Bio-inspired robotic systems are capable of adaptive learning, scalable control, and efficient information processing. Enabling real-time decision-making for such systems is critical to respond to dynamic changes in the environment. We focus on dynamic target tracking in open areas using a robotic six-degree-of-freedom manipulator with a bird-eye view camera for visual feedback, and by deploying the Neurodynamical Computational Framework (NeuCF). NeuCF is a recently developed bio-inspired model for target tracking based on Dynamic Neural Fields (DNFs) and Stochastic Optimal Control (SOC) theory. It has been trained for reