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Chapter 1: Deployment Strategies

Learning Objectives​

  • Deploy VLA models on edge devices
  • Optimize inference for real-time control
  • Implement model quantization and pruning
  • Monitor deployed models in production

Introduction​

Deploying VLAs on robots requires:

  • Low latency: <100ms for reactive control
  • Efficiency: Run on limited compute (Jetson, edge TPU)
  • Reliability: Handle failures gracefully

Model Optimization​

Quantization​

import torch

# Chapter 1: Load full-precision model
model = RT2ForConditionalGeneration.from_pretrained("google/rt-2-base")

# Chapter 1: Quantize to INT8
quantized_model = torch.quantization.quantize_dynamic(
    model,
    {torch.nn.Linear},
    dtype=torch.qint8
)

# Chapter 1: Save quantized model
torch.save(quantized_model.state_dict(), "rt2_int8.pth")

# Chapter 1: Inference speedup: 2-4x, model size: 4x smaller

Pruning​

import torch.nn.utils.prune as prune

# Chapter 1: Prune 30% of weights
for module in model.modules():
    if isinstance(module, torch.nn.Linear):
        prune.l1_unstructured(module, name='weight', amount=0.3)

# Chapter 1: Make pruning permanent
for module in model.modules():
    if isinstance(module, torch.nn.Linear):
        prune.remove(module, 'weight')

Edge Deployment​

NVIDIA Jetson​

# Chapter 1: Convert to TensorRT
import tensorrt as trt

# Chapter 1: Build TensorRT engine
with trt.Builder(TRT_LOGGER) as builder:
    network = builder.create_network()
    # ... build network from ONNX ...
    engine = builder.build_cuda_engine(network)

# Chapter 1: Inference
with engine.create_execution_context() as context:
    # Allocate buffers
    inputs, outputs, bindings = allocate_buffers(engine)
    
    # Run inference
    context.execute_v2(bindings=bindings)

Google Coral TPU​

from pycoral.utils import edgetpu
from pycoral.adapters import common

# Chapter 1: Load TPU model
interpreter = edgetpu.make_interpreter("model_edgetpu.tflite")
interpreter.allocate_tensors()

# Chapter 1: Inference
common.set_input(interpreter, input_data)
interpreter.invoke()
output = common.output_tensor(interpreter, 0)

Real-Time Control Loop​

class RealtimeVLAController(Node):
    def __init__(self):
        super().__init__('vla_controller')
        self.model = load_optimized_model()
        self.control_rate = 10  # Hz
        
    def control_loop(self):
        rate = self.create_rate(self.control_rate)
        
        while rclpy.ok():
            start_time = time.time()
            
            # Get observation
            obs = self.get_observation()
            
            # Inference
            with torch.no_grad():
                action = self.model(obs)
            
            # Send command
            self.robot.execute(action)
            
            # Check latency
            latency = time.time() - start_time
            if latency > 0.1:  # 100ms
                self.get_logger().warn(f"High latency: {latency*1000:.1f}ms")
            
            rate.sleep()

Monitoring and Logging​

class ModelMonitor:
    def __init__(self):
        self.metrics = {
            'inference_time': [],
            'success_rate': [],
            'failure_modes': {},
        }
    
    def log_inference(self, obs, action, result):
        # Log inference time
        self.metrics['inference_time'].append(result.latency)
        
        # Log success/failure
        if result.success:
            self.metrics['success_rate'].append(1.0)
        else:
            self.metrics['success_rate'].append(0.0)
            
            # Track failure mode
            mode = result.failure_mode
            self.metrics['failure_modes'][mode] = \
                self.metrics['failure_modes'].get(mode, 0) + 1
    
    def publish_diagnostics(self):
        avg_latency = np.mean(self.metrics['inference_time'])
        success_rate = np.mean(self.metrics['success_rate'])
        
        self.get_logger().info(
            f"Latency: {avg_latency*1000:.1f}ms, "
            f"Success: {success_rate*100:.1f}%"
        )

Fallback Strategies​

class RobustVLAController:
    def predict_with_fallback(self, obs):
        try:
            # Try VLA model
            action = self.vla_model(obs)
            
            # Validate action
            if self.is_valid_action(action):
                return action
            else:
                raise ValueError("Invalid action")
                
        except Exception as e:
            self.get_logger().warn(f"VLA failed: {e}, using fallback")
            
            # Fallback to scripted policy
            return self.scripted_policy(obs)

Key Takeaways​

✅ Quantization reduces model size and speeds up inference
✅ Edge deployment enables on-robot inference
✅ Real-time control requires <100ms latency
✅ Monitoring tracks performance in production
✅ Fallbacks ensure safety when models fail

Course Summary​

Congratulations! You've completed Physical AI & Humanoid Robotics. You now understand:

Chapter 1: Physical AI foundations, sensors, humanoid landscape
Chapter 2: ROS 2 architecture, nodes, packages, launch files
Chapter 3: Gazebo simulation, URDF, ROS 2 integration
Chapter 4: NVIDIA Isaac Sim, Isaac Gym, synthetic data
Chapter 5: Locomotion, manipulation, whole-body control, deployment
Chapter 6: VLAs, multimodal integration, deployment strategies

Next Steps:

  • Build your own humanoid robot project
  • Contribute to open-source robotics
  • Join the Physical AI community
  • Continue learning with advanced courses

Thank you for learning with us! 🤖

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