  Representation learning approaches for robotic manipulation have boomed in
recent years. Due to the scarcity of in-domain robot data, prevailing
methodologies tend to leverage large-scale human video datasets to extract
generalizable features for visuomotor policy learning. Despite the progress
achieved, prior endeavors disregard the interactive dynamics that capture
behavior patterns and physical interaction during the manipulation process,
resulting in an inadequate understanding of the relationship between objects
and the environment. To this end, we propose a general pre-training pipeline
that learns Manipulation by Predicting the Interaction (MPI) and enhances the
visual representation.Given a pair of keyframes representing the initial and
final states, along with language instructions, our algorithm predicts the
transition frame and detects the interaction object, respectively. These two
learning objectives achieve superior comprehension towards "how-to-interact"
and "where-to-interact". We conduct a comprehensive evaluation of several
challenging robotic tasks.The experimental results demonstrate that MPI
exhibits remarkable improvement by 10% to 64% compared with previous
state-of-the-art in real-world robot platforms as well as simulation
environments. Code and checkpoints are publicly shared at
https://github.com/OpenDriveLab/MPI.
