URPlanner: A Universal Paradigm For Collision-Free Robotic Motion Planning Based on Deep Reinforcement Learning

TL;DR

URPlanner presents a universal, IK-free framework for collision-free motion planning in complex environments by combining a parameterized task space, a minimum-distance-free universal obstacle avoidance reward (UOAR), Augmented Policy Exploration and Evaluation (APE2), and an Expert Data Diffusion (ED2) strategy. The approach yields a platform-agnostic pipeline that trains rapidly in the parameterized space and achieves millisecond-scale trajectory generation without real-robot fine-tuning. Key contributions include UOAR for robust obstacle handling, APE2 for diverse action exploration and unbiased policy evaluation, and ED2 with a data compensation mechanism to leverage limited expert demonstrations. Together, these components enable efficient, generalizable motion planning across diverse manipulators and scenarios, with demonstrated superiority over traditional planners and prior DRL-based methods in training efficiency, trajectory quality, and replanning capabilities.

Abstract

Collision-free motion planning for redundant robot manipulators in complex environments is yet to be explored. Although recent advancements at the intersection of deep reinforcement learning (DRL) and robotics have highlighted its potential to handle versatile robotic tasks, current DRL-based collision-free motion planners for manipulators are highly costly, hindering their deployment and application. This is due to an overreliance on the minimum distance between the manipulator and obstacles, inadequate exploration and decision-making by DRL, and inefficient data acquisition and utilization. In this article, we propose URPlanner, a universal paradigm for collision-free robotic motion planning based on DRL. URPlanner offers several advantages over existing approaches: it is platform-agnostic, cost-effective in both training and deployment, and applicable to arbitrary manipulators without solving inverse kinematics. To achieve this, we first develop a parameterized task space and a universal obstacle avoidance reward that is independent of minimum distance. Second, we introduce an augmented policy exploration and evaluation algorithm that can be applied to various DRL algorithms to enhance their performance. Third, we propose an expert data diffusion strategy for efficient policy learning, which can produce a large-scale trajectory dataset from only a few expert demonstrations. Finally, the superiority of the proposed methods is comprehensively verified through experiments.

Figures (19)

• Figure 1: Schematic diagram of the parameterized space and reward calculation. The manipulator is represented as line segments encapsulated by cylinders, while the obstacles are modeled as expanded bounding boxes, incorporating the cylinder's radius along with a safety offset. UOAR is designed based on the overlap length between the line segments and bounding boxes. This reward encourages the DRL agent to learn collision-free motion planning by minimizing the overlap length, thereby ensuring safe and efficient trajectories.
• Figure 2: Framework of the APE2 algorithm. For each state, APE2 efficiently generates a large pool of action candidates through enhanced action exploration. During hybrid policy evaluation, the combination of the average Q-value from multiple critics, $Q_{\rm LTR}$, and the immediate return, $R_{\rm IR}$, ensures a more accurate and comprehensive assessment throughout the training process. The agent then executes the action candidate with the highest evaluated value.
• Figure 3: Frameworks of the ED2 model and two expert data utilization methods. Given a target area, the ED2 model can be efficiently trained using a very limited set of expert demonstrations. The trained model can generate a large number of novel trajectories. The generated expert data can be utilized in two ways: the hybrid policy method creates a synergy between behavior cloning and DRL but may not yield optimal trajectories, while the data compensation method enhances DRL training and ensures learning optimal policies.
• Figure 4: Overall framework of URPlanner. The proposed URPlanner consists of three modules: a DRL module where APE2 is applied, an environment module for the parameterized space and reward calculation, and an efficient policy learning module for deploying the ED2 model.
• Figure 5: Real-world scene and the corresponding parameterized space.