Behavior Transformer

Behavior Transformers (BeTs) are a class of machine learning models designed to predict and generate complex sequential behaviors from observational data, often addressing the challenges of high-dimensionality and multi-modality inherent in real-world actions. Current research focuses on improving BeT architectures, such as incorporating vector quantization for efficient handling of continuous action spaces and leveraging adversarial imitation learning to enhance robustness and adaptability. This work has significant implications for various fields, including robotics (e.g., improving robot manipulation and human-robot collaboration) and behavioral science (e.g., analyzing and classifying social interactions), by enabling more accurate and efficient modeling of dynamic behaviors from diverse datasets.