Neural Networks for Robotics

Conceptual

Neural Networks are computational models inspired by biological neurons that learn patterns from data. In robotics, they power perception (object detection, depth estimation), decision-making (policy learning), and increasingly, end-to-end control.

Why Neural Networks for Robots?

Traditional robotics relied on hand-crafted algorithms. Neural networks excel when:

Input is high-dimensional — cameras, LiDAR produce millions of data points
Patterns are complex — object recognition, natural language
Rules are hard to specify — dexterous manipulation, social navigation
Adaptation is needed — handle novel situations not seen during programming

Key Architectures

Convolutional Neural Networks (CNNs)

Process spatial data like images. Used for:

Object detection (YOLO, Faster R-CNN)
Semantic segmentation (U-Net, DeepLab)
Depth estimation (MiDaS, DepthAnything)

Image → [Conv] → [Conv] → [Pool] → [FC] → Detection
         ↓        ↓
     Features  Features

Transformers

Attention-based architecture. Originally for language, now dominant in:

Vision (ViT, DINO)
Multi-modal models (VLMs)
Robot foundation models (RT-2, GR00T)

Input Tokens → [Self-Attention] → [FFN] → Output
                    ↓
              "What's relevant?"

Recurrent Networks (RNN, LSTM)

Process sequences. Used for:

Trajectory prediction
Language understanding
Time-series sensor data

Increasingly replaced by Transformers.

Neural Networks in the Robot Stack

┌─────────────────────────────────────────────────┐
│                Perception                        │
│  CNN: Object detection, segmentation            │
│  ViT: Scene understanding                       │
│  Depth networks: 3D reconstruction              │
├─────────────────────────────────────────────────┤
│              Understanding                       │
│  VLM: "What do I see?" + "What should I do?"   │
│  LLM: Task planning from language              │
├─────────────────────────────────────────────────┤
│                 Policy                           │
│  Imitation learning: Learn from demonstrations  │
│  RL: Learn from trial and error                │
│  VLA: Vision-Language-Action models            │
├─────────────────────────────────────────────────┤
│                Control                           │
│  Neural network controllers                     │
│  Residual learning on top of classical control │
└─────────────────────────────────────────────────┘

Training Paradigms

Supervised Learning

Learn from labeled examples: (input, correct_output) pairs

Robotics use: Object detection, segmentation, depth estimation

Reinforcement Learning (RL)

Learn from rewards: trial and error

Robotics use: Locomotion, manipulation, game playing

Imitation Learning

Learn from demonstrations: watch expert, mimic behavior

Robotics use: Manipulation tasks, driving

Self-Supervised Learning

Learn from data structure itself: no labels needed

Robotics use: Pre-training visual representations (DINO, MAE)

Deployment Considerations

Inference Speed

Real-time robotics requires fast inference:

Hardware	Typical Inference
CPU only	100ms+
GPU (desktop)	5-20ms
Jetson Orin	10-30ms
Jetson Thor	5-15ms
TensorRT optimized	2-10ms

Model Optimization

TensorRT (NVIDIA) — Optimizes models for Jetson/GPU:

Layer fusion
Precision reduction (FP32 → FP16 → INT8)
Kernel auto-tuning

# Convert PyTorch model to TensorRT
trtexec --onnx=model.onnx --saveEngine=model.trt --fp16

Edge vs Cloud

Edge (Jetson): Low latency, works offline, limited compute
Cloud: Unlimited compute, high latency, requires connectivity

Most robots use edge inference for real-time control, cloud for heavy lifting.

Foundation Models for Robotics

Large models pre-trained on massive data, fine-tuned for robotics:

Model	Type	Use Case
RT-2	VLA	Manipulation from language
GR00T N1.6	VLA	Humanoid full-body control
OpenVLA	VLA	Generalizable manipulation (open-weight)
PaLM-E	VLM	Embodied reasoning
Octo	Policy	Generalizable manipulation

These run efficiently on Jetson Thor with Transformer Engine.

Reinforcement Learning — Learning from rewards
Isaac ROS — GPU-accelerated inference
Jetson Thor — Hardware for foundation models

Sources

NVIDIA GR00T N1 Blog — GR00T N1.6 architecture and capabilities
GR00T N1.6 Sim-to-Real Blog — GR00T N1.6 with Cosmos Reason integration
OpenVLA Paper — Open-source 7B VLA model architecture
RT-2 Blog — Google DeepMind VLA model
Octo Paper — Generalizable manipulation policy
TensorRT SDK — Model optimization for inference
NVIDIA Jetson Modules — Hardware specifications