Smarter Motion Control: Physics Meets Adaptation with Sym2Real

Sym2Real is revolutionizing motion control by blending the best of both worlds: symbolic physics-based models and neural network adaptability. Starting in simple simulations, it extracts core equations of motion—then fine-tunes them in the real world with a lightweight neural "residual" to handle noise, payload changes, and dynamic conditions. From quadrotors braving wind gusts to racecars drifting with precision, this hybrid system delivers robust, low-level control with minimal data and maximum efficiency. It's not just control—it’s adaptive, physics-aware motion mastery.

Transcript

00:00:00 You've got a robot in your hand. Now it's time to bring it to life. But this is only possible if the model behind it understands its dynamics. Learning those dynamics directly in the real world is slow, costly, and sometimes dangerous. Instead, Sim toreal starts in simulation. Not a highfidelity fine-tuned simulator, just a simple one

00:00:34 with its own default settings. From that, it extracts equations, compact symbolic expressions that capture the underlying physics. Why? Because no matter the fidelity of the simulator, the fundamental physics stays roughly the same. No matter if a quadrotor is flying with different payloads through various amount of winds, all share similar Newtonian

00:00:59 structure. Our idea is to first extract a shared core. Symbolic regression gives us compact equations that generalize far beyond the training data. Think of a sine wave. A neuronet network fits the part it sees but collapses outside its range. Symbolic regression recovers the true equation and keeps working everywhere else.

00:01:32 That same principle holds in robotics. Even if a model only trains on forward driving, it still works in reverse. This generalization makes symbolic regression a strong base for adaptive control. But these equations alone can't handle the messy reality things like sensor noise, motor delays, or extra weight. That's where the second step comes in. A small neuronet network acts as a

00:02:03 residual adding just enough correction to adapt the model in the real world. With this hybrid approach, a quadrotor can adapt to new payloads, unexpected winds, or shifts in balance, all with only a handful of real trajectories. The same method works for a small race car as well, coping with drifting or steering offsets.

00:02:59 Unlike pure neuronet network approaches that need huge data sets, sim to real needs only a few runs, very few yet keeps the structure of physics intact. And unlike pure symbolic models, it doesn't collapse when faced with noise. Together, the two sides combine. Symbols for the core loss, residuals for the fine details. The result, data

00:03:31 efficient, adaptive control, robust across simulation and reality with little tuning and no expert priors. Sim to real paves the way for robots to learn low-level control more easily, more reliably, and faster than before.