Obstacle-aware robotic manipulation can require substantial interaction data when deep reinforcement learning policies are trained from scratch. We study a task decomposition framework combining first-layer critic transfer with staged reward redesign (RR) on a Truncated Quantile Critics (TQC) backbone. Transfer reuses an input representation across tasks, while RR supplies intermediate feedback for Pick-and-Place. Experiments cover Push, Pick-and-Place, and Slide in obstacle-free and single-obstacle PyBullet environments, with three random seeds per setting. On obstacle-aware Push, transfer achieves $0.83\pm0.05$ success against $0.77\pm0.06$ for TQC. On obstacle-aware Slide, it achieves $0.95\pm0.02$ against $0.84\pm0.06$ for SAC, the strongest baseline in that setting. These are gains of $6$ and $11$ percentage points, respectively. RR reduces the target-training steps needed for obstacle-free Pick-and-Place from roughly $0.75$M to $0.20$M. Transfer benefits depend on the route: obstacle-aware Pick-and-Place shows a lower mean than training from scratch. A selected simulated trajectory is executed open-loop on a Franka Emika FR3 as a feasibility demonstration. The study characterizes when a lightweight combination of established transfer and reward-design components helps learning.

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