1. Overview#

Robomimic supports structured action spaces where different action components can be treated differently. This is particularly useful for:

• Robot manipulation tasks with different action components (e.g., end-effector position and rotation)

• Actions that require different normalization schemes

• Combining multiple action outputs with different physical meanings

2. Action Configuration Structure#

The action configuration consists of two main components:

1. action_keys: List of action components to use

2. action_config: Dictionary specifying how each action component should be processed

Here’s the basic structure:

config.train.action_keys = ["action/eef_pos", "action/eef_rot"]  # order matters!

config.train.action_config = {
    "action/eef_pos": {
        "normalization": "min_max",    # normalize to [-1, 1]
        "rot_conversion": None         # no rotation conversion
    },
    "action/eef_rot": {
        "normalization": None,         # no normalization
        "rot_conversion": "axis_angle_to_6d"  # convert rotation format
    }
}

3. Supported Normalization Methods#

Robomimic supports several normalization methods for action components:

1. None: No normalization

   • Uses unit scale and zero offset

   • Useful when actions are already in desired range

2. "min_max": Min-max normalization

   • Scales actions to range [-1, 1]

   • Useful for bounded action components like positions

   • Handles numerical stability with small ranges

   "normalization": "min_max"
   

3. "gaussian": Gaussian normalization

   • Normalizes to zero mean and unit variance

   • Useful for unbounded action components

   "normalization": "gaussian"
   

4. Example Configurations#

Here are some common use cases:

# Configure end-effector position and rotation actions
config.train.action_keys = ["action/eef_pos", "action/eef_rot"]
config.train.action_config = {
    "action/eef_pos": {
        "normalization": "min_max",     # normalize position to [-1, 1]
    },
    "action/eef_rot": {
        "normalization": "gaussian",    # normalize rotation with zero mean, unit variance
    }
}

# Configure position, rotation, and gripper actions
config.train.action_keys = ["action/eef_pos", "action/eef_rot", "action/gripper"]
config.train.action_config = {
    "action/eef_pos": {
        "normalization": "min_max",
    },
    "action/eef_rot": {
        "normalization": "gaussian",
    },
    "action/gripper": {
        "normalization": None,          # gripper already in [-1, 1]
    }
}

5. Best Practices#

1. Action Component Order:

   • Order in action_keys determines concatenation order

   • Keep order consistent across training and deployment

   • Document the expected order in your configs

2. Normalization Selection:

   • Use min_max for bounded values (e.g., positions, normalized vectors)

   • Use gaussian for unbounded values or when distribution matters

   • Use None when values are already properly scaled

3. Numerical Stability:

   • In min_max implementation, ranges smaller than 1e-4 are not scaled

   • In gaussian implementation, distributions with stddev smaller than 1e-6 are not scaled

4. Diffusion Policy Compatibility:

   • When using Diffusion Policy, ensure actions are normalized to [-1, 1]

   • Use min_max normalization or pre-normalize your data

   • The policy will check if actions are in the correct range

6. Implementation Details#

The normalization process:

1. Computes statistics (min, max, mean, std) across the entire dataset

2. Applies the specified normalization method to each action component

3. Concatenates the normalized components in the order specified by action_keys

For min_max normalization:

# Normalizes to range [-0.999999, 0.999999] for numerical stability
scale = (input_max - input_min) / (output_max - output_min)
offset = input_min - scale * output_min
normalized_action = (raw_action - offset) / scale

For gaussian normalization:

# Normalizes to zero mean, unit variance
normalized_action = (raw_action - mean) / (std + epsilon)