Bridging Discrete Planning and Continuous Execution for Redundant Robot

• URL: http://arxiv.org/abs/2604.02021v1
• Date: Thu, 02 Apr 2026 13:23:54 GMT
• Title: Bridging Discrete Planning and Continuous Execution for Redundant Robot
• Abstract summary: Voxel-grid reinforcement learning is widely adopted for path planning in redundant manipulators.<n>However, direct execution through point-wise numerical inverse kinematics on 7-DoF arms often yields step-size jitter.<n>This work proposes a bridging framework between discrete planning and continuous execution without modifying the discrete planner itself.
• Score: 12.278986080239692
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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