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issacdataengine/docs_crawled/concepts_controllers.md
Tangger 3d6b73753a feat: add test tube pick task with custom assets and grasp annotations 
- Add pick_test_tube task: USDC asset repackaging, grasp generation, task config
- Add tools: usdc_to_obj.py, repackage_test_tube.py, fix_test_tube_materials.py
- Add custom_task_guide.md: full Chinese documentation for creating custom tasks
- Add crawled InternDataEngine online docs (23 pages)
- Add grasp generation script (gen_tube_grasp.py) and pipeline config
2026-04-05 11:01:59 +08:00

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# Source: https://internrobotics.github.io/InternDataEngine-Docs/concepts/controllers.html
# Controllers [​](#controllers)
Controllers are the core component responsible for robot arm motion planning in InternDataEngine. They provide **GPU-accelerated collision-free trajectory generation **using CuRobo, while simultaneously handling gripper actions.
## Overview [​](#overview)
Each controller manages motion planning for a single robot arm. For dual-arm robots (like Lift2, Genie1, Split Aloha), two controller instances are created - one for each arm.
The controller's primary responsibilities include:
- **Collision-Free Motion Planning **: Generate safe trajectories that avoid obstacles in the scene using CuRobo's GPU-accelerated motion generation
- **Inverse Kinematics **: Solve IK queries to find joint configurations for desired end-effector poses
- **Gripper Control **: Handle gripper open/close commands integrated with arm motion
- **Collision World Management **: Update and maintain the collision world from the simulation scene
### CuRobo Configuration Files [​](#curobo-configuration-files)
Each robot arm requires a CuRobo configuration file that defines kinematics, collision geometry, and motion generation parameters:
- **YAML Configs **: `workflows/simbox/curobo/src/curobo/content/configs/robot/ `
- **URDF Files **: `workflows/simbox/curobo/src/curobo/content/assets/robot/ `
Available robot configs include:
- `r5a_left_arm.yml `/ `r5a_right_arm.yml `- ARX Lift-2 (R5a arms)
- `piper100_left_arm.yml `/ `piper100_right_arm.yml `- AgiLEx Split Aloha
- `G1_120s_left_arm_parallel_gripper.yml `/ `G1_120s_right_arm_parallel_gripper.yml `- Genie-1
- `fr3_left_arm.yml `- Franka FR3
- `frankarobotiq_left_arm.yml `- Franka with Robotiq 2F-85 gripper
## Controller Architecture [​](#controller-architecture)
All controllers inherit from `TemplateController `and customize behavior via method overrides.
```
TemplateController (base class)
├── FR3Controller
├── FrankaRobotiq85Controller
├── Genie1Controller
├── Lift2Controller
└── SplitAlohaController
```
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## Controller Wrappers [​](#controller-wrappers)
Controller wrappers (located in `workflows/simbox/core/controllers/ `) provide a unified interface for motion planning. The base class `TemplateController `implements all core functionality, with subclasses overriding specific methods for robot-specific configurations.
Template Code Example:
python
```
"""
Template Controller base class for robot motion planning.
Common functionality extracted from FR3, FrankaRobotiq85, Genie1, Lift2, SplitAloha.
Subclasses implement _get_default_ignore_substring() and _configure_joint_indices().
"""
import random
import time
from copy import deepcopy
from typing import List, Optional
import numpy as np
import torch
from core.utils.constants import CUROBO_BATCH_SIZE
from core.utils.plan_utils import (
filter_paths_by_position_error,
filter_paths_by_rotation_error,
sort_by_difference_js,
)
from curobo.cuda_robot_model.cuda_robot_model import CudaRobotModel
from curobo.geom.sdf.world import CollisionCheckerType
from curobo.geom.sphere_fit import SphereFitType
from curobo.geom.types import WorldConfig
from curobo.types.base import TensorDeviceType
from curobo.types.math import Pose
from curobo.types.robot import JointState, RobotConfig
from curobo.util.usd_helper import UsdHelper
from curobo.util_file import get_world_configs_path, join_path, load_yaml
from curobo.wrap.reacher.ik_solver import IKSolver, IKSolverConfig
from curobo.wrap.reacher.motion_gen import (
MotionGen,
MotionGenConfig,
MotionGenPlanConfig,
PoseCostMetric,
)
from omni.isaac.core import World
from omni.isaac.core.controllers import BaseController
from omni.isaac.core.tasks import BaseTask
from omni.isaac.core.utils.prims import get_prim_at_path
from omni.isaac.core.utils.transformations import (
get_relative_transform,
pose_from_tf_matrix,
)
from omni.isaac.core.utils.types import ArticulationAction
class TemplateController(BaseController):
"""Base controller for CuRobo-based motion planning. Supports single and batch planning."""
