Language Conditioned Policy

Language-conditioned policies aim to enable machines, particularly robots and language models, to understand and respond to natural language instructions for performing tasks. Current research focuses on developing efficient training methods for these policies, often employing multi-objective fine-tuning, hierarchical architectures (e.g., separating policy decision from action execution), and leveraging pre-trained multimodal models to improve generalization and reduce data requirements. This field is significant because it bridges the gap between human-understandable instructions and machine action, with potential applications ranging from more adaptable robots to improved machine translation and human-computer interaction.