This is the example of multi-env with fixed-based camera.
def _create_envs(self, num_envs, spacing, num_per_row):
# define plane on which environments are initialized
lower = gymapi.Vec3(-spacing, -spacing, 0.0) if self.up_axis == 'z' else gymapi.Vec3(0.5 * -spacing, 0.0, -spacing)
upper = gymapi.Vec3(spacing, spacing, spacing)
asset_root = os.path.join(os.path.dirname(os.path.abspath(__file__)), "../../assets")
asset_file = "urdf/cartpole.urdf"
if "asset" in self.cfg["env"]:
asset_root = os.path.join(os.path.dirname(os.path.abspath(__file__)), self.cfg["env"]["asset"].get("assetRoot", asset_root))
asset_file = self.cfg["env"]["asset"].get("assetFileName", asset_file)
asset_path = os.path.join(asset_root, asset_file)
asset_root = os.path.dirname(asset_path)
asset_file = os.path.basename(asset_path)
asset_options = gymapi.AssetOptions()
asset_options.fix_base_link = True
cartpole_asset = self.gym.load_asset(self.sim, asset_root, asset_file, asset_options)
self.num_dof = self.gym.get_asset_dof_count(cartpole_asset)
pose = gymapi.Transform()
if self.up_axis == 'z':
pose.p.z = 2.0
# asset is rotated z-up by default, no additional rotations needed
pose.r = gymapi.Quat(0.0, 0.0, 0.0, 1.0)
else:
pose.p.y = 2.0
pose.r = gymapi.Quat(-np.sqrt(2)/2, 0.0, 0.0, np.sqrt(2)/2)
self.cartpole_handles = []
self.envs = []
self.D_camera_handles = []
for i in range(self.num_envs):
# create env instance
env_ptr = self.gym.create_env(
self.sim, lower, upper, num_per_row
)
cartpole_handle = self.gym.create_actor(env_ptr, cartpole_asset, pose, "cartpole", i, 1, 0)
dof_props = self.gym.get_actor_dof_properties(env_ptr, cartpole_handle)
dof_props['driveMode'][0] = gymapi.DOF_MODE_EFFORT
dof_props['driveMode'][1] = gymapi.DOF_MODE_NONE
dof_props['stiffness'][:] = 0.0
dof_props['damping'][:] = 0.0
self.gym.set_actor_dof_properties(env_ptr, cartpole_handle, dof_props)
self.envs.append(env_ptr)
self.cartpole_handles.append(cartpole_handle)
D_camera_properties = gymapi.CameraProperties()
D_camera_properties.width = 800
D_camera_properties.height = 600
D_camera_properties.enable_tensors = True
self.D_camera = self.gym.create_camera_sensor(env_ptr, D_camera_properties)
self.D_camera_handles.append(self.D_camera)
def pre_physics_step(self, actions):
actions_tensor = torch.zeros(self.num_envs * self.num_dof, device=self.device, dtype=torch.float)
actions_tensor[::self.num_dof] = actions.to(self.device).squeeze() * self.max_push_effort
forces = gymtorch.unwrap_tensor(actions_tensor)
self.gym.set_dof_actuation_force_tensor(self.sim, forces)
self.gym.render_all_camera_sensors(self.sim)
for i in range(4):
D_camera_handle = self.D_camera_handles[i]
cam_start = gymapi.Vec3(-2.0, 0.0, 2.0)
cam_end = gymapi.Vec3(0.0, 0.0, 2.0)
self.gym.set_camera_location(D_camera_handle, self.envs[i], cam_start, cam_end)
self.gym.write_camera_image_to_file(self.sim, self.envs[i], D_camera_handle, gymapi.IMAGE_COLOR, f"/home/jianlin/Desktop/isaacgym/IsaacGymEnvs-main/isaacgymenvs/tasks/image2/rgb_env_{i}.png")
Does anybody encountered this problem? Help!!!
And when it turns to graphical mode, it works!!! Why???
