Omniverse Isaac Gym utils#
Utilities for ease of programming of Omniverse Isaac Gym environments.
Control of robotic manipulators#
Differential inverse kinematics#
This implementation attempts to unify under a single and reusable function the whole set of procedures used to compute the inverse kinematics of a robotic manipulator, originally shown in the Isaac Orbit framework’s task space controllers section, but this time for Omniverse Isaac Gym.
\(\Delta\theta =\) scale \(J^\dagger \, \vec{e}\)
where
The pseudoinverse of the Jacobian (\(J^\dagger\)) is estimated as follows:
Tanspose: \(\; J^\dagger = J^T\)
Pseduoinverse: \(\; J^\dagger = J^T(JJ^T)^{-1}\)
Damped least-squares: \(\; J^\dagger = J^T(JJ^T \, +\) damping\({}^2 I)^{-1}\)
Singular-vale decomposition: See buss2004introduction (section 6)
API#
- skrl.utils.omniverse_isaacgym_utils.ik(jacobian_end_effector: torch.Tensor, current_position: torch.Tensor, current_orientation: torch.Tensor, goal_position: torch.Tensor, goal_orientation: torch.Tensor | None = None, method: str = 'damped least-squares', method_cfg: Mapping[str, float] = {'damping': 0.05, 'min_singular_value': 1e-05, 'scale': 1}, squeeze_output: bool = True) torch.Tensor#
Differential inverse kinematics
- Parameters:
jacobian_end_effector (torch.Tensor) – End effector’s jacobian
current_position (torch.Tensor) – End effector’s current position
current_orientation (torch.Tensor) – End effector’s current orientation
goal_position (torch.Tensor) – End effector’s goal position
goal_orientation (torch.Tensor, optional) – End effector’s goal orientation (default:
None). If not provided, the current orientation will be used instead.method (str, optional) –
Differential inverse kinematics formulation (default:
"damped least-squares"). The supported methods are described in the following table:IK Method
Method tag
Damped least-squares
"damped least-squares"Tanspose
"transpose"Pseduoinverse
"pseudoinverse"Singular-vale decomposition (SVD)
"singular-vale decomposition"method_cfg (dict, optional) – Method configurations (default:
{"scale": 1, "damping": 0.05, "min_singular_value": 1e-5})squeeze_output (bool, optional) – Squeeze output (default:
True)
- Returns:
Change in joint angles
- Return type:
OmniIsaacGymEnvs-like environment instance#
Instantiate a VecEnvBase-based object compatible with OmniIsaacGymEnvs for use outside of the OmniIsaacGymEnvs implementation.
API#
- skrl.utils.omniverse_isaacgym_utils.get_env_instance(headless: bool = True, enable_livestream: bool = False, enable_viewport: bool = False, multi_threaded: bool = False) omni.isaac.gym.vec_env.VecEnvBase#
Instantiate a VecEnvBase-based object compatible with OmniIsaacGymEnvs
- Parameters:
headless (bool, optional) – Disable UI when running (default:
True)enable_livestream (bool, optional) – Whether to enable live streaming (default:
False)enable_viewport (bool, optional) – Whether to enable viewport (default:
False)multi_threaded (bool, optional) – Whether to return a multi-threaded environment instance (default:
False)
- Returns:
Environment instance
- Return type:
omni.isaac.gym.vec_env.VecEnvBase
Example:
from skrl.envs.wrappers.torch import wrap_env from skrl.utils.omniverse_isaacgym_utils import get_env_instance # get environment instance env = get_env_instance(headless=True) # parse sim configuration from omniisaacgymenvs.utils.config_utils.sim_config import SimConfig sim_config = SimConfig({"test": False, "device_id": 0, "headless": True, "multi_gpu": False, "sim_device": "gpu", "enable_livestream": False, "task": {"name": "CustomTask", "physics_engine": "physx", "env": {"numEnvs": 512, "envSpacing": 1.5, "enableDebugVis": False, "clipObservations": 1000.0, "clipActions": 1.0, "controlFrequencyInv": 4}, "sim": {"dt": 0.0083, # 1 / 120 "use_gpu_pipeline": True, "gravity": [0.0, 0.0, -9.81], "add_ground_plane": True, "use_flatcache": True, "enable_scene_query_support": False, "enable_cameras": False, "default_physics_material": {"static_friction": 1.0, "dynamic_friction": 1.0, "restitution": 0.0}, "physx": {"worker_thread_count": 4, "solver_type": 1, "use_gpu": True, "solver_position_iteration_count": 4, "solver_velocity_iteration_count": 1, "contact_offset": 0.005, "rest_offset": 0.0, "bounce_threshold_velocity": 0.2, "friction_offset_threshold": 0.04, "friction_correlation_distance": 0.025, "enable_sleeping": True, "enable_stabilization": True, "max_depenetration_velocity": 1000.0, "gpu_max_rigid_contact_count": 524288, "gpu_max_rigid_patch_count": 33554432, "gpu_found_lost_pairs_capacity": 524288, "gpu_found_lost_aggregate_pairs_capacity": 262144, "gpu_total_aggregate_pairs_capacity": 1048576, "gpu_max_soft_body_contacts": 1048576, "gpu_max_particle_contacts": 1048576, "gpu_heap_capacity": 33554432, "gpu_temp_buffer_capacity": 16777216, "gpu_max_num_partitions": 8}}}}) # import and setup custom task from custom_task import CustomTask task = CustomTask(name="CustomTask", sim_config=sim_config, env=env) env.set_task(task=task, sim_params=sim_config.get_physics_params(), backend="torch", init_sim=True) # wrap the environment env = wrap_env(env, "omniverse-isaacgym")