def __init__(
self,
name: str,
robot_file: str,
task: BaseTask,
world: World,
constrain_grasp_approach: bool = False,
collision_activation_distance: float = 0.03,
ignore_substring: Optional[List[str]] = None,
use_batch: bool = False,
**kwargs,
) -> None:
super().__init__(name=name)
self.name = name
self.world = world
self.task = task
self.robot = self.task.robots[name]
self.ignore_substring = self._get_default_ignore_substring()
if ignore_substring is not None:
self.ignore_substring = ignore_substring
self.ignore_substring.append(name)
self.use_batch = use_batch
self.constrain_grasp_approach = constrain_grasp_approach
self.collision_activation_distance = collision_activation_distance
self.usd_help = UsdHelper()
self.tensor_args = TensorDeviceType()
self.init_curobo = False
self.robot_file = robot_file
self.num_plan_failed = 0
self.raw_js_names = []
self.cmd_js_names = []
self.arm_indices = np.array([])
self.gripper_indices = np.array([])
self.reference_prim_path = None
self.lr_name = None
self._ee_trans = 0.0
self._ee_ori = 0.0
self._gripper_state = 1.0
self._gripper_joint_position = np.array([1.0])
self.idx_list = None
self._configure_joint_indices(robot_file)
self._load_robot(robot_file)
self._load_kin_model()
self._load_world()
self._init_motion_gen()
self.usd_help.load_stage(self.world.stage)
self.cmd_plan = None
self.cmd_idx = 0
self._step_idx = 0
self.num_last_cmd = 0
self.ds_ratio = 1
def _get_default_ignore_substring(self) -> List[str]:
return ["material", "Plane", "conveyor", "scene", "table"]
def _configure_joint_indices(self, robot_file: str) -> None:
raise NotImplementedError
def _load_robot(self, robot_file: str) -> None:
self.robot_cfg = load_yaml(robot_file)["robot_cfg"]
def _load_kin_model(self) -> None:
urdf_file = self.robot_cfg["kinematics"]["urdf_path"]
base_link = self.robot_cfg["kinematics"]["base_link"]
ee_link = self.robot_cfg["kinematics"]["ee_link"]
robot_cfg = RobotConfig.from_basic(urdf_file, base_link, ee_link, self.tensor_args)
self.kin_model = CudaRobotModel(robot_cfg.kinematics)
def _load_world(self, use_default: bool = True) -> None:
if use_default:
self.world_cfg = WorldConfig()
else:
world_cfg_table = WorldConfig.from_dict(
load_yaml(join_path(get_world_configs_path(), "collision_table.yml"))
)
self._world_cfg_table = world_cfg_table
self._world_cfg_table.cuboid[0].pose[2] -= 10.5
world_cfg1 = WorldConfig.from_dict(
load_yaml(join_path(get_world_configs_path(), "collision_table.yml"))
).get_mesh_world()
world_cfg1.mesh[0].name += "_mesh"
world_cfg1.mesh[0].pose[2] = -10.5
self.world_cfg = WorldConfig(cuboid=world_cfg_table.cuboid, mesh=world_cfg1.mesh)
def _get_motion_gen_collision_cache(self):
return {"obb": 700, "mesh": 700}
def _get_grasp_approach_linear_axis(self) -> int:
return 2
def _get_sort_path_weights(self) -> Optional[List[float]]:
return None
def _init_motion_gen(self) -> None:
pose_metric = None
if self.constrain_grasp_approach:
pose_metric = PoseCostMetric.create_grasp_approach_metric(
offset_position=0.1,
linear_axis=self._get_grasp_approach_linear_axis(),
)
if self.use_batch:
self.plan_config = MotionGenPlanConfig(
enable_graph=True,
enable_opt=True,
need_graph_success=True,
enable_graph_attempt=4,
max_attempts=4,
enable_finetune_trajopt=True,
parallel_finetune=True,
time_dilation_factor=1.0,
)
else:
self.plan_config = MotionGenPlanConfig(
enable_graph=False,
enable_graph_attempt=7,
max_attempts=10,
pose_cost_metric=pose_metric,
enable_finetune_trajopt=True,
time_dilation_factor=1.0,
