Voxel-grid reinforcement learning is widely adopted for path planning in redundant manipulators due to its simplicity and reproducibility. However, direct execution through point-wise numerical inverse kinematics on 7-DoF arms often yields step-size jitter, abrupt joint transitions, and instability near singular configurations. This work proposes a bridging framework between discrete planning and continuous execution without modifying the discrete planner itself. On the planning side, step-normalized 26-neighbor Cartesian actions and a geometric tie-breaking mechanism are introduced to suppress unnecessary turns and eliminate step-size oscillations. On the execution side, a task-priority damped least-squares (TP-DLS) inverse kinematics layer is implemented. This layer treats end-effector position as a primary task, while posture and joint centering are handled as subordinate tasks projected into the null space, combined with trust-region clipping and joint velocity constraints. On a 7-DoF manipulator in random sparse, medium, and dense environments, this bridge raises planning success in dense scenes from about 0.58 to 1.00, shortens representative path length from roughly 1.53 m to 1.10 m, and while keeping end-effector error below 1 mm, reduces peak joint accelerations by over an order of magnitude, substantially improving the continuous execution quality of voxel-based RL paths on redundant manipulators.

翻译：基于体素网格的强化学习因其简洁性和可复现性，被广泛用于冗余机械臂的路径规划。然而，通过逐点数值逆运动学在七自由度机械臂上直接执行，往往会导致步长抖动、关节突变以及奇异位形附近的不稳定性。本文提出一种桥接框架，在不修改离散规划器的前提下，连接离散规划与连续执行。在规划侧，引入步长归一化的26邻域笛卡尔动作与几何破链机制，以抑制非必要转向并消除步长振荡。在执行侧，实现基于任务优先级阻尼最小二乘（TP-DLS）的逆运动学层。该层将末端执行器位姿设为主任务，而姿态与关节居中作为子任务投影至零空间，并结合信赖域裁剪与关节速度约束进行处理。在随机稀疏、中等及密集环境中的七自由度机械臂上，该桥接方法将密集场景下的规划成功率从约0.58提升至1.00，代表性路径长度从约1.53米缩短至1.10米，同时将末端执行器误差控制在1毫米以内，并将峰值关节加速度降低一个数量级以上，显著提升了基于体素的强化学习路径在冗余机械臂上的连续执行质量。