Episode 15: Adversarial Approaches to Sim2Real

Shaoqing Tan
Thu, 31 Jul 2025 07:02:00 GMT

Welcome back to our series on the Sim2Real challenge. In our last episode, we explored Domain Randomization, a technique where we embrace chaos in our simulations to build robust robots. We learned that by training on a wide variety of simulated conditions, we can create policies that are less sensitive to the "reality gap."

But what if, instead of preparing for a broad range of possibilities, we could prepare for the worst-case scenario? What if we could find the "chinks in the armor" of our policy and patch them up before they're ever exploited in the real world? This is the core idea behind our topic today: Adversarial Reinforcement Learning.

If Domain Randomization is like learning to catch by practicing with all sorts of different balls, Adversarial Reinforcement Learning (ARL) is like having a dedicated sparring partner whose only goal is to make you fail. This sparring partner knows all your weaknesses and will exploit them mercilessly. It's a tough way to train, but it forces you to become incredibly resilient.

In ARL, we frame the learning problem as a zero-sum game between two agents: a protagonist and an adversary. The protagonist is our robot's policy, trying to complete its task. The adversary's goal is to make the protagonist fail. This adversarial dance forces the protagonist to learn a policy that is robust not just to random noise, but to intelligent, targeted attacks.

So, how can an adversary "attack" a robot in a simulation? There are a few different ways.

First, the adversary can apply forces to the robot, simulating unexpected disturbances. Imagine a drone trying to fly a precise trajectory. The adversary could apply a sudden gust of wind at the exact moment the drone is most vulnerable, trying to knock it off course.

Second, the adversary can manipulate the robot's observations. It could add noise to the robot's camera feed, or even create optical illusions to trick the robot into making a mistake.

Third, and perhaps most insidiously, the adversary can alter the dynamics of the environment itself. It could change the friction of a surface right as the robot is about to put its foot down, or change the mass of an object as the robot is trying to pick it up.

This adversarial setup has deep connections to the field of robust control theory. In fact, ARL can be seen as a way to solve a classic problem in robust control called the H-infinity control problem. The goal of H-infinity control is to design a controller that is robust to the worst-case disturbance. By training against an adversary, we are essentially searching for that worst-case disturbance and forcing our policy to be robust to it.

But training an adversarial policy is a delicate balancing act. If the adversary is too powerful, it can make it impossible for the protagonist to learn anything at all. It's like trying to learn to box by stepping into the ring with a heavyweight champion on your first day. To get around this, researchers often use a curriculum approach, where the adversary's strength is gradually increased as the protagonist gets better.

Adversarial Reinforcement Learning is a powerful technique for forging hyper-robust policies that can withstand the harsh and unpredictable nature of the real world. By training our robots against a dedicated adversary, we can find and fix their weaknesses before they ever cause a problem.

So far in our journey, we've focused on building a single, super-robust policy from scratch. But what if there's another way? What if, instead of trying to build a policy that works perfectly everywhere, we could build a policy that can quickly adapt to new situations?

In our next episode, we'll explore the exciting world of Transfer Learning and Meta-Learning, where we'll learn how to take knowledge from the simulated world and apply it to the real world in a much more flexible and efficient way.