)
motion_gen_config = MotionGenConfig.load_from_robot_config(
self.robot_cfg,
self.world_cfg,
self.tensor_args,
interpolation_dt=0.01,
collision_activation_distance=self.collision_activation_distance,
trajopt_tsteps=32,
collision_checker_type=CollisionCheckerType.MESH,
use_cuda_graph=True,
self_collision_check=True,
collision_cache=self._get_motion_gen_collision_cache(),
num_trajopt_seeds=12,
num_graph_seeds=12,
optimize_dt=True,
trajopt_dt=None,
trim_steps=None,
project_pose_to_goal_frame=False,
)
ik_config = IKSolverConfig.load_from_robot_config(
self.robot_cfg,
self.world_cfg,
rotation_threshold=0.05,
position_threshold=0.005,
num_seeds=20,
self_collision_check=True,
self_collision_opt=True,
tensor_args=self.tensor_args,
use_cuda_graph=True,
collision_checker_type=CollisionCheckerType.MESH,
collision_cache={"obb": 700, "mesh": 700},
)
self.ik_solver = IKSolver(ik_config)
self.motion_gen = MotionGen(motion_gen_config)
print("warming up..")
if self.use_batch:
self.motion_gen.warmup(parallel_finetune=True, batch=CUROBO_BATCH_SIZE)
else:
self.motion_gen.warmup(enable_graph=True, warmup_js_trajopt=False)
self.world_model = self.motion_gen.world_collision
self.motion_gen.clear_world_cache()
self.motion_gen.reset(reset_seed=False)
self.motion_gen.update_world(self.world_cfg)
def update_pose_cost_metric(self, hold_vec_weight: Optional[List[float]] = None) -> None:
if hold_vec_weight:
pose_cost_metric = PoseCostMetric(
hold_partial_pose=True,
hold_vec_weight=self.motion_gen.tensor_args.to_device(hold_vec_weight),
)
else:
pose_cost_metric = None
self.plan_config.pose_cost_metric = pose_cost_metric
def update(self) -> None:
obstacles = self.usd_help.get_obstacles_from_stage(
ignore_substring=self.ignore_substring, reference_prim_path=self.reference_prim_path
).get_collision_check_world()
if self.motion_gen is not None:
self.motion_gen.update_world(obstacles)
self.world_cfg = obstacles
def reset(self, ignore_substring: Optional[str] = None) -> None:
if ignore_substring:
self.ignore_substring = ignore_substring
self.update()
self.init_curobo = True
self.cmd_plan = None
self.cmd_idx = 0
self.num_plan_failed = 0
if self.lr_name == "left":
self._gripper_state = 1.0 if self.robot.left_gripper_state == 1.0 else -1.0
elif self.lr_name == "right":
self._gripper_state = 1.0 if self.robot.right_gripper_state == 1.0 else -1.0
if self.lr_name == "left":
self.robot_ee_path = self.robot.fl_ee_path
self.robot_base_path = self.robot.fl_base_path
else:
self.robot_ee_path = self.robot.fr_ee_path
self.robot_base_path = self.robot.fr_base_path
self.T_base_ee_init = get_relative_transform(
get_prim_at_path(self.robot_ee_path), get_prim_at_path(self.robot_base_path)
)
self.T_world_base_init = get_relative_transform(
get_prim_at_path(self.robot_base_path), get_prim_at_path(self.task.root_prim_path)
)
self.T_world_ee_init = self.T_world_base_init @ self.T_base_ee_init
self._ee_trans, self._ee_ori = self.get_ee_pose()
self._ee_trans = self.tensor_args.to_device(self._ee_trans)
self._ee_ori = self.tensor_args.to_device(self._ee_ori)
self.update_pose_cost_metric()
def update_specific(self, ignore_substring, reference_prim_path):
obstacles = self.usd_help.get_obstacles_from_stage(
ignore_substring=ignore_substring, reference_prim_path=reference_prim_path
).get_collision_check_world()
if self.motion_gen is not None:
self.motion_gen.update_world(obstacles)
self.world_cfg = obstacles
def plan(self, ee_translation_goal, ee_orientation_goal, sim_js: JointState, js_names: list):
