🧠AI Fetch Interface

This document describes the interface specification for neural network models that control the Fetch mobile manipulator robot in ManiSkill/MS-HAB environments. Models following this specification can be used as drop-in replacements for the default control policies.

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Quick Reference

Component	Specification
Input	Observation (state)
Input Shape	[batch_size, state_dim] or [state_dim,]
Input Data Type	float32
Input Range	Typically [-inf, inf] or normalized [-1, 1]
Neural Network
Architecture	MLP / CNN / Transformer / etc.
Weights Format	PyTorch (.pt) / ONNX (.onnx) / Keras (.h5)
Output	Action Vector
Output Shape	[batch_size, 13] or [13,]
Output Data Type	float32
Output Range	[-1.0, 1.0] (normalized)

Overview

The Fetch robot is a mobile manipulator with:

• 7-DOF arm (shoulder, elbow, wrist joints)

• 2-finger gripper (mimic-controlled)

• 3-DOF body (head pan/tilt, torso lift)

• Mobile base (2D translation)

The control interface uses delta position control (pd_joint_delta_pos mode), where actions specify incremental changes to joint positions rather than absolute targets.

---

Model Interface

Input: Observation Space

Format: Dict[str, Array] or flattened Array

Observation Mode: state (default) or rgbd

State Observation (Recommended)

When obs_mode="state", the observation format depends on the environment:

Format 1: Flattened Tensor (most common, e.g., ReplicaCAD_SceneManipulation-v1)

observation = torch.Tensor[float32]  # Shape: (batch_size, state_dim) or (state_dim,)
# Contains: flattened robot state + task state
# Example: Shape (1, 30) for ReplicaCAD_SceneManipulation-v1

Format 2: Dictionary (some environments)

observation = {
    "agent": {
        # Robot state (shape: [state_dim])
        "qpos": Array[float32],      # Joint positions (all joints)
        "qvel": Array[float32],       # Joint velocities (all joints)
        # ... other agent-specific state
    },
    "extra": {
        # Task-specific state (object positions, etc.)
        # Shape and content depend on the environment
    },
    "sensor_param": {
        # Camera/sensor parameters
    },
    "sensor_data": {
        # Camera images (if rgbd mode)
    }
}

State Dimensions by Environment:

• ReplicaCAD_SceneManipulation-v1: 30 dimensions (flattened tensor)

• PickCube-v1: Typically 40-50 dimensions

• SequentialTask-v0: Typically 50-100 dimensions (varies by task)

Data Type: float32

Normalization: Observations may be normalized to [-1, 1] range depending on the training setup.

Model Input Handling: Models should handle both formats for maximum compatibility:

def preprocess_observation(obs):
    """Handle both tensor and dictionary observations."""
    if isinstance(obs, dict):
        # Extract and flatten dictionary
        if 'agent' in obs:
            state = obs['agent']
        else:
            # Concatenate all dict values
            state = torch.cat([v.flatten() for v in obs.values()], dim=-1)
    else:
        # Already a tensor
        state = obs
    
    # Ensure correct shape
    if len(state.shape) == 1:
        state = state.unsqueeze(0)  # Add batch dimension if needed
    
    return state

RGBD Observation (Alternative)

When obs_mode="rgbd", the observation includes camera images:

observation = {
    "agent": {...},  # Same as above
    "sensor_data": {
        "rgb": Array[uint8],      # Shape: [H, W, 3] or [N, H, W, 3]
        "depth": Array[float32],   # Shape: [H, W] or [N, H, W]
        # Camera-specific keys
    },
    # ... other keys
}

Note: For model compatibility, state observations are recommended as they are more compact and environment-agnostic.

