Introduction

The DARPA Transfer from Imprecise and Abstract Models to Autonomous Technologies Program (TIAMAT) program tackles the challenge of enabling rapid and robust autonomy transfer in dynamic and complex environments. Existing methods, which often rely on high-fidelity simulations,struggle with efficiency and adaptability, particularly in time-sensitive scenarios. However, these techniques often fall short in handling complex, dynamic, and unforeseen real-world scenarios due to the inherent limitations of simulations.
In contrast to current research that aims to bridge the gap between simulation environments and the real world through increasingly sophisticated simulations, domain randomization, and dynamic optimization, TIAMAT takes a different approach. Instead of relying solely on high-fidelity simulations, TIAMAT methodology emphasizes the transfer and adaptation of perception, planning, and other
autonomy components directly to real-world environments by introducing innovative approaches that utilize low(er)-fidelity simulations to achieve fast and effective sim-to-real transfers. By abstractly, learning from and collating learned behaviors from multiple and simulation environments, TIAMAT looks to achieve abstract-to-real transfer, for that effective and rapid real-world adaptation. Further, this program aims to improve the overall autonomy pipeline by addressing the inherent challenges in translating simulated behaviors into effective real-world performance.