if self.use_batch:
ik_goal = Pose(
position=self.tensor_args.to_device(ee_translation_goal.unsqueeze(0).expand(CUROBO_BATCH_SIZE, -1)),
quaternion=self.tensor_args.to_device(ee_orientation_goal.unsqueeze(0).expand(CUROBO_BATCH_SIZE, -1)),
batch=CUROBO_BATCH_SIZE,
)
cu_js = JointState(
position=self.tensor_args.to_device(np.tile((sim_js.positions)[np.newaxis, :], (CUROBO_BATCH_SIZE, 1))),
velocity=self.tensor_args.to_device(np.tile((sim_js.positions)[np.newaxis, :], (CUROBO_BATCH_SIZE, 1)))
* 0.0,
acceleration=self.tensor_args.to_device(
np.tile((sim_js.positions)[np.newaxis, :], (CUROBO_BATCH_SIZE, 1))
)
* 0.0,
jerk=self.tensor_args.to_device(np.tile((sim_js.positions)[np.newaxis, :], (CUROBO_BATCH_SIZE, 1)))
* 0.0,
joint_names=js_names,
)
cu_js = cu_js.get_ordered_joint_state(self.cmd_js_names)
return self.motion_gen.plan_batch(cu_js, ik_goal, self.plan_config.clone())
ik_goal = Pose(
position=self.tensor_args.to_device(ee_translation_goal),
quaternion=self.tensor_args.to_device(ee_orientation_goal),
)
cu_js = JointState(
position=self.tensor_args.to_device(sim_js.positions),
velocity=self.tensor_args.to_device(sim_js.velocities) * 0.0,
acceleration=self.tensor_args.to_device(sim_js.velocities) * 0.0,
jerk=self.tensor_args.to_device(sim_js.velocities) * 0.0,
joint_names=js_names,
)
cu_js = cu_js.get_ordered_joint_state(self.cmd_js_names)
return self.motion_gen.plan_single(cu_js.unsqueeze(0), ik_goal, self.plan_config.clone())
def forward(self, manip_cmd, eps=5e-3):
ee_trans, ee_ori = manip_cmd[0:2]
gripper_fn = manip_cmd[2]
params = manip_cmd[3]
assert hasattr(self, gripper_fn)
method = getattr(self, gripper_fn)
if gripper_fn in ["in_plane_rotation", "mobile_move", "dummy_forward"]:
return method(**params)
elif gripper_fn in ["update_pose_cost_metric", "update_specific"]:
method(**params)
return self.ee_forward(ee_trans, ee_ori, eps=eps, skip_plan=True)
else:
method(**params)
return self.ee_forward(ee_trans, ee_ori, eps)
def ee_forward(
self,
ee_trans: torch.Tensor | np.ndarray,
ee_ori: torch.Tensor | np.ndarray,
eps=1e-4,
skip_plan=False,
):
ee_trans = self.tensor_args.to_device(ee_trans)
ee_ori = self.tensor_args.to_device(ee_ori)
sim_js = self.robot.get_joints_state()
js_names = self.robot.dof_names
plan_flag = torch.logical_or(
torch.norm(self._ee_trans - ee_trans) > eps,
torch.norm(self._ee_ori - ee_ori) > eps,
)
if not skip_plan:
if plan_flag:
self.cmd_idx = 0
self._step_idx = 0
self.num_last_cmd = 0
result = self.plan(ee_trans, ee_ori, sim_js, js_names)
if self.use_batch:
if result.success.any():
self._ee_trans = ee_trans
self._ee_ori = ee_ori
paths = result.get_successful_paths()
position_filter_res = filter_paths_by_position_error(
paths, result.position_error[result.success]
)
rotation_filter_res = filter_paths_by_rotation_error(
paths, result.rotation_error[result.success]
)
filtered_paths = [
p for i, p in enumerate(paths) if position_filter_res[i] and rotation_filter_res[i]
]
if len(filtered_paths) == 0:
filtered_paths = paths
sort_weights = self._get_sort_path_weights()
weights_arg = self.tensor_args.to_device(sort_weights) if sort_weights is not None else None
sorted_indices = sort_by_difference_js(filtered_paths, weights=weights_arg)
cmd_plan = self.motion_gen.get_full_js(paths[sorted_indices[0]])
self.idx_list = list(range(len(self.raw_js_names)))
self.cmd_plan = cmd_plan.get_ordered_joint_state(self.raw_js_names)
self.num_plan_failed = 0
else:
print("Plan did not converge to a solution.")
