# Physical AI and the Sim2Real Transition

As we advance into the realm of **Physical AI**, DeBot Science is exploring ways to bring virtual training insights into real-world applications. This involves not just deploying trained policies but also creating a feedback loop where real-world performance informs further simulation improvements.

Our Approach:

* **Policy Validation in Real-World Prototypes**: We are currently integrating trained policies into physical robot prototypes for tasks such as terrain navigation and resource collection.
* **Real-to-Sim Feedback**: Data from real-world deployments is reintroduced into RoboGym and SOBO Lab to refine simulation accuracy and enhance policy robustness.
* **Partnerships for Physical Testing**: Collaborations with research labs and field teams are underway to test rover capabilities in Mars-analog environments on Earth.

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**6. Looking Ahead**

DeBot Science’s technical roadmap is centered on scaling capabilities and expanding applications:

* **Asimov Agent Integration**: Bridging physical and virtual domains, the Asimov Agent will enable non-experts to design and train robots using natural language and intuitive interfaces.
* **Broadening Robot Typologies**: From quadrupeds to drones and humanoids, we aim to create adaptable policies for a wide range of robotic forms.
* **Industry-Grade Applications**: Simulations and trained policies will be made accessible to enterprises, researchers, and developers, fostering mass adoption and driving innovation in the robotics space.
