The　widespread　application　of　learning-based　methods　in　robotics　has　allowed　significant　simplifications　to　controller　design　and　parameter　adjustment.　In　this　article,　robot　motion　is　controlled　with　learning-based　methods.　A　control　policy　using　a　broad　learning　system　(BLS)　for　robot　point-reaching　motion　is　developed.　A　sample　application　based　on　a　magnetic　small-scale　robotic　system　is　designed　without　detailed　mathematical　modeling　of　the　dynamic　systems.　The　parameter　constraints　of　the　nodes　in　the　BLS-based　controller　are　derived　based　on　Lyapunov　theory.　The　design　and　control　training　processes　for　a　small-scale　magnetic　fish　motion　are　presented.　Finally,　the　effectiveness　of　the　proposed　method　is　demonstrated　by　convergence　of　the　artificial　magnetic　fish　motion　to　the　targeted　area　with　the　BLS　trajectory,　successfully　avoiding　obstacles.　©　2013　IEEE.