Conditioned Policy

Conditioned policy learning aims to create adaptable robotic control systems that can generalize to new tasks and environments with minimal retraining. Current research heavily focuses on leveraging demonstrations (e.g., videos, language descriptions, or task parameters) to condition policies, often employing transformer-based architectures and techniques like flow matching for efficient and robust performance. This approach is crucial for bridging the simulation-to-real gap and enabling robots to learn complex tasks from limited real-world data, impacting fields like robotics, human-robot interaction, and AI safety through the development of more versatile and reliable autonomous systems.