Where to Stand for Manipulation: Affordance Prediction and Feasibility-Guided Standing Refinement for Last-Meter Mobile Manipulation

Last-meter mobile manipulation requires a robot not only to navigate toward a task-relevant object, but also to terminate at a standing pose that enables successful downstream manipulation. However, existing navigation objectives primarily guide the robot to the target vicinity, without explicitly accounting for whether the resulting standing pose is feasible for the subsequent manipulation. We propose StandReady, which predicts manipulation-ready standing affordances from a single egocentric observation and a task instruction. To incorporate downstream manipulation feasibility, StandReady introduces manipulation-feasibility-guided standing refinement, which evaluates candidate standing poses using reference end-effector trajectories during training and uses the resulting trajectory-level rewards to refine affordance supervision. Experiments on the RoboCasa benchmark demonstrate that StandReady selects more manipulation-ready terminal poses and consistently improves downstream manipulation success over existing baselines. Ablation studies confirm that both feasibility-guided refinement and the trajectory-level reward design contribute to the performance gains. Overall, StandReady bridges the gap between navigation-oriented terminal-pose selection and the requirements of downstream manipulation.