Abstract
The development of artificial intelligence towards real-time interaction with the environment is a key aspect of embodied intelligence and robotics. Inverse dynamics is a fundamental robotics problem, which maps from joint space to torque space of robotic systems. Traditional methods for solving it rely on direct physical modeling of robots which is difficult or even impossible due to nonlinearity and external disturbance. Recently, data-based model-learning algorithms are adopted to address this issue. However, they often require manual parameter tuning and high computational costs. Neuromorphic computing is inherently suitable to process spatiotemporal features in robot motion control at extremely low costs. However, current research is still in its infancy: existing works control only low-degree-of-freedom systems and lack performance quantification and comparison. In this paper, we propose a neuromorphic control framework to control 7 degree-of-freedom robotic manipulators. We use Spiking Neural Network to leverage the spatiotemporal continuity of the motion data to improve control accuracy, and eliminate manual parameters tuning. We validated the algorithm on two robotic platforms, which reduces torque prediction error by at least 60% and performs a target position tracking task successfully. This work advances embodied neuromorphic control by one step forward from proof of concept to applications in complex real-world tasks.
Abstract (translated)
人工智能向环境实时互动的发展是具身智能和机器人技术的关键方面。逆动力学问题是机器人技术中的一个基本问题，它从关节空间映射到机器人的扭矩空间。传统的方法依赖于对机器人的直接物理建模来解决这个问题，但由于非线性和外部干扰的存在，这种建模往往很难甚至不可能实现。近年来，基于数据的模型学习算法被用来应对这一挑战。然而，这些方法通常需要手动参数调整，并且计算成本高昂。神经形态计算天然适合在机器人运动控制中以极低的成本处理时空特征。但是，目前的研究仍处于初级阶段：现有的工作仅能控制自由度较低的系统，并且缺乏性能量化和比较。 本文提出了一种用于控制具有7个自由度机械臂的神经形态控制框架。我们使用脉冲神经网络来利用运动数据中的时空连续性以提高控制精度，并消除手动参数调整的需求。我们在两个机器人平台上验证了该算法，减少了至少60%的扭矩预测误差，并成功完成了一个目标位置跟踪任务。这项工作使具身神经形态控制从概念证明迈向复杂现实世界应用迈出了重要的一步。
URL
https://arxiv.org/abs/2504.12702
