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Abstract: In this talk, we cover four aspects of research on general manipulation for assistive robotics : i), efficiently adapting large-scale models to downstream scene affordance understanding tasks, especially in daily living scenarios where gathering multi-task data involving humans requires strenuous effort; ii), effectively learning robot action trajectories with our flow matching policy; iii) sim-to-real physics learning for deformable manipulation and robot-assisted dressing;  iv) multi-modal (vision, tactile, audio) learning.