Neural Networks for Robotics

What is it?

Neural networks are computational models inspired by biological neurons that learn from data. In robotics, they are used for perception (detecting objects), prediction (forecasting other agents), and control (mapping sensor input directly to motor commands).

How it works

A neural network is a stack of layers, each performing a weighted sum followed by a non-linearity. Weights are learned by gradient descent on labelled or self-supervised data. In robotics, common architectures include CNNs for vision, transformers for sequence modelling, and policy networks for reinforcement learning. Real-time inference typically runs on GPUs (e.g., NVIDIA Jetson) or specialised accelerators.

Real-world example

Tesla Autopilot uses a single 'HydraNet' neural network for vision. Boston Dynamics Atlas uses neural networks for object detection. Indian agritech robots use CNNs to spot diseased crops in real time.

Why it matters for robotics

Neural networks have transformed robotics in the last decade. From sim-to-real transfer to vision-language models for manipulation, neural networks are now central to almost every research advance.

See also

• Neural Network
• Deep Learning
• Computer Vision