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# All rights reserved.
#
# SPDX-License-Identifier: Apache-2.0
# Copyright (c) 2024-2025, The Isaac Lab Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import random
from typing import Any
import pytest
SEED: int = 42
random.seed(SEED)
from isaaclab.app import AppLauncher
headless = True
app_launcher = AppLauncher(headless=headless)
simulation_app: Any = app_launcher.app
from collections.abc import Generator
import gymnasium as gym
import torch
import isaaclab.utils.math as math_utils
from isaaclab.assets import Articulation, RigidObject
from isaaclab.envs.manager_based_env import ManagerBasedEnv
from isaaclab.markers import FRAME_MARKER_CFG, VisualizationMarkers
from isaaclab_mimic.envs.franka_stack_ik_rel_mimic_env_cfg import FrankaCubeStackIKRelMimicEnvCfg
from isaaclab_mimic.motion_planners.curobo.curobo_planner import CuroboPlanner
from isaaclab_mimic.motion_planners.curobo.curobo_planner_cfg import CuroboPlannerCfg
GRIPPER_OPEN_CMD: float = 1.0
GRIPPER_CLOSE_CMD: float = -1.0
def _eef_name(env: ManagerBasedEnv) -> str:
return list(env.cfg.subtask_configs.keys())[0]
def _action_from_pose(
env: ManagerBasedEnv, target_pose: torch.Tensor, gripper_binary_action: float, env_id: int = 0
) -> torch.Tensor:
eef = _eef_name(env)
play_action = env.target_eef_pose_to_action(
target_eef_pose_dict={eef: target_pose},
gripper_action_dict={eef: torch.tensor([gripper_binary_action], device=env.device, dtype=torch.float32)},
env_id=env_id,
)
if play_action.dim() == 1:
play_action = play_action.unsqueeze(0)
return play_action
def _env_step_with_action(env: ManagerBasedEnv, action: torch.Tensor) -> None:
env.step(action)
def _execute_plan(env: ManagerBasedEnv, planner: CuroboPlanner, gripper_binary_action: float, env_id: int = 0) -> None:
"""Execute planner's EEF planned poses using env.step with IK-relative controller actions."""
planned_poses = planner.get_planned_poses()
if not planned_poses:
return
for pose in planned_poses:
action = _action_from_pose(env, pose, gripper_binary_action, env_id=env_id)
_env_step_with_action(env, action)
def _execute_gripper_action(
env: ManagerBasedEnv, robot: Articulation, gripper_binary_action: float, steps: int = 12, env_id: int = 0
) -> None:
"""Hold current EEF pose and toggle gripper for a few steps."""
eef = _eef_name(env)
curr_pose = env.get_robot_eef_pose(eef_name=eef, env_ids=[env_id])[0]
for _ in range(steps):
action = _action_from_pose(env, curr_pose, gripper_binary_action, env_id=env_id)
_env_step_with_action(env, action)
DOWN_FACING_QUAT = torch.tensor([0.0, 1.0, 0.0, 0.0], dtype=torch.float32)
@pytest.fixture(scope="class")
def cube_stack_test_env() -> Generator[dict[str, Any], None, None]:
"""Create the environment and motion planner once for the test suite and yield them."""
random.seed(SEED)
torch.manual_seed(SEED)
env_cfg = FrankaCubeStackIKRelMimicEnvCfg()
env_cfg.scene.num_envs = 1
for frame in env_cfg.scene.ee_frame.target_frames:
if frame.name == "end_effector":
print(f"Setting end effector offset from {frame.offset.pos} to (0.0, 0.0, 0.0) for SkillGen parity")
frame.offset.pos = (0.0, 0.0, 0.0)
env: ManagerBasedEnv = gym.make(
"Isaac-Stack-Cube-Franka-IK-Rel-Mimic-v0",
cfg=env_cfg,
headless=headless,
).unwrapped
env.reset()
robot: Articulation = env.scene["robot"]
planner_cfg = CuroboPlannerCfg.franka_stack_cube_config()
planner_cfg.visualize_plan = False
planner_cfg.visualize_spheres = False
planner_cfg.debug_planner = True
planner_cfg.retreat_distance = 0.05
planner_cfg.approach_distance = 0.05
planner_cfg.time_dilation_factor = 1.0
planner = CuroboPlanner(
env=env,
robot=robot,
config=planner_cfg,
env_id=0,
)
goal_pose_visualizer = None
if not headless:
marker_cfg = FRAME_MARKER_CFG.replace(prim_path="/World/Visuals/goal_pose")
marker_cfg.markers["frame"].scale = (0.1, 0.1, 0.1)
goal_pose_visualizer = VisualizationMarkers(marker_cfg)
yield {
"env": env,
"robot": robot,
"planner": planner,
"goal_pose_visualizer": goal_pose_visualizer,
}
env.close()
class TestCubeStackPlanner:
@pytest.fixture(autouse=True)
def setup(self, cube_stack_test_env) -> None:
self.env: ManagerBasedEnv = cube_stack_test_env["env"]
self.robot: Articulation = cube_stack_test_env["robot"]
self.planner: CuroboPlanner = cube_stack_test_env["planner"]
self.goal_pose_visualizer: VisualizationMarkers | None = cube_stack_test_env["goal_pose_visualizer"]
def _visualize_goal_pose(self, pos: torch.Tensor, quat: torch.Tensor) -> None:
"""Visualize the goal frame markers at pos, quat (xyzw)."""
