Rigid container walls invalidate the open-workspace assumptions of tabletop manipulation, leaving three coupled gaps: unconstrained trajectories can be wall-unsafe, single-point grasp checks reject accessible offset positions, and occlusion-removal actions can ignore wall proximity. This paper treats bin wall geometry as a first-class constraint. A multimodal framework couples YOLO11 instance segmentation with a radial-basis-function support vector machine (SVM) and geometric rules to select among five modes—direct grasping, height-based grasping, two-dimensional singulation, and partial and full occlusion removal based on target visibility, relative height, grasp feasibility and wall proximity. Every trajectory passes a common wall-constraint validator before reaching the controller. Experiments used a UR5e with an overhead RGB-D camera. YOLOv11m-Segmentation model reached 0.992 box and 0.991 mask mAP@0.5 with an average inference time of 0.4350 ± 0.11 s. Task success was 100.0%, 93.3%, and 86.66% at low, medium, and high clutter (30 trials per condition). At high clutter, the ablation increased TSR from 66.66% for the single-point wall-unaware baseline to 83.33% with multi-point grasp selection and 86.66% with wall-aware singulation. The proposed system achieved a 90% Task Success Rate (TSR), compared with Target-Oriented VPG, which achieved 60% TSR, and GraspNet with bin-wall constraints, which achieved 40% TSR with 10 trials in a high-clutter environment. Across the 90 proposed-system trials, six task failures were observed, but none involved wall contact. Because container awareness sits in the planning layer rather than the end-effector, the method retrofits to installed cells using standard two-finger hardware.