  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/