self.num_plan_failed += 1
else:
succ = result.success.item()
if succ:
self._ee_trans = ee_trans
self._ee_ori = ee_ori
cmd_plan = result.get_interpolated_plan()
self.idx_list = list(range(len(self.raw_js_names)))
self.cmd_plan = cmd_plan.get_ordered_joint_state(self.raw_js_names)
self.num_plan_failed = 0
else:
print("Plan did not converge to a solution.")
self.num_plan_failed += 1
if self.cmd_plan and self._step_idx % 1 == 0:
cmd_state = self.cmd_plan[self.cmd_idx]
art_action = ArticulationAction(
cmd_state.position.cpu().numpy(),
cmd_state.velocity.cpu().numpy() * 0.0,
joint_indices=self.idx_list,
)
self.cmd_idx += self.ds_ratio
if self.cmd_idx >= len(self.cmd_plan):
self.cmd_idx = 0
self.cmd_plan = None
else:
self.num_last_cmd += 1
art_action = ArticulationAction(joint_positions=sim_js.positions[self.arm_indices])
else:
art_action = ArticulationAction(joint_positions=sim_js.positions[self.arm_indices])
self._step_idx += 1
arm_action = art_action.joint_positions
gripper_action = self.get_gripper_action()
joint_positions = np.concatenate([arm_action, gripper_action])
self._action = {
"joint_positions": joint_positions,
"joint_indices": np.concatenate([self.arm_indices, self.gripper_indices]),
"lr_name": self.lr_name,
"arm_action": arm_action,
"gripper_action": gripper_action,
}
return self._action
def get_gripper_action(self):
return np.clip(self._gripper_state * self._gripper_joint_position, 0.0, 0.04)
def get_ee_pose(self):
sim_js = self.robot.get_joints_state()
q_state = torch.tensor(sim_js.positions[self.arm_indices], **self.tensor_args.as_torch_dict()).reshape(1, -1)
ee_pose = self.kin_model.get_state(q_state)
return ee_pose.ee_position[0].cpu().numpy(), ee_pose.ee_quaternion[0].cpu().numpy()
def get_armbase_pose(self):
armbase_pose = get_relative_transform(
get_prim_at_path(self.robot_base_path), get_prim_at_path(self.task.root_prim_path)
)
return pose_from_tf_matrix(armbase_pose)
def forward_kinematic(self, q_state: np.ndarray):
q_state = q_state.reshape(1, -1)
q_state = self.tensor_args.to_device(q_state)
out = self.kin_model.get_state(q_state)
return out.ee_position[0].cpu().numpy(), out.ee_quaternion[0].cpu().numpy()
def close_gripper(self):
self._gripper_state = -1.0
def open_gripper(self):
self._gripper_state = 1.0
def attach_obj(self, obj_prim_path: str, link_name="attached_object"):
sim_js = self.robot.get_joints_state()
js_names = self.robot.dof_names
cu_js = JointState(
position=self.tensor_args.to_device(sim_js.positions),
velocity=self.tensor_args.to_device(sim_js.velocities) * 0.0,
acceleration=self.tensor_args.to_device(sim_js.velocities) * 0.0,
jerk=self.tensor_args.to_device(sim_js.velocities) * 0.0,
joint_names=js_names,
)
self.motion_gen.attach_objects_to_robot(
cu_js,
[obj_prim_path],
link_name=link_name,
sphere_fit_type=SphereFitType.VOXEL_VOLUME_SAMPLE_SURFACE,
world_objects_pose_offset=Pose.from_list([0, 0, 0.01, 1, 0, 0, 0], self.tensor_args),
)
def detach_obj(self):
self.motion_gen.detach_object_from_robot()
```
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__init__(self, name, robot_file, task, world, constrain_grasp_approach, collision_activation_distance, ignore_substring, use_batch, **kwargs)
Initialize the controller, load robot configuration, and set up CuRobo motion generator.
Parameters:
- **name **( str ): Controller name, matching the robot name in task.
- **robot_file **( str ): Path to CuRobo robot YAML configuration file.
- **task **( BaseTask ): The task instance containing robots and scene.
