When we think of the Skynet scenario, videos of increasingly nimble anthropomorphic machines from Boston Dynamics awe, or terrify, us. Just a couple years ago these humanoids developed an ability to get up when we knocked them down. Then, they began parkour-ing with finesse around our man-made obstacles. Now, they’re elegantly out-performing most humans in gymnastics. It all portrays a deceiving story of the rapid evolution of intelligence in robots. But in reality, these robots are still far from possessing the intelligence to fold our laundry, let alone become our overlords.

There’s a lot of hype playing into the robot takeover narrative. The purpose of this blog post is to present some exciting breakthroughs in robotics research while debunking fact from fiction.

Robots have been around and widely used in manufacturing since the 1960’s. While we’ve been calling them robots for all this time, a more fitting name would be ‘reprogrammable motion machines’. They are explicitly programmed to repeat trajectories exactly the same way, every time. They lack the intelligence to self-adapt if their environment or task changes even the slightest.

Now fast forward to today — almost nothing has changed. Nearly all deployed robot arms are still not intelligent and are confined to highly structured manufacturing environments. Yet, if we look beyond the walls of factories, the world is full of monotonous tasks across logistics, delivery, farming, construction, and transportation which are prime for automation. The reason many of these labor intensive jobs are not yet automated is because they inherently bear an enormous amount of variability — the achilles heel of robots.

One example of a repetitive yet enormously varying task is ‘picking and packing’ in eCommerce fulfillment centers. This job requires proper handling of millions of different products — all with varying sizes, shapes, weights, colors, textures, stiffnesses and fragilities. There isn’t a one-grasp-fits-all solution to handle any object. As humans, we take our innate ability to grasp, assemble, disassemble, reorient, fold, pack and generally manipulate any object for granted. For robots, this is very hard.

In addition to lacking general intelligence, robots are still very expensive. The mainstream arms from UR, Kuka, Franka, Yaskawa, Fanuc, and ABB start at $20k and can easily cost over $100k.

The inability to handle variability along with a high price tag makes it difficult to justify the economics of most robotic applications — this is the reason many robotic startups fail. If you replace a burger flipper in a fast-food restaurant with a robot, you are not replacing one employee. You’re replacing a lot less. In one minute a person can be flipping burgers. When they’re not flipping burgers they can be making fries, wiping tables, cleaning bathrooms or taking orders. Replacing a small fraction of a minimum wage employee is not financially compelling, especially given the cost and practical complexity of implementing such a piece of technology. A lot of robot applications suffer from this dilemma.

Building a viable robotics use-case

If your goal is to build a valuable robot application and deploy it successfully in the real world today, then my recommendation is to consider the following:

1. The price you charge your customer should be a fraction of the total cost of labor you are replacing over some reasonable period of time (usually no more than 2 years). Alternatively, the demand for the labor your robot can perform should be extremely high, to the point where the pool of available human labor is not willing or able to provide the total labor needed.

2. Robots aren’t people. Retro-fitting an environment designed for humans with robots will ALWAYS be less optimal, in the long-run, than designing the environment around the robot’s capabilities. Robots love structure, so give them as much structure as possible so long as it does not impose unreasonable cost or additional labor on the customer. In the same vein, we should not design robots to exactly mimic things we find in nature. Just because humans do things a certain way doesn’t mean there isn’t a simpler, more optimal solution enabled by modern engineering. See Rubik’s cube example…