HOI4ABOT: Human-Object Interaction Anticipation for Human Intention Reading Collaborative roBOTs
This work addresses the need for robots to understand human intentions in collaborative tasks, representing an incremental improvement in human-robot interaction.
The paper tackles the problem of enabling robots to anticipate human-object interactions for better collaboration, proposing a transformer-based model that achieves state-of-the-art results with a 1.76% increase in mAP for detection and 1.04% for anticipation on the VidHOI dataset, while being 15.4 times faster.
Robots are becoming increasingly integrated into our lives, assisting us in various tasks. To ensure effective collaboration between humans and robots, it is essential that they understand our intentions and anticipate our actions. In this paper, we propose a Human-Object Interaction (HOI) anticipation framework for collaborative robots. We propose an efficient and robust transformer-based model to detect and anticipate HOIs from videos. This enhanced anticipation empowers robots to proactively assist humans, resulting in more efficient and intuitive collaborations. Our model outperforms state-of-the-art results in HOI detection and anticipation in VidHOI dataset with an increase of 1.76% and 1.04% in mAP respectively while being 15.4 times faster. We showcase the effectiveness of our approach through experimental results in a real robot, demonstrating that the robot's ability to anticipate HOIs is key for better Human-Robot Interaction. More information can be found on our project webpage: https://evm7.github.io/HOI4ABOT_page/