Learning　to　interact　with　objects　is　significant　for　robots　to　integrate　into　human　environments.　When　the　interaction　semantic　is　definite,　manually　guiding　the　manipulator　is　a　commonly　used　method　to　teach　robots　how　to　interact　with　objects.　However,　the　learning　results　are　robot-dependent　because　the　mechanical　parameters　are　different　for　different　robots,　which　means　the　learning　process　must　be　executed　again.　Moreover,　during　the　manual　guiding　process,　operators　are　responsible　for　recognizing　the　region　being　contacted　and　providing　expert　motion　programming,　which　limits　the　robot's　intelligence.　To　enhance　the　level　of　automation　in　object　interaction　for　robots,　this　paper　proposes　IRMT-Net　(Interaction　Region　and　Motion　Trajectory　prediction　Network)　to　predict　the　interaction　region　and　motion　trajectory　simultaneously　based　on　images.　IRMT-Net　achieves　state-of-the-art　interaction　region　prediction　results　on　Epic-kitchens　dataset,　generates　reasonable　motion　trajectories　and　can　support　robot　interaction　in　actual　situations.　IEEE