- **world **( World ): Isaac Sim world instance.
- **constrain_grasp_approach **( bool , optional): Whether to add grasp approach constraint. Default is `False `.
- **collision_activation_distance **( float , optional): Distance threshold for collision activation. Default is `0.03 `.
- **ignore_substring **( List[str] , optional): Substrings for objects to ignore in collision checking.
- **use_batch **( bool , optional): Enable batch planning mode for multiple goals. Default is `False `.
- ** **kwargs **: Additional keyword arguments.
reset(self, ignore_substring=None)
Reset controller state, update collision world, and initialize end-effector pose.
Parameters:
- **ignore_substring **( List[str] , optional): New collision filter substrings to use.
update_pose_cost_metric(self, hold_vec_weight=None)
Update the pose cost metric for constrained motion planning. This method is used to hold specific orientations or positions during robot motion, which is useful for tasks like keeping a container upright while moving.
Parameters:
- **hold_vec_weight **( List[float] , optional): A 6-element weight vector `[angular-x, angular-y, angular-z, linear-x, linear-y, linear-z] `that controls constraint costs. Defaults to `None `, which corresponds to `[0, 0, 0, 0, 0, 0] `(no constraints). For example, `[1, 1, 1, 0, 0, 0] `holds the tool orientation fixed during motion.
Reference:
- [CuRobo Constrained Planning](https://curobo.org/advanced_examples/3_constrained_planning.html)
update_specific(self, ignore_substring, reference_prim_path)
Update collision world with specific ignore substrings and reference prim path. Used for fine-grained collision filtering during skill execution.
Parameters:
- **ignore_substring **( List[str] ): List of substrings for objects to ignore in collision checking.
- **reference_prim_path **( str ): Reference prim path for relative transform calculations.
plan(self, ee_translation_goal, ee_orientation_goal, sim_js, js_names)
Generate a collision-free trajectory from current joint state to target end-effector pose.
Parameters:
- **ee_translation_goal **( torch.Tensor | np.ndarray ): Target end-effector position `[x, y, z] `.
- **ee_orientation_goal **( torch.Tensor | np.ndarray ): Target end-effector orientation as quaternion `[w, x, y, z] `.
- **sim_js **( JointState ): Current joint state from simulation.
- **js_names **( list ): List of joint names in simulation order.
Returns:
- **MotionGenResult **: CuRobo planning result containing success status, trajectory, and errors.
forward(self, manip_cmd, eps=5e-3)
Execute a manipulation command. This is the main entry point called by skills to execute motions.
Parameters:
- **manip_cmd **( tuple ): Command tuple `(ee_trans, ee_ori, gripper_fn, params) `:
- `ee_trans `: Target end-effector translation.
- `ee_ori `: Target end-effector orientation.
- `gripper_fn `: Name of gripper function to call (e.g., `"open_gripper" `, `"close_gripper" `).
- `params `: Parameters for the gripper function.
- **eps **( float , optional): Position/orientation threshold to trigger new planning. Default is `5e-3 `.
Returns:
- **dict **: Action dictionary containing:
- `joint_positions `: Full joint positions including gripper.
- `joint_indices `: Indices of joints to command.
- `lr_name `: Left/right arm identifier.
- `arm_action `: Arm joint positions only.
- `gripper_action `: Gripper joint positions only.
## Example: Lift2Controller [​](#example-lift2controller)
The `Lift2Controller `demonstrates how to create a robot-specific controller by overriding key methods from `TemplateController `. This controller manages the ARX Lift-2 dual-arm robot with R5a arms.
python
```
"""Lift2 mobile manipulator controller – template-based."""
