a common example from my robotics experience (mainly mobile robots) has been getting something powerful enough to run our image recognition/interpreting sensor data. We often have something like several microprocessors (think:arduino equivalent running c++ or c) which run all the motor control etc and a high level system (used to often be raspberry pi, now more often nvidia jetson nano) listening to all of those and using most of it's computing power on some kind of sensor data, usually image recognition or processing TOF camera/lidar/radar data etc. We often have to optimise hard to get a couple of cycles or "frames" per second with these, which really puts limitations on how robots respond (250ms delay is veeeery noticable, especially if it's in obstacle avoidance - relatively common)

I don't work in the field but just to kind of put it into perspective, a 12v 100A LiFePO4 battery has 1200 Watts capacity and weighs 30 pounds. A typical gaming PC (which to be fair, is more willing to trade power for performance) consumes about 600 Watts per hour. Problem for a Tesla? Not so much. Problem for a lightweight drone? Definitely.

The NVIDIA Jetson boards are popular, but even with a full desktop processor and state of the art GPU, you can easily down them in data from a LIDAR sensor or a few cameras. Especially since robots may also need fast response times.

There is another reply to your comment that shares a lot of what I have experienced. You have so many pieces of code that need to run and a good handful of them are working on something like LIDAR point clouds with a million 3D points in them, plus some cameras running several different image recognition and segmentation algorithms, and you want to have fast cycle times, it just all adds up. Every serious robot I have ever worked on is maxing out its system, even ones at Google X with a full desktop CPU, a high end NVIDIA graphics card, and a couple secondary ARM CPUs.