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.