Design Lessons Learned
Draft
July 02, 2021
Introduction
This article builds from the example given in the project page, where road repair crews use robots to identify the areas most in need of repair. Check out that page for user descriptions, workflows, and example data.
Planning Robot Tasks
Show Robot Intent
In the planning stage, giving the planner a way to see and review what will happen will refine the plan and inspire trust and confidence. Showing system intent is like giving the planner a peek into the future.
Visualize Robot Constraints
Occasionally, there are hard limits to operational possibilities, such as how far a battery-powered, street traversing robot can travel. Show this upfront to let the planner get it right the first time.
Visualising the Data Coming Back
Accuracy in perception is hard
Taking the more challenging path of perceptual honesty not only takes more time but can easily be overlooked.
When using GPS coordinates to visualize robot location, for example, using the coordinate “straight out of the box” falsely suggests a location at maximum precision and accuracy. To correctly manage perception, build in a way to visualize error along with the data.
Point the way without getting in the way
The product team may have spent many iterations refining the pipeline to turn raw data into a data product for the data analyst. For example, it could be an algorithm that ingests all the robot data and produces regions with the most serious potholes in a city.
In those circumstances, it’s natural to lose sight of how the user will consume the data product. But unfortunately, putting too much emphasis on the data product can obscure the actual answers the analyst seeks!
In other words, although our focus is creating those polygons, we should not let that detract from the analyst’s focus.
That’s It, for Now
I’ll update this page as more thoughts continue to crystallize, but that’s it for now!