Neural Networks

Neural Networks

Neural networks are the core computational structures that enable humanoid robots to see, move, understand, and adapt. Inspired by biological neurons, they learn patterns and behaviors from data — whether through pretraining or real-world interaction.

In humanoids, convolutional networks process vision; transformer and recurrent architectures handle joint feedback, audio, and time-dependent signals. Some networks translate tactile input into force estimates or generate motion plans from language prompts.

A major trend is multimodal fusion: training networks that integrate visual, inertial, and proprioceptive data to produce coordinated actions. These models increasingly span perception, reasoning, and control in a single architecture.

Neural policies now run on edge devices thanks to pruning, quantization, and specialized hardware. This enables humanoids to adapt in real time without relying solely on cloud-based inference.