End-to-End

End-to-end systems learn or operate across the full robotics stack — from raw input to physical action — without hand-tuned intermediate stages. Instead of separate modules for perception, planning, and control, these systems train a unified policy or model that maps sensor data directly to motor commands or task execution.

In humanoid robotics, end-to-end learning simplifies integration and enables adaptability. A robot might learn to walk, grasp, or follow spoken instructions without explicit programming for each subtask. But it comes with trade-offs, including reduced interpretability, higher data demands, and safety concerns during training or deployment.