---

Output: Action Space

Format: Array[float32]

Shape: (13,) - Fixed dimension

Range: [-1.0, 1.0] (normalized)

Action Vector Breakdown:

action = [
    a0,   # [0]  Arm: shoulder_pan_joint      (delta position)
    a1,   # [1]  Arm: shoulder_lift_joint       (delta position)
    a2,   # [2]  Arm: upperarm_roll_joint     (delta position)
    a3,   # [3]  Arm: elbow_flex_joint         (delta position)
    a4,   # [4]  Arm: forearm_roll_joint        (delta position)
    a5,   # [5]  Arm: wrist_flex_joint          (delta position)
    a6,   # [6]  Arm: wrist_roll_joint         (delta position)
    a7,   # [7]  Gripper: l_gripper_finger_joint (position, mimic-controlled)
    a8,   # [8]  Body: head_pan_joint           (delta position)
    a9,   # [9]  Body: head_tilt_joint          (delta position)
    a10,  # [10] Body: torso_lift_joint         (delta position)
    a11,  # [11] Base: root_x_axis_joint       (velocity: left/right)
    a12,  # [12] Base: root_y_axis_joint       (velocity: forward/back)
]

Detailed Action Components

Index	Component	Joint Name	Control Type	Range (normalized)	Physical Range
0-6	Arm
0	shoulder_pan_joint	Shoulder pan (left/right)	Delta position	[-1, 1]	[-0.1, 0.1] rad
1	shoulder_lift_joint	Shoulder lift (up/down)	Delta position	[-1, 1]	[-0.1, 0.1] rad
2	upperarm_roll_joint	Upper arm roll	Delta position	[-1, 1]	[-0.1, 0.1] rad
3	elbow_flex_joint	Elbow flexion	Delta position	[-1, 1]	[-0.1, 0.1] rad
4	forearm_roll_joint	Forearm roll	Delta position	[-1, 1]	[-0.1, 0.1] rad
5	wrist_flex_joint	Wrist flexion	Delta position	[-1, 1]	[-0.1, 0.1] rad
6	wrist_roll_joint	Wrist roll	Delta position	[-1, 1]	[-0.1, 0.1] rad
7	Gripper
7	l_gripper_finger_joint	Gripper open/close	Position (mimic)	[-1, 1]	[-0.01, 0.05] m
8-10	Body
8	head_pan_joint	Head pan (left/right)	Delta position	[-1, 1]	[-0.1, 0.1] rad
9	head_tilt_joint	Head tilt (up/down)	Delta position	[-1, 1]	[-0.1, 0.1] rad
10	torso_lift_joint	Torso lift	Delta position	[-1, 1]	[-0.1, 0.1] m
11-12	Base
11	root_x_axis_joint	Base translation X (left/right)	Velocity	[-1, 1]	[-1.0, 1.0] m/s
12	root_y_axis_joint	Base translation Y (forward/back)	Velocity	[-1, 1]	[-1.0, 1.0] m/s

Note: The gripper uses a mimic controller where r_gripper_finger_joint automatically mirrors l_gripper_finger_joint.

---

Model Architecture Requirements

Input Processing

The model should accept:

1. State observations (recommended):

   • Input shape: (batch_size, state_dim) where state_dim is:

     • 30 for ReplicaCAD_SceneManipulation-v1

     • 40-50 for PickCube-v1

     • 50-100 for other environments (varies)

   • Or: Dictionary with agent key containing state vector

   • Data type: float32

2. RGBD observations (optional):

   • Input shape: (batch_size, H, W, C) for images

   • May require CNN backbone (ResNet, etc.)

   • Data type: uint8 for RGB, float32 for depth

Output Processing

The model must output:

• Shape: (batch_size, 13) or (13,) for single inference

• Data type: float32

• Range: [-1.0, 1.0] (normalized actions)

• Activation: tanh is commonly used for the final layer

Example Architectures

PyTorch MLP (Minimal)

import torch
import torch.nn as nn

class FetchControlModel(nn.Module):
    def __init__(self, state_dim=30):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(state_dim, 256),
            nn.ReLU(),
            nn.Linear(256, 256),
            nn.ReLU(),
            nn.Linear(256, 256),
            nn.ReLU(),
            nn.Linear(256, 13),
            nn.Tanh()  # Ensures output in [-1, 1]
        )
    
    def forward(self, obs):
        # Handle both dict and tensor observations
        if isinstance(obs, dict):
            if 'agent' in obs:
                state = obs['agent']
            else:
                # Flatten all dict values
                state = torch.cat([v.flatten() for v in obs.values()], dim=-1)
        else:
            state = obs
        
        # Ensure correct shape
        if len(state.shape) == 1:
            state = state.unsqueeze(0)  # Add batch dimension
        
        return self.net(state)

Keras/TensorFlow (Includes Architecture)

import tensorflow as tf
from tensorflow import keras

def create_fetch_model(state_dim=30):
    """Create a Keras model for Fetch control."""
    model = keras.Sequential([
        keras.layers.Dense(256, activation='relu', input_shape=(state_dim,)),
        keras.layers.Dense(256, activation='relu'),
        keras.layers.Dense(256, activation='relu'),
        keras.layers.Dense(13, activation='tanh')  # Output in [-1, 1]
    ])
    return model

# Save model (includes architecture + weights)
model = create_fetch_model()
model.save('fetch_policy.h5')  # Can be loaded directly without architecture code

Advantage of Keras: The .h5 file contains both architecture and weights, so it can be loaded without providing the architecture definition:

# Load Keras model (no need to define architecture)
model = keras.models.load_model('fetch_policy.h5')

---

Model Weights Format

Supported Formats

Models should be saved in one of the following formats:

1. PyTorch (.pt or .pth):

   torch.save({
       'model_state_dict': model.state_dict(),
       'config': {...},  # Optional: model config
   }, 'model.pt')

2. ONNX (.onnx):

   • Standardized format, framework-agnostic

   • Can be loaded by PyTorch, TensorFlow, etc.

3. TensorFlow/Keras (.h5 or SavedModel):

   • Key Advantage: Keras models include architecture + weights in a single file

   • Can be loaded directly without defining architecture separately

   • Perfect for URL-based loading (like Keras model zoo)

   • Example: model = keras.models.load_model(url) - no architecture code needed!

4. HuggingFace Model Hub:

   • Models can be hosted and loaded via URL

   • Example: model = torch.hub.load('user/repo', 'model')

Model Metadata

The model file or repository should include metadata for proper loading and usage:

{
    "model_type": "fetch_control",
    "version": "1.0",
    "architecture": "mlp" | "cnn" | "transformer" | ...,
    "input_type": "state" | "rgbd",
    "input_dim": 30,  # For ReplicaCAD_SceneManipulation-v1, or shape for images
    "output_dim": 13,
    "control_mode": "pd_joint_delta_pos",
    "normalization": {
        "obs_mean": [0.0, 0.0, ...],  # Mean for each observation dimension
        "obs_std": [1.0, 1.0, ...]    # Std for each observation dimension
    },
    "training_config": {
        "env_id": "PickCube-v1" | "ReplicaCAD_SceneManipulation-v1" | ...,
        "algorithm": "SAC" | "PPO" | "BC" | "ACT" | ...,
        "seed": 42,
        "total_timesteps": 1000000
    },
    "framework": "pytorch" | "tensorflow" | "onnx",
    "device": "cuda" | "cpu"
}

Metadata Location:

• For PyTorch: Include in checkpoint dict or separate config.json

• For Keras: Stored in model file or config.json

• For HuggingFace: In repository root as config.json

---

Model Loading Interface

URL-Based Loading

Models should be loadable via URL or local path:

def load_fetch_model(model_url: str, device: str = "cuda"):
    """
    Load a Fetch control model from URL or local path.
    
    Args:
        model_url: URL (http/https) or local file path
        device: Device to load model on ("cuda" or "cpu")
    
    Returns:
        model: Loaded model ready for inference
        metadata: Model metadata dict
    """
    # Implementation depends on model format
    # Example for PyTorch:
    if model_url.startswith("http"):
        # Download from URL
        model_path = download_model(model_url)
    else:
        model_path = model_url
    
    # Load model
    checkpoint = torch.load(model_path, map_location=device)
    model = create_model_from_checkpoint(checkpoint)
    model.eval()
    return model, checkpoint.get('metadata', {})

Example Usage

import torch
from fetch_model_loader import load_fetch_model

# Load model from URL
model, metadata = load_fetch_model(
    "https://huggingface.co/user/fetch-policy/resolve/main/model.pt"
)

# Or from local path
model, metadata = load_fetch_model("./checkpoints/fetch_policy.pt")

# Inference
obs = env.reset()
with torch.no_grad():
    action = model(obs)  # Shape: (13,)
    action = action.clamp(-1.0, 1.0)  # Ensure valid range

env.step(action)

