Interaction Intention

Interaction intention research focuses on accurately predicting and understanding a user's or agent's intended actions, particularly in human-robot interaction and human-AI collaboration. Current efforts concentrate on developing models that jointly predict both the initial intention (e.g., hand movements, interaction hotspots) and subsequent actions, often employing transformer-based architectures and incorporating techniques like reinforcement learning and learning from demonstration to handle uncertainty. This field is crucial for improving the safety, efficiency, and naturalness of human-machine interaction, with applications ranging from robotics and assistive technologies to more intuitive and effective AI agents.