结合强化学习与SDF约束二次规划的移动机械臂全身运动规划反应式框架
A Reactive Framework for Whole-Body Motion Planning of Mobile Manipulators Combining Reinforcement Learning and SDF-Constrained Quadratic Programmi
AI总结：
针对移动机械臂在杂乱环境中因冗余自由度导致运动规划受限的问题，提出一种结合Bayes-DSAC强化学习与SDF约束二次规划的混合框架，显著提升了全身运动规划的效率、安全性与成功率。
摘要：
作为具身人工智能的重要分支，移动机械正日益广泛地应用于智能服务中，但其冗余自由度也限制了在杂乱环境中的高效运动规划。为了解决这一问题，本文提出了一种混合学习与优化框架，用于移动机械臂的反应式全身运动规划。我们开发了贝叶斯分布式软执行者-评论家（Bayes-DSAC）算法，以提高价值估计的质量和学习的收敛性能。此外，我们引入了一种由符号距离场（SDF）约束的二次规划方法，以增强避障运动的安全性。我们进行了实验并与标准基准进行了比较。实验结果验证了我们提出的框架显著提高了反应式全身运动规划的效率，减少了规划时间，并提高了运动规划的成功率。此外，所提出的强化学习方法确保了全身规划任务中的快速学习过程。这一新颖的框架使移动机械臂能够更安全、更高效地适应复杂环境。
英文摘要：
As an important branch of embodied artificial intelligence, mobile manipulators are increasingly applied in intelligent services, but their redundant degrees of freedom also limit efficient motion planning in cluttered environments. To address this issue, this paper proposes a hybrid learning and optimization framework for reactive whole-body motion planning of mobile manipulators. We develop the Bayesian distributional soft actor-critic (Bayes-DSAC) algorithm to improve the quality of value estimation and the convergence performance of the learning. Additionally, we introduce a quadratic programming method constrained by the signed distance field to enhance the safety of the obstacle avoidance motion. We conduct experiments and make comparison with standard benchmark. The experimental results verify that our proposed framework significantly improves the efficiency of reactive whole-body motion planning, reduces the planning time, and improves the success rate of motion planning. Additionally, the proposed reinforcement learning method ensures a rapid learning process in the whole-body planning task. The novel framework allows mobile manipulators to adapt to complex environments more safely and efficiently.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
