I've been doing this for 10+ years, and have seen GPU-based calculations slowly eat away at the following problems: * Mapping. Nowadays generating a dense grid

I've been doing this for 10+ years, and have seen GPU-based calculations slowly eat away at the following problems: * Mapping. Nowadays generating a dense grid of costs can be done insanely fast on GPU. There's just no excuse to not use a GPU on every robot so it can build a fast map, unless you move at snail speed. * Computer Vision. Classical depth mapping is best done on a GPU. Classical computer vision object detection has fallen away to the rise in ML-based CV for segmentation. Some (IMHO) overzealous practitioners are trying to eat away at estimation and tracking, which IMHO will recede a little since there was nothing wrong with the estimators (just Bayesian stats) to begin with, it was always the measurements. Still, for detection (and sometimes association), ML on GPU is the way to go and that will very likely not change. It has gotten so good that you can get away without using other sensors and just deploying a vision system (though I don't recommend it, but this is what Tesla does). This is an obvious case for one (or one more) GPU on every robot. * Planning - End to end planning is eating traditional planning now, similar to CV. There are some areas where this is an obvious win (e.g., complex manipulation tasks), and some areas where some overzealous overreach is happening (e.g., simpler planning tasks like routing). But ML on GPUs is here to stay for all planning tasks, especially when estimating costs from complex data, even if a classical planner uses those costs. And I'd be remiss if I didn't mention policy-based planning, which does a huge amount of training to generate essentially a fast lookup table for actions. Deployment of these types of planners often requires a very good estimator to determine what state you are in - and this is a great area for ML, mapping real world messy data to a clean state lookup. I think this can typically be done without a GPU, due to training prior to deployment, but if you have a GPU already (see prior two), you will find this is a good use of it. * Low-level planning / Controls - Shares a small overlap with above, but mostly concerned with fast responses to transient data and stabilizing the system. I've heard, but not seen directly, that learned policies are coming into vogue here. But regardless, it is a common thread that a network can assist with estimating costs and states to allow a traditional controls system to operate more reliably. I doubt this will necessitate a GPU, but like above, will gladly use it if required and available. To add to this, consider that we're generally not talking about discrete, gaming-type GPUs, we're talking about purpose built robotics-targeted embedded systems that speak native CUDA. The Jetson family, in particular.