if headless or self.goal_pose_visualizer is None:
return
self.goal_pose_visualizer.visualize(translations=pos.unsqueeze(0), orientations=quat.unsqueeze(0))
def _pose_from_xy_quat(self, xy: torch.Tensor, z: float, quat: torch.Tensor) -> torch.Tensor:
"""Build a 4×4 pose given xy (Tensor[2]), z, and quaternion."""
device = xy.device
dtype = xy.dtype
pos = torch.cat([xy, torch.tensor([z], dtype=dtype, device=device)])
rot = math_utils.matrix_from_quat(quat.to(device).unsqueeze(0))[0]
return math_utils.make_pose(pos, rot)
def _get_cube_pos(self, cube_name: str) -> torch.Tensor:
"""Return the current world position of a cube's root (x, y, z)."""
obj: RigidObject = self.env.scene[cube_name]
return obj.data.root_pos_w[0, :3].clone().detach()
def _place_pose_over_cube(self, cube_name: str, height_offset: float) -> torch.Tensor:
"""Compute a goal pose directly above the named cube using the latest pose."""
base_pos = self._get_cube_pos(cube_name)
return self._pose_from_xy_quat(base_pos[:2], base_pos[2].item() + height_offset, DOWN_FACING_QUAT)
def test_pick_and_stack(self) -> None:
"""Plan and execute pick-and-place to stack cube_1 on cube_2, then cube_3 on the stack."""
cube_1_pos = self._get_cube_pos("cube_1")
cube_2_pos = self._get_cube_pos("cube_2")
cube_3_pos = self._get_cube_pos("cube_3")
print(f"Cube 1 position: {cube_1_pos}")
print(f"Cube 2 position: {cube_2_pos}")
print(f"Cube 3 position: {cube_3_pos}")
# Approach above cube_1
pre_grasp_height = 0.1
pre_grasp_pose = self._pose_from_xy_quat(cube_1_pos[:2], pre_grasp_height, DOWN_FACING_QUAT)
print(f"Pre-grasp pose: {pre_grasp_pose}")
if not headless:
pos_pg = pre_grasp_pose[:3, 3].detach().cpu()
quat_pg = math_utils.quat_from_matrix(pre_grasp_pose[:3, :3].unsqueeze(0))[0].detach().cpu()
self._visualize_goal_pose(pos_pg, quat_pg)
# Plan to pre-grasp
assert self.planner.update_world_and_plan_motion(pre_grasp_pose), "Failed to plan to pre-grasp pose"
_execute_plan(self.env, self.planner, gripper_binary_action=GRIPPER_OPEN_CMD)
# Close gripper to grasp cube_1 (hold pose while closing)
_execute_gripper_action(self.env, self.robot, GRIPPER_CLOSE_CMD, steps=16)
# Plan placement with cube_1 attached (above latest cube_2)
place_pose = self._place_pose_over_cube("cube_2", 0.15)
if not headless:
pos_place = place_pose[:3, 3].detach().cpu()
quat_place = math_utils.quat_from_matrix(place_pose[:3, :3].unsqueeze(0))[0].detach().cpu()
self._visualize_goal_pose(pos_place, quat_place)
# Plan with attached object
assert self.planner.update_world_and_plan_motion(place_pose, expected_attached_object="cube_1"), (
"Failed to plan placement trajectory with attached cube"
)
_execute_plan(self.env, self.planner, gripper_binary_action=GRIPPER_CLOSE_CMD)
# Release cube 1
_execute_gripper_action(self.env, self.robot, GRIPPER_OPEN_CMD, steps=16)
# Go to cube 3
cube_3_pos_now = self._get_cube_pos("cube_3")
pre_grasp_pose = self._pose_from_xy_quat(cube_3_pos_now[:2], pre_grasp_height, DOWN_FACING_QUAT)
print(f"Pre-grasp pose: {pre_grasp_pose}")
if not headless:
pos_pg = pre_grasp_pose[:3, 3].detach().cpu()
quat_pg = math_utils.quat_from_matrix(pre_grasp_pose[:3, :3].unsqueeze(0))[0].detach().cpu()
self._visualize_goal_pose(pos_pg, quat_pg)
assert self.planner.update_world_and_plan_motion(pre_grasp_pose, expected_attached_object=None), (
"Failed to plan retract motion"
)
_execute_plan(self.env, self.planner, gripper_binary_action=GRIPPER_OPEN_CMD)
# Grasp cube 3
_execute_gripper_action(self.env, self.robot, GRIPPER_CLOSE_CMD)
# Plan placement with cube_3 attached, to cube 2 (use latest cube_2 pose)
place_pose = self._place_pose_over_cube("cube_2", 0.18)
if not headless:
pos_place = place_pose[:3, 3].detach().cpu()
quat_place = math_utils.quat_from_matrix(place_pose[:3, :3].unsqueeze(0))[0].detach().cpu()
self._visualize_goal_pose(pos_place, quat_place)
assert self.planner.update_world_and_plan_motion(place_pose, expected_attached_object="cube_3"), (
"Failed to plan placement trajectory with attached cube"
)
_execute_plan(self.env, self.planner, gripper_binary_action=GRIPPER_CLOSE_CMD)
# Release cube 3
_execute_gripper_action(self.env, self.robot, GRIPPER_OPEN_CMD)
print("Pick-and-place stacking test completed successfully!")
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