Conventional robotic systems often struggle in such contexts due to limited adaptability, fragile autonomy, and insufficient coordination. Challenges such as degraded sensor inputs, dynamic and unstructured terrains, GPS-denied environments, and constrained communications further hinder their effectiveness. While embodied AI has significantly enhanced the perception and action capabilities of individual robots, its focus has largely remained on single-agent systems.
Our objective
Introduction
Objectives
Achieving efficient multi-robot collaboration under uncertain, real-time, and bandwidth-limited conditions remains a major research frontier. The DIRECT project tackles these challenges by developing an innovative distributed machine learning framework that enables a fleet of robots to collaboratively perceive, reason, plan, and act in extreme environments.
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