TIAMAT (Transfer from Imprecise and Abstract Models to Autonomous Technologies)

TIAMAT (Transfer from Imprecise and Abstract Models to Autonomous Technologies) is sponsored by Defense Advanced Research Projects Agency (DARPA). The TIAMAT program funds teams to solve sim-to-real transfer, which involves training autonomous systems in low-fidelity simulations and deploying them in unpredictable physical environments.

Transfer from Imprecise and Abstract Models to Autonomous Technologies | DARPA Department of War organization. Transfer From Imprecise and Abstract Models To Autonomous Technologies Transfer from Imprecise and Abstract Models to Autonomous Technologies Multiple factors limit the potential of modern autonomous systems (e.g., self-driving vehicles and uncrewed aircraft and watercraft).

Autonomy is learned through modeling and simulation, given the expense of training in the real world. Generally, it goes like this: A model of the intended platform requiring autonomy is created. The model goes through various simulations in an environment as realistic as possible to generate the data that trains the autonomous system to make the right decisions.

After sufficiently training the model, those learnings are transferred to a physical system and tested to ensure the training works. Training models in high-fidelity environments for Defense Department platforms can sometimes take months to even years. Furthermore, autonomy becomes vulnerable when faced with unknown situations/observations in the real world.

This brittleness is known as the simulation-to-real (sim-to-real) gap. For example, a drone moving from a dense city to a coastal environment would encounter a dramatically different observation space. Unlike commercial autonomous systems, such as warehouse robotics or autonomous vehicles operating in a controlled environment using geofencing, military systems have far more unknown variables.

For instance, flight dynamics could be off, the lighting conditions are likely to vary, and it’s often impossible to model an adversary precisely as they act in the real world.

Contrary to the conventional wisdom of high-fidelity simulation, DARPA theorizes that learning and transferring autonomy across diverse, low-fidelity simulations leveraging their shared semantics (e.g., rules of engagement) instead can lead to a more rapid transfer of autonomy from simulation to reality – perhaps even as early as the same-day versus weeks/months with traditional approaches.

Moreover, moving from complex/realistic simulations to abstract and imprecise ones could allow systems to better adapt to the quick and inevitable changes in dynamic environments.

The Transfer from Imprecise and Abstract Models to Autonomous Technologies program aims to develop rapid autonomy transfer techniques to enable same day autonomy that is robust to the quick and inevitable changes in dynamic environments and adaptable to a variety of platforms and domains.

The program will test the theory that low-fidelity simulations can generate data at a much greater speed and scale, introducing the possibility of generalization rather than memorization. The program is organized into two phases: Phase 1 is 18 months and will develop sim-to-sim autonomy transfer techniques and novel methods for automatically developing or refining low-fidelity models and simulations to be used for transfer.

Phase 2 is 18 months and will develop sim-to-real autonomy transfer techniques and novel methods for automatically developing or refining low-fidelity models and simulations to be used for transfer. There will be two in-program competitions corresponding to the two phases of the program.

The model (and simulation) researcher | Ep 62 From abstraction to reality: DARPA's vision for robust sim-to-real autonomy | AI Magazine Information Processing Techniques Office