Discover how deep reinforcement learning enables robot-agnostic locomotion control across diverse quadrupeds. Learn how one policy can adapt zero-shot to new hardware.

Why Robot-Agnostic Locomotion Matters

If you have ever worked on robotics, especially legged locomotion, you know how frustrating it is to retrain your whole policy every time the robot hardware changes. Whether it’s a shift in weight, joint angles, or actuator speed it all breaks the existing model.

That’s where robot-agnostic locomotion control comes into play. This approach allows you to build a universal policy that works across multiple robot platforms without the need for retraining from scratch.

One Policy, Many Robots

Most RL-based locomotion systems are built to work on a single robot. Once the robot’s body changes say you move from a Unitree Go1 to a Go2 you are stuck. You have got to re-collect data, re-tune hyperparameters, and go back to the drawing board.

But what if you could create a single locomotion policy that generalizes across a whole family of robots?

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Deep Reinforcement Learning with Recurrent Policies

To solve this, I trained a recurrent policy using deep reinforcement learning (DRL). Inspired by meta-RL, the architecture uses a GRU (Gated Recurrent Unit) that captures general movement strategies and performs implicit system identification basically, the model learns to adapt on the fly.

This way, the robot figures out how to walk, trot, and balance without needing to know exactly which robot it’s operating on.

Meta-RL Meets Kinematic Guidance

I started with two commercially available robots Unitree Go1 and Unitree Aliengo. By randomizing their physical parameters, I created a diverse set of virtual quadrupeds. Then, I designed an RL task to imitate motion from a kinematic reference generator.

This reference provided the “ideal” base and feet trajectories. The policy had to learn to track this motion across all the different simulated morphologies.

What’s cool is that the policy naturally started by mastering easier robots first, then gradually adapted to harder ones with unusual gaits, body masses, and joint configs.

Zero-Shot Transfer to Unseen Quadrupeds

In simulation, I tested the policy on 40 new robots it had never seen before. Every single one was a quadruped with its own quirks.

Here’s what I found:

• The recurrent policy generalized well to all 40 robots
• It could trot smoothly, respond to speed commands, and track desired trajectories
• Even edge-case robots with weird dynamics were handled effectively

No retraining. No fine-tuning. Just plug and trot.

Real-World Testing on Go1, Go2, and Aliengo

I didn’t stop at simulation. The same policy was deployed, zero-shot, on:

1. Unitree Go1
2. Unitree Go2 – despite its very different dynamics
3. Unitree Aliengo – a larger and heavier quadruped

Each robot could execute stable locomotion using exactly the same policy, with no code or training tweaks. That’s the power of robot-agnostic control.

Why GRU Outperforms MLP in Locomotion

You might ask, “Why not just use a Multi-Layer Perceptron (MLP)?”

Well, I tried that too. MLPs even those fed with a history of past 16 states and actions struggled. Here’s what went wrong:

• They couldn’t track foot height correctly
• They failed to follow speed commands
• They caused excessive base rotation, violating the regularization reward

The GRU-based policy nailed it, keeping roll and pitch angles close to zero while executing fluid, natural motions.

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FAQs About Reinforcement Learning in Robotics

Conclusion

Building a robot-agnostic locomotion policy is no longer just an academic dream. Using deep reinforcement learning, domain randomization, and a GRU-based recurrent policy, we can now create robust, general-purpose controllers that work across a wide range of quadruped robots right out of the box.

If you are working in robotics and tired of starting from scratch every time your hardware changes, this approach might be exactly what you need.