# Description

This PR created two curriculum mdp that can change any parameter in env
instance.
namely `modify_term_cfg` and `modify_env_param`.

`modify_env_param` is a more general version that can override any value
belongs to env, but requires user to know the full path to the value.

`modify_term_cfg` only work with manager_term, but is a more user
friendly version that simplify path specification, for example, instead
of write "observation_manager.cfg.policy.joint_pos.noise", you instead
write "observations.policy.joint_pos.noise", consistent with hydra
overriding style

Besides path to value is needed, modify_fn, modify_params is also needed
for telling the term how to modify.



Demo 1: difficulty-adaptive modification for all python native data type
```
# iv -> initial value, fv -> final value
def initial_final_interpolate_fn(env: ManagerBasedRLEnv, env_id, data, iv, fv, get_fraction):
    iv_, fv_ = torch.tensor(iv, device=env.device), torch.tensor(fv, device=env.device)
    fraction = eval(get_fraction)
    new_val = fraction * (fv_ - iv_) + iv_
    if isinstance(data, float):
        return new_val.item()
    elif isinstance(data, int):
        return int(new_val.item())
    elif isinstance(data, (tuple, list)):
        raw = new_val.tolist()
        # assume iv is sequence of all ints or all floats:
        is_int = isinstance(iv[0], int)
        casted = [int(x) if is_int else float(x) for x in raw]
        return tuple(casted) if isinstance(data, tuple) else casted
    else:
        raise TypeError(f"Does not support the type {type(data)}")
```
(float)
```
    joint_pos_unoise_min_adr = CurrTerm(
        func=mdp.modify_term_cfg,
        params={
            "address": "observations.policy.joint_pos.noise.n_min",
            "modify_fn": initial_final_interpolate_fn,
            "modify_params": {"iv": 0., "fv": -.1, "get_fraction": "env.command_manager.get_command("difficulty")"}
        }
    )
```

(tuple or list)
```
command_object_pose_xrange_adr = CurrTerm(
        func=mdp.modify_term_cfg,
        params={
            "address": "commands.object_pose.ranges.pos_x",
            "modify_fn": initial_final_interpolate_fn,
            "modify_params": {"iv": (-.5, -.5), "fv": (-.75, -.25), "get_fraction": "env.command_manager.get_command("difficulty")"}
        }
    )
```

Demo 3: overriding entire term on env_step counter rather than adaptive
```
def value_override(env: ManagerBasedRLEnv, env_id, data, new_val, num_steps):
    if env.common_step_counter > num_steps:
        return new_val
    return mdp.modify_term_cfg.NO_CHANGE

object_pos_curriculum = CurrTerm(
        func=mdp.modify_term_cfg,
        params={
            "address": "commands.object_pose",
            "modify_fn": value_override,
            "modify_params": {"new_val": <new_observation_term>, "num_step": 120000 }
        }
    )
```

Demo 4: overriding Tensor field within some arbitary class not visible
from term_cfg
(you can see that 'address' is not as nice as mdp.modify_term_cfg)
```
def resample_bucket_range(env: ManagerBasedRLEnv, env_id, data, static_friction_range, dynamic_friction_range, restitution_range, num_steps):
    if env.common_step_counter > num_steps:
          range_list = [static_friction_range, dynamic_friction_range, restitution_range]
          ranges = torch.tensor(range_list, device="cpu")
          new_buckets = math_utils.sample_uniform(ranges[:, 0], ranges[:, 1], (len(data), 3), device="cpu")
          return new_buckets
    return mdp.modify_env_param.NO_CHANGE

object_physics_material_curriculum = CurrTerm(
        func=mdp.modify_env_param,
        params={
            "address": "event_manager.cfg.object_physics_material.func.material_buckets",
            "modify_fn": resample_bucket_range,
            "modify_params": {"static_friction_range": [.5, 1.], "dynamic_friction_range": [.3, 1.], "restitution_range": [0.0, 0.5], "num_step": 120000 }
        }
    )
```


## Type of change

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applicable. -->

- New feature (non-breaking change which adds functionality)


## Checklist

- [x] I have run the [`pre-commit` checks](https://pre-commit.com/) with
`./isaaclab.sh --format`
- [ ] I have made corresponding changes to the documentation
- [x] My changes generate no new warnings
- [x] I have added tests that prove my fix is effective or that my
feature works
- [x] I have updated the changelog and the corresponding version in the
extension's `config/extension.toml` file
- [x] I have added my name to the `CONTRIBUTORS.md` or my name already
exists there

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---------
Signed-off-by: ooctipus <zhengyuz@nvidia.com>
Signed-off-by: Kelly Guo <kellyg@nvidia.com>
Co-authored-by: Kelly Guo <kellyg@nvidia.com>