  Recently significant progress has been made in human action recognition and
behavior prediction using deep learning techniques, leading to improved
vision-based semantic understanding. However, there is still a lack of
high-quality motion datasets for small bio-robotics, which presents more
challenging scenarios for long-term movement prediction and behavior control
based on third-person observation. In this study, we introduce RatPose, a
bio-robot motion prediction dataset constructed by considering the influence
factors of individuals and environments based on predefined annotation rules.
To enhance the robustness of motion prediction against these factors, we
propose a Dual-stream Motion-Scenario Decoupling (\textit{DMSD}) framework that
effectively separates scenario-oriented and motion-oriented features and
designs a scenario contrast loss and motion clustering loss for overall
training. With such distinctive architecture, the dual-branch feature flow
information is interacted and compensated in a decomposition-then-fusion
manner. Moreover, we demonstrate significant performance improvements of the
proposed \textit{DMSD} framework on different difficulty-level tasks. We also
implement long-term discretized trajectory prediction tasks to verify the
generalization ability of the proposed dataset.