---

Environment Compatibility

Required Environment Settings

env = gym.make(
    env_id,  # e.g., "PickCube-v1", "SequentialTask-v0"
    num_envs=1,
    obs_mode="state",  # or "rgbd" if model supports it
    render_mode="rgb_array",
    sim_backend="gpu",  # or "cpu"
    robot_uids="fetch",
    control_mode="pd_joint_delta_pos",  # Must match!
)

Observation Preprocessing

If the model was trained with normalized observations:

def preprocess_obs(obs, metadata):
    """Normalize observation using model's statistics."""
    if isinstance(obs, dict):
        state = obs['agent']
    else:
        state = obs
    
    if 'normalization' in metadata:
        mean = metadata['normalization']['obs_mean']
        std = metadata['normalization']['obs_std']
        state = (state - mean) / (std + 1e-8)
    
    return state

---

Testing Model Compatibility

Validation Checklist

Before using a model, verify:

• Input shape matches: Model accepts observation of correct dimension

• Output shape is (13,): Model outputs 13-dimensional action vector

• Output range is [-1, 1]: Actions are properly normalized

• Control mode matches: Model trained with pd_joint_delta_pos

• Observation mode matches: Model trained with state or rgbd as specified

• Framework compatibility: Model can be loaded in your environment

Test Script

def test_model_compatibility(model, env):
    """Test if model is compatible with environment."""
    obs, _ = env.reset()
    
    # Test single observation
    with torch.no_grad():
        action = model(obs)
    
    # Validate output
    assert action.shape == (13,), f"Expected shape (13,), got {action.shape}"
    assert action.min() >= -1.0 and action.max() <= 1.0, \
        f"Actions must be in [-1, 1], got range [{action.min()}, {action.max()}]"
    
    # Test step
    obs, reward, done, truncated, info = env.step(action)
    print("✅ Model is compatible!")
    return True

---

Example Model Repositories

Format for HuggingFace

fetch-policy/
├── model.pt              # PyTorch weights
├── config.json           # Model metadata
├── README.md            # Documentation
└── requirements.txt     # Dependencies

Format for Local Storage

models/
├── fetch_policy_v1.pt
├── fetch_policy_v1.json  # Metadata
└── fetch_policy_v1_README.md

---

Model Loading from URL

Supported URL Formats

Models can be loaded from:

1. HuggingFace Model Hub:

   model_url = "https://huggingface.co/user/fetch-policy/resolve/main/model.pt"

2. Direct HTTP/HTTPS URLs:

   model_url = "https://example.com/models/fetch_policy.pt"

3. Local file paths:

   model_url = "./checkpoints/fetch_policy.pt"
   model_url = "/path/to/model.pt"

Implementation Example

import torch
import urllib.request
from pathlib import Path

def load_model_from_url(model_url: str, device: str = "cuda"):
    """
    Load a Fetch control model from URL or local path.
    
    Supports:
    - HuggingFace model URLs
    - Direct HTTP/HTTPS URLs
    - Local file paths
    
    Args:
        model_url: URL or local path to model file
        device: Device to load model on ("cuda" or "cpu")
    
    Returns:
        model: Loaded model ready for inference
        metadata: Model metadata dict (if available)
    """
    # Download if URL
    if model_url.startswith("http"):
        print(f"Downloading model from {model_url}...")
        model_path = Path("/tmp/fetch_model.pt")
        urllib.request.urlretrieve(model_url, model_path)
    else:
        model_path = Path(model_url)
    
    if not model_path.exists():
        raise FileNotFoundError(f"Model not found: {model_url}")
    
    # Load checkpoint
    checkpoint = torch.load(model_path, map_location=device)
    
    # Extract model state and metadata
    if isinstance(checkpoint, dict):
        state_dict = checkpoint.get('model_state_dict', checkpoint.get('state_dict', checkpoint))
        metadata = checkpoint.get('metadata', {})
    else:
        state_dict = checkpoint
        metadata = {}
    
    # Create model architecture (user must provide)
    # This is framework-specific
    model = create_model_architecture(metadata)  # User implements this
    model.load_state_dict(state_dict)
    model.eval()
    model.to(device)
    
    return model, metadata

Keras/TensorFlow Models

For Keras models (which include architecture + weights in a single file):

import tensorflow as tf
from tensorflow import keras
import urllib.request
from pathlib import Path

def load_keras_model_from_url(model_url: str):
    """
    Load Keras model from URL.
    
