`rofunc.simulator.gluon_sim`#

1. Module Contents#

class rofunc.simulator.gluon_sim.GluonSim(args)[source]#

Initialization

Initialize the robot-centered simulator

show(visual_obs_flag=False, camera_props=None, attached_body=None, local_transform=None)[source]#: Visualize the CURI robot :param visual_obs_flag: if True, show visual observation :param camera_props: If visual_obs_flag is True, use this camera_props to config the camera :param attached_body: If visual_obs_flag is True, use this to refer the body the camera attached to :param local_transform: If visual_obs_flag is True, use this local transform to adjust the camera pose

update_robot(traj, attractor_handles, axes_geom, sphere_geom, index)[source]#

run_traj(traj, attracted_rigid_bodies='6_Link', update_freq=0.001)[source]#

run_traj_multi_rigid_bodies_with_interference(traj: List, intf_index: List, intf_mode: str, intf_forces=None, intf_torques=None, intf_joints: List = None, intf_efforts: numpy.ndarray = None, attracted_rigid_bodies: List = None, update_freq=0.001, save_name=None)[source]#: Run the trajectory with multiple rigid bodies with interference, the default is to run the trajectory with the left and right hand of the CURI robot. Args:

traj: a list of trajectories, each trajectory is a numpy array of shape (N, 7) intf_index: a list of the timing indices of the interference occurs intf_mode: the mode of the interference, [“actor_dof_efforts”, “body_forces”, “body_force_at_pos”] intf_forces: a tensor of shape (num_envs, num_bodies, 3), the interference forces applied to the bodies intf_torques: a tensor of shape (num_envs, num_bodies, 3), the interference torques applied to the bodies intf_joints: [list], e.g. [“panda_left_hand”] intf_efforts: array containing the efforts for all degrees of freedom of the actor. attracted_rigid_bodies: [list], e.g. [“panda_left_hand”, “panda_right_hand”] update_freq: the frequency of updating the robot pose