Battery

Battery design defines how long a humanoid robot can remain useful, mobile, and safe. Runtime depends not only on battery capacity, but also on how the robot moves, senses, and computes. Every additional joint, sensor, or onboard model increases power demand. Managing that demand is critical for deploying robots outside controlled environments.

Efficiency depends on tight coordination between hardware and software. Locomotion speed, joint torque, thermal load, and neural inference all contribute to energy consumption. Strategies such as model quantization, batch size tuning, and adaptive power modes help reduce drain without sacrificing real-time performance. 

One recent study of LLM inference on edge accelerators shows how energy draw varies with model configuration and runtime choices — highlighting trade-offs that shape battery-constrained deployment.

Battery life is no longer a passive specification, it is a dynamic engineering target shaped by the robot’s entire computational and mechanical behavior.