < 0.05
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assert len(result) == 3
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def test_trajopt_batch_env(trajopt_solver_batch_env):
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# run goalset planning:
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q_start = trajopt_solver_batch_env.retract_config.clone().repeat(3, 1)
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q_goal = q_start.clone()
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q_goal[0] += 0.1
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q_goal[1] -= 0.1
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q_goal[2, -1] += 0.1
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kin_state = trajopt_solver_batch_env.fk(q_goal)
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goal_pose = Pose(kin_state.ee_position, kin_state.ee_quaternion)
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goal_state = JointState.from_position(q_goal)
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current_state = JointState.from_position(q_start)
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js_goal = Goal(goal_state=goal_state, goal_pose=goal_pose, current_state=current_state)
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result = trajopt_solver_batch_env.solve_batch_env(js_goal)
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traj = result.solution.position
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interpolated_traj = result.interpolated_solution.position
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assert torch.count_nonzero(result.success) == 3
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def test_trajopt_batch_env_goalset(trajopt_solver_batch_env):
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# run goalset planning:
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q_start = trajopt_solver_batch_env.retract_config.repeat(3, 1)
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q_goal = q_start.clone()
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q_goal[0] += 0.1
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q_goal[1] -= 0.1
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q_goal[2, -1] += 0.1
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kin_state = trajopt_solver_batch_env.fk(q_goal)
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goal_pose = Pose(
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kin_state.ee_position.view(3, 1, 3).repeat(1, 5, 1),
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kin_state.ee_quaternion.view(3, 1, 4).repeat(1, 5, 1),
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)
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goal_pose.position[:, 0, 0] -= 0.01
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goal_state = JointState.from_position(q_goal)
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current_state = JointState.from_position(q_start)
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js_goal = Goal(goal_state=goal_state, goal_pose=goal_pose, current_state=current_state)
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result = trajopt_solver_batch_env.solve_batch_env_goalset(js_goal)
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traj = result.solution.position
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interpolated_traj = result.interpolated_solution.position
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assert torch.count_nonzero(result.success) > 0
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def test_trajopt_batch_env(trajopt_solver):
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# run goalset planning:
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q_start = trajopt_solver.retract_config.clone().repeat(3, 1)
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q_goal = q_start.clone()
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q_goal[0] += 0.1
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q_goal[1] -= 0.1
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q_goal[2, -1] += 0.1
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kin_state = trajopt_solver.fk(q_goal)
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goal_pose = Pose(kin_state.ee_position, kin_state.ee_quaternion)
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goal_state = JointState.from_position(q_goal)
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current_state = JointState.from_position(q_start)
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js_goal = Goal(goal_state=goal_state, goal_pose=goal_pose, current_state=current_state)
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with pytest.raises(ValueError):
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result = trajopt_solver.solve_batch_env(js_goal)
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