robomimic/docs/tutorials/action_configs.md

Action Configuration

This tutorial shows how to configure action spaces and normalization in robomimic, particularly useful for tasks with structured action spaces like robot manipulation.

Note: Understand how to launch training runs first!

Before diving into action configuration, it might be useful to read the following tutorials:

• how to launch training runs
• how to view training results

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]
    },
    "action/eef_rot": {
        "normalization": None,                  # no normalization
        "format": "rot_6d",                     # rotation format in dataset / prediction
        "convert_at_runtime": "rot_axis_angle"  # convert rotation format at runtime
    }
}

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. Supported Rotation Conversions

Currently, robomimic supports converting 6D rotations (as proposed in this paper) to either axis-angle or Euler formats during rollouts. Below are example action configurations that convert the action key action/eef_rot from 6D to each format:

1. 6D to axis-angle

config.train.action_config = {
    "action/eef_rot": {
        "normalization": None,                  # no normalization
        "format": "rot_6d",                     # 6D rotation format in dataset / prediction
        "convert_at_runtime": "rot_axis_angle"  # convert rotation format to axis-angle at runtime
    }
}

2. 6D to axis-angle

config.train.action_config = {
    "action/eef_rot": {
        "normalization": None,                  # no normalization
        "format": "rot_6d",                     # 6D rotation format in dataset / prediction
        "convert_at_runtime": "rot_euler"       # convert rotation format to Euler at runtime
    }
}

If "convert_at_runtime" is not provided, 6D actions are converted to axis-angle format by default.

5. Example Configurations

Here are some common use cases:

5.1 Robot End-Effector Control

# 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
    }
}

5.2 Mixed Action Spaces

# 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]
    }
}

6. 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

7. 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)