Hey friend,
One of the biggest problems in humanoid robotics is accurate real-time physics understanding.
What if a robot could learn and predict its physics on the fly. In real time. And adapt instantly to changes in its body or environment. This is what the latest research is exploring: using neural networks for real-time physics inference. Two of the powerful tools in this area are Differentiable Simulators and Neural ODEs.
Why Traditional Physics Simulation Isn’t
Traditional physics engines like MuJoCo Isaac Lab, Bullet and so on are great but they have limitations.
- They rely on fixed parameters that someone has to set by hand.
- They cannot easily adapt when the real robot changes.
- Running a high-fidelity simulation in time is very expensive in terms of computer power.
This is where neural approaches are really useful.
1. Differentiable Simulators
A differentiable simulator is a physics engine where every operation is differentiable.
This means you can run it forward to simulate motion and backward to compute gradients.
This enables some capabilities:
- You can optimize control policies directly through the simulator.
- The simulator can be fine-tuned using real-world data.
- It bridges the gap between simulation and reality effectively.
Some popular examples include DiffTaichi MuJoCo XLA and custom engines used by research labs working on Optimus and Figure-style robots.
2. Neural ODEs
ODEs take this idea even further.
Of manually writing equations of motion a Neural ODE learns a continuous-time dynamic model using a neural network:
Where \( f_\theta \) is a network.
Why this is powerful for humanoids:
- It can learn dynamics that are hard to model by hand.
- It naturally handles time.
- It can be integrated with control methods or reinforcement learning.
- The model can be updated online as the robot gathers real-world experience.
Some advanced systems use ODEs to predict how the robot’s Center of Mass angular momentum and foot contacts will evolve.
Real-World Applications in Humanoids
Leading teams are already using these techniques:
- Adaptive dynamics models that update when the robot picks up an object.
- Real-time system identification.
- Better simulation to reality transfer by learning a “reality gap” compensator.
- Hybrid controllers that combine classical physics with learned neural components.
This approach is especially valuable for humanoid designs where traditional rigid-body assumptions break down.
My Personal Take
Neural networks for physics inference represent a marriage between classical robotics and modern deep learning.
We no longer need analytical models. Instead we can build systems that continuously learn their physics. A child doesn’t need to know equations to learn how to walk; they learn through experience. ODEs and differentiable simulators are giving robots a similar capability.
I believe this direction. Combining physics with the flexibility of neural networks. Will be one of the most important breakthroughs in the next few years.
The ultimate goal is a humanoid that maintains a evolving internal model of its own body and the world, around it updated in real-time physics understanding.