Teaching Robots to Make Decisions
Or why giving a robot a goal is sometimes more interesting than telling it what to do.
There is something fascinating about watching a robot learn.
You give it a relatively simple task, such as picking up an object. A human would look at it, reach out, and get on with the job.
A robot, on the other hand, might spend the next six hours waving its arms, knocking things over, and discovering entirely new ways of embarrassing itself.
And yet, eventually, it learns.
Behind this rather entertaining process sits a mathematical framework called a Markov Decision Process (MDP).
Life is a series of questionable decisions
Imagine leaving your house in the Netherlands. The weather forecast promises sunshine. Naturally, it’s raining. You need to get to the supermarket. You could walk, cycle, drive, or abandon the expedition entirely.
Each decision has consequences. Cycling is quicker, walking is healthier, driving keeps you dry, and staying home means dinner might consist of half an onion and some suspicious cheese.
You’re evaluating your situation, considering possible actions, and anticipating their consequences. Congratulations. You’re already thinking in terms of a MDP (Markov Decision Process).
Mathematically, an MDP consists of five ingredients:
MDP = (S, A, P, R, gamma) | Element | Meaning |
|---|---|
| State (S) | Where am I, and what’s happening? |
| Action (A) | What can I do? |
| Transition (P) | What happens if I do it? |
| Reward (R) | Was that a good decision? |
| Discount (γ) | How much do future consequences matter? |
The Markov part introduces one important assumption: if the present state contains all relevant information, we don’t need the entire history to decide what happens next. Rather convenient, considering how much time humans spend regretting previous decisions.
Teaching without giving instructions
This is where reinforcement learning becomes interesting.
Traditionally, engineers describe how a system should behave. We define procedures, conditions, and expected outcomes. With reinforcement learning, we take a different approach:
We define what success looks like and let the system discover how to achieve
it.Consider a robotic arm learning to pick up an object. We could reward it only when the task succeeds. Unfortunately, the robot might spend millions of attempts doing absolutely nothing useful.
Instead, we can reward meaningful progress:
- approaching the object
- grasping it
- lifting it
- placing it correctly.
We’re providing encouragement rather than explicit instructions. But there’s a catch.
Robots are exceptionally good at finding loopholes. Reward movement, and your robot might discover that vibrating enthusiastically produces excellent results.
Technically, it has done precisely what you asked. Unfortunately, what you asked and what you wanted were two entirely different things.
Something anyone who has managed an engineering project will probably recognise.
Where Isaac Lab comes in
In NVIDIA Isaac Lab, these mathematical concepts become practical engineering decisions. We define what the robot observes, which actions it can perform, and how its behavior is rewarded.
Isaac Sim provides the physics, determining what happens when the robot interacts with its environment. A reinforcement learning algorithm then searches for a strategy, called a policy, that maximises the expected accumulated reward.
The interesting part is that we don’t need to explicitly program every movement. We establish the environment, define the objectives, and create the conditions for learning. But this also introduces responsibility.
Poorly designed observations, misleading rewards, or unrealistic simulation conditions can produce a robot that performs brilliantly in training while being spectacularly useless everywhere else.
The engineering lesson
By working with complex technical systems, I’ve learned that defining the right problem is often more important than choosing the technology used to solve it. Reinforcement learning illustrates this rather beautifully.
The mathematics provides the framework. Simulation provides the environment. Algorithms provide the learning mechanism. But the engineer must still decide what success means. And that requires understanding the problem, anticipating unintended consequences, and recognising when a technically correct result isn’t actually the result we wanted.
In that sense, robotics isn’t so different from leading an engineering team. You establish objectives, provide the necessary resources, create an environment where progress is possible, and continuously evaluate the results.
The difference is that when a robot makes a terrible decision, I can reset the simulation. With people, that approach might be considerably less popular.