Bridging Discrete Planning and Continuous Execution for Redundant Robot
On the map: Deep reinforcement learning methods → RL for robot locomotion and manipulation
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods. Builds on 7 of its 20 references that are in the map. First posted on arXiv 2 Apr 2026.
Builds on
7 of 20 references.
References · 7
- 2021Coarse-to-Fine Q-attention: Efficient Learning for Visual Robotic Manipulation via Discretisationcited by 122
- 2024An End-to-End Robust Point Cloud Semantic Segmentation Network with Single-Step Conditional Diffusion Modelscited by 23
- 2025Robust Single-Stage Fully Sparse 3D Object Detection via Detachable Latent Diffusioncited by 12
- 2025State-Aware Perturbation Optimization for Robust Deep Reinforcement Learningcited by 8
- 2024Q-value Regularized Decision ConvFormer for Offline Reinforcement Learningcited by 4
- 2021Safe-visor Architecture for Sandboxing (AI-based) Unverified Controllers in Stochastic Cyber-Physical Systemscited by 2
- 2020Task-Priority Control of Redundant Robotic Systems using Control Lyapunov and Control Barrier Function based Quadratic Programscited by 1
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- 2024RobotDiffuse: Diffusion-Based Motion Planning for Redundant Manipulators with the ROP Obstacle Avoidance Dataset
- 2026Learning-based Initialization of Trajectory Optimization for Path-following Problems of Redundant Manipulators ↗
- 2025URPlanner: A Universal Paradigm For Collision-Free Robotic Motion Planning Based on Deep Reinforcement Learning
- 2024Integrating DeepRL with Robust Low-Level Control in Robotic Manipulators for Non-Repetitive Reaching Tasks
- 2026A New Quaternion-Joint Cable-Driven Redundant Manipulator Configuration and its Control Through FABRIK and Residual Reinforcement Learning
- 2024Discrete Policy: Learning Disentangled Action Space for Multi-Task Robotic Manipulation
- 2023Offline Skill Generalization via Task and Motion Planning
- 2020Learning of Long-Horizon Sparse-Reward Robotic Manipulator Tasks with Base Controllers