# reset
    reset_cart_position = EventTerm(
        func=mdp.reset_joints_by_offset,
        mode="reset",
        params={
            "asset_cfg": SceneEntityCfg("robot", joint_names=["slider_to_cart"]),
            "position_range": (-1.0, 1.0),
            "velocity_range": (-0.5, 0.5),
        },
    )

    reset_pole_position = EventTerm(
        func=mdp.reset_joints_by_offset,
        mode="reset",
        params={
            "asset_cfg": SceneEntityCfg("robot", joint_names=["cart_to_pole"]),
            "position_range": (-0.25 * math.pi, 0.25 * math.pi),
            "velocity_range": (-0.25 * math.pi, 0.25 * math.pi),
        },
    )

def reset_joints_by_scale(
    env: ManagerBasedEnv,
    env_ids: torch.Tensor,
    position_range: tuple[float, float],
    velocity_range: tuple[float, float],
    asset_cfg: SceneEntityCfg = SceneEntityCfg("robot"),
):
    """Reset the robot joints by scaling the default position and velocity by the given ranges.

    This function samples random values from the given ranges and scales the default joint positions and velocities
    by these values. The scaled values are then set into the physics simulation.
    """
    # extract the used quantities (to enable type-hinting)
    asset: Articulation = env.scene[asset_cfg.name]
    # get default joint state
    joint_pos = asset.data.default_joint_pos[env_ids].clone()
    joint_vel = asset.data.default_joint_vel[env_ids].clone()

    # scale these values randomly
    joint_pos *= math_utils.sample_uniform(*position_range, joint_pos.shape, joint_pos.device)
    joint_vel *= math_utils.sample_uniform(*velocity_range, joint_vel.shape, joint_vel.device)

    # clamp joint pos to limits
    joint_pos_limits = asset.data.soft_joint_pos_limits[env_ids]
    joint_pos = joint_pos.clamp_(joint_pos_limits[..., 0], joint_pos_limits[..., 1])
    # clamp joint vel to limits
    joint_vel_limits = asset.data.soft_joint_vel_limits[env_ids]
    joint_vel = joint_vel.clamp_(-joint_vel_limits, joint_vel_limits)

    # set into the physics simulation
    asset.write_joint_state_to_sim(joint_pos, joint_vel, env_ids=env_ids)


def reset_joints_by_offset(
    env: ManagerBasedEnv,
    env_ids: torch.Tensor,
    position_range: tuple[float, float],
    velocity_range: tuple[float, float],
    asset_cfg: SceneEntityCfg = SceneEntityCfg("robot"),
):
    """Reset the robot joints with offsets around the default position and velocity by the given ranges.

    This function samples random values from the given ranges and biases the default joint positions and velocities
    by these values. The biased values are then set into the physics simulation.
    """
    # extract the used quantities (to enable type-hinting)
    asset: Articulation = env.scene[asset_cfg.name]

    # get default joint state
    joint_pos = asset.data.default_joint_pos[env_ids].clone()
    joint_vel = asset.data.default_joint_vel[env_ids].clone()

    # bias these values randomly
    joint_pos += math_utils.sample_uniform(*position_range, joint_pos.shape, joint_pos.device)
    joint_vel += math_utils.sample_uniform(*velocity_range, joint_vel.shape, joint_vel.device)

    # clamp joint pos to limits
    joint_pos_limits = asset.data.soft_joint_pos_limits[env_ids]
    joint_pos = joint_pos.clamp_(joint_pos_limits[..., 0], joint_pos_limits[..., 1])
    # clamp joint vel to limits
    joint_vel_limits = asset.data.soft_joint_vel_limits[env_ids]
    joint_vel = joint_vel.clamp_(-joint_vel_limits, joint_vel_limits)

    # set into the physics simulation
    asset.write_joint_state_to_sim(joint_pos, joint_vel, env_ids=env_ids)