    Keras models (.h5 or SavedModel) include both architecture and weights,
    so no need to specify architecture separately - this is the key advantage!
    
    Args:
        model_url: URL or local path to .h5 or SavedModel directory
    
    Returns:
        model: Loaded Keras model ready for inference
    """
    if model_url.startswith("http"):
        # Download using Keras utility or manual download
        print(f"Downloading model from {model_url}...")
        model_path = tf.keras.utils.get_file(
            "fetch_model.h5",
            model_url,
            cache_subdir="models",
            cache_dir="/tmp"
        )
    else:
        model_path = model_url
    
    if not Path(model_path).exists():
        raise FileNotFoundError(f"Model not found: {model_url}")
    
    # Keras automatically loads architecture + weights
    # This is why Keras is convenient - no need to define architecture!
    model = keras.models.load_model(model_path)
    
    print(f"✅ Model loaded from {model_path}")
    print(f"   Input shape: {model.input_shape}")
    print(f"   Output shape: {model.output_shape}")
    
    return model

# Usage
model = load_keras_model_from_url("https://example.com/models/fetch_policy.h5")
# Or from HuggingFace (if they support direct .h5 links)
model = load_keras_model_from_url("https://huggingface.co/user/fetch-policy/resolve/main/model.h5")

Key Advantage of Keras: The model file is self-contained - it includes:

• Architecture definition (layers, connections)

• Weights (trained parameters)

• Optimizer state (optional)

• Training configuration (optional)

This means you can load a Keras model with just:

model = keras.models.load_model(url_or_path)

No need to define the architecture separately!

---

Summary

Component	Specification
Input	Observation dict or flattened array, shape (batch_size, state_dim) or (state_dim,)
Input Dimension	Typically 30-100 (varies by environment)
Output	Action array, shape (13,), range [-1.0, 1.0], dtype float32
Control Mode	pd_joint_delta_pos (required)
Robot	Fetch mobile manipulator
Action Components	7 arm joints + 1 gripper + 3 body joints + 2 base velocities
Model Format	PyTorch (.pt), ONNX (.onnx), or Keras (.h5)
Loading	URL (HTTP/HTTPS/HuggingFace) or local path supported
Architecture	User-defined (MLP, CNN, Transformer, etc.)

---

Action Vector Visualization

Action Vector (13 dimensions)

Index	Component	Joint Name	Control Type	Description
0-6	Arm (7 DOF)
0	Shoulder Pan	shoulder_pan_joint	Δpos	Shoulder rotation (left/right)
1	Shoulder Lift	shoulder_lift_joint	Δpos	Shoulder elevation (up/down)
2	Upper Arm Roll	upperarm_roll_joint	Δpos	Upper arm rotation
3	Elbow Flex	elbow_flex_joint	Δpos	Elbow flexion
4	Forearm Roll	forearm_roll_joint	Δpos	Forearm rotation
5	Wrist Flex	wrist_flex_joint	Δpos	Wrist flexion
6	Wrist Roll	wrist_roll_joint	Δpos	Wrist rotation
7	Gripper
7	Gripper	l_gripper_finger_joint	pos	Gripper open/close (mimic-controlled)
8-10	Body (3 DOF)
8	Head Pan	head_pan_joint	Δpos	Head rotation (left/right)
9	Head Tilt	head_tilt_joint	Δpos	Head tilt (up/down)
10	Torso Lift	torso_lift_joint	Δpos	Torso vertical movement
11-12	Base (2 DOF)
11	Base X	root_x_axis_joint	vel	Base translation X (left/right)
12	Base Y	root_y_axis_joint	vel	Base translation Y (forward/back)

Legend:

• Δpos = Delta position (incremental change from current position)

• pos = Absolute position target

• vel = Velocity control

---

References

• ManiSkill Documentation

• MS-HAB Repository

• Fetch Robot URDF: Included in ManiSkill assets

• Control Modes: See mani_skill/agents/robots/fetch/fetch.py