import numpy as np
from core.controllers.base_controller import register_controller
from core.controllers.template_controller import TemplateController
@register_controller
class Lift2Controller(TemplateController):
def _get_default_ignore_substring(self):
return ["material", "Plane", "conveyor", "scene", "table", "fluid"]
def _configure_joint_indices(self, robot_file: str) -> None:
self.raw_js_names = ["joint1", "joint2", "joint3", "joint4", "joint5", "joint6"]
if "left" in robot_file:
self.cmd_js_names = ["fl_joint1", "fl_joint2", "fl_joint3", "fl_joint4", "fl_joint5", "fl_joint6"]
self.arm_indices = np.array([10, 12, 14, 16, 18, 20])
self.gripper_indices = np.array([23])
self.reference_prim_path = self.task.robots[self.name].fl_base_path
self.lr_name = "left"
self._gripper_state = 1.0 if self.robot.left_gripper_state == 1.0 else -1.0
elif "right" in robot_file:
self.cmd_js_names = ["fr_joint1", "fr_joint2", "fr_joint3", "fr_joint4", "fr_joint5", "fr_joint6"]
self.arm_indices = np.array([9, 11, 13, 15, 17, 19])
self.gripper_indices = np.array([21])
self.reference_prim_path = self.task.robots[self.name].fr_base_path
self.lr_name = "right"
self._gripper_state = 1.0 if self.robot.right_gripper_state == 1.0 else -1.0
else:
raise NotImplementedError("robot_file must contain 'left' or 'right'")
self._gripper_joint_position = np.array([1.0])
def _get_grasp_approach_linear_axis(self) -> int:
"""Lift2 uses x-axis (0) for grasp approach."""
return 0
def get_gripper_action(self):
return np.clip(self._gripper_state * self._gripper_joint_position, 0.0, 0.1)
def forward(self, manip_cmd, eps=5e-3):
ee_trans, ee_ori = manip_cmd[0:2]
gripper_fn = manip_cmd[2]
params = manip_cmd[3]
gripper_vel = manip_cmd[4] if len(manip_cmd) > 4 else None
assert hasattr(self, gripper_fn)
method = getattr(self, gripper_fn)
if gripper_fn in ["in_plane_rotation", "mobile_move", "dummy_forward", "joint_ctrl"]:
return method(**params)
elif gripper_fn in ["update_pose_cost_metric", "update_specific"]:
method(**params)
return self.ee_forward(ee_trans, ee_ori, eps=eps, gripper_vel=gripper_vel, skip_plan=True)
else:
method(**params)
return self.ee_forward(ee_trans, ee_ori, eps=eps, gripper_vel=gripper_vel)
def ee_forward(
self,
ee_trans,
ee_ori,
eps=1e-4,
skip_plan=False,
gripper_vel=None,
):
super().ee_forward(ee_trans, ee_ori, eps=eps, skip_plan=skip_plan)
self._action["gripper_vel"] = gripper_vel
return self._action
```
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### Overridden Methods [​](#overridden-methods)
The following four methods must be overridden to create a robot-specific controller:
_get_default_ignore_substring(self)
Return a list of substrings for objects to ignore during collision checking. These typically include visual-only objects, scene elements, and robot-specific parts.
Returns:
- **List[str] **: List of substrings to filter out collision objects. For Lift2, this includes `"material" `, `"Plane" `, `"conveyor" `, `"scene" `, `"table" `, and `"fluid" `.
_configure_joint_indices(self, robot_file)
Configure the mapping between CuRobo planner joint names and simulation joint names. This is critical for translating planned trajectories into simulation commands.
Parameters:
- **robot_file **( str ): Path to the CuRobo robot configuration file. Used to determine left/right arm for dual-arm robots.
Sets the following attributes:
- **raw_js_names **( List[str] ): Joint names in CuRobo planner order.
- **cmd_js_names **( List[str] ): Joint names in simulation order.
- **arm_indices **( np.ndarray ): Indices of arm joints in simulation.
- **gripper_indices **( np.ndarray ): Indices of gripper joints in simulation.
- **reference_prim_path **( str ): Path to robot base prim for collision reference.
- **lr_name **( str ): "left" or "right" for dual-arm robots.
_get_grasp_approach_linear_axis(self)
Return the axis used for constrained grasp approach motion. This defines which axis the end-effector should align with during approach.
Returns:
- **int **: Axis index (0=x, 1=y, 2=z). Default in `TemplateController `is 2 (z-axis). Lift2 uses 0 (x-axis) due to its gripper orientation.
get_gripper_action(self)
Convert internal gripper state to actual gripper joint position command. Different robots have different gripper mechanisms and ranges.
Returns:
- **np.ndarray **: Gripper joint position(s). The value is clipped to the robot's valid gripper range. For Lift2, the range is `[0.0, 0.1] `.
## References [​](#references)
- [CuRobo Documentation](https://curobo.org/)
- [Genie Sim 3.0 - Motion Generator](https://github.com/AgibotTech/genie_sim/tree/main/source/data_collection/server/motion_generator)
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