How to use a method from a custom class as the func in ObservationTermCfg

[hao-tian-zhang]

When I try to create a class to manage the observation functions by myself, I encounter an error.

Error executing job with overrides: []
Traceback (most recent call last):
  File "/home/robot/code/IsaacLab/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/utils/hydra.py", line 85, in hydra_main
    env_cfg.from_dict(hydra_env_cfg["env"])
  File "/home/robot/code/IsaacLab/source/extensions/omni.isaac.lab/omni/isaac/lab/utils/configclass.py", line 140, in _update_class_from_dict
    update_class_from_dict(obj, data, _ns="")
  File "/home/robot/code/IsaacLab/source/extensions/omni.isaac.lab/omni/isaac/lab/utils/dict.py", line 87, in update_class_from_dict
    update_class_from_dict(obj_mem, value, _ns=key_ns)
  File "/home/robot/code/IsaacLab/source/extensions/omni.isaac.lab/omni/isaac/lab/utils/dict.py", line 87, in update_class_from_dict
    update_class_from_dict(obj_mem, value, _ns=key_ns)
  File "/home/robot/code/IsaacLab/source/extensions/omni.isaac.lab/omni/isaac/lab/utils/dict.py", line 110, in update_class_from_dict
    value = string_to_callable(value)
  File "/home/robot/code/IsaacLab/source/extensions/omni.isaac.lab/omni/isaac/lab/utils/string.py", line 158, in string_to_callable
    callable_object = getattr(mod, attr_name)
AttributeError: module 'robot_lab.tasks.locomotion.walker.mdp.observations' has no attribute 'get_gait_phase'

Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.

[hao-tian-zhang]

These are part of the code for my custom class```
class ObsTermAppends:

def __init__(self):
    self.cycle_time = 0.64
    self.target_joint_pos_scale = 0.2
    self.num_envs = 4096
    self.N = 10

def get_gait_phase(self, env : ManagerBasedRLEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")):
    # return float mask 1 is stance, 0 is swing
    asset: Articulation = env.scene[asset_cfg.name]
    self.ref_dof_pos = torch.zeros_like(asset.data.joint_pos[:, asset_cfg.joint_ids])
    self.phase = env.episode_length_buf * env.step_dt / self.cycle_time
    self.stance_mask = torch.zeros((env.num_envs, 2))
    self.compute_ref_state
    sin_pos = torch.sin(2 * torch.pi * self.phase)
    # Add double support phase
    self.stance_mask.zero_()
    # left foot stance
    self.stance_mask[:, 0] = sin_pos >= 0
    # right foot stance
    self.stance_mask[:, 1] = sin_pos < 0
    # Double support phase
    self.stance_mask[torch.abs(sin_pos) < 0.1] = 1
    return self.stance_mask

  def base_pos_z(self, env : ManagerBasedRLEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")) -> torch.Tensor:
      """Root height in the simulation world frame."""
      # extract the used quantities (to enable type-hinting)
      asset: Articulation = env.scene[asset_cfg.name]
      self.base_pos_z = asset.data.root_pos_w[:, 2].unsqueeze(-1)
      return self.base_pos_z

  def base_lin_vel(self, env : ManagerBasedRLEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")) -> torch.Tensor:
      """Root linear velocity in the asset's root frame."""
      # extract the used quantities (to enable type-hinting)
      asset: Articulation = env.scene[asset_cfg.name]
      self.base_lin_vel = asset.data.root_lin_vel_b
      return self.base_lin_vel
  
  def base_ang_vel(self, env : ManagerBasedRLEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")) -> torch.Tensor:
      """Root angular velocity in the asset's root frame."""
      # extract the used quantities (to enable type-hinting)
      asset: Articulation = env.scene[asset_cfg.name]
      self.base_ang_vel = asset.data.root_ang_vel_b
      return self.base_ang_vel

[hao-tian-zhang]

and，I am using these functions like this：

class ObservationsCfg:
    """Observation specifications for the MDP."""
    obsAppends = ObsTermAppends()

    get_gait_phase = ObsTerm(
        func=obsAppends.get_gait_phase,
    )
    diff_ref_pos = ObsTerm(
        func=obsAppends.diff_ref_pos,
    )
    # observation terms (order preserved)
    base_pos_z = ObsTerm(
        func=obsAppends.base_lin_vel,
        noise=Unoise(n_min=-0.1, n_max=0.1),
    )
    base_lin_vel = ObsTerm(
        func=obsAppends.base_lin_vel,
        noise=Unoise(n_min=-0.1, n_max=0.1),
    )
    base_ang_vel = ObsTerm(
        func=obsAppends.base_ang_vel,
        noise=Unoise(n_min=-0.2, n_max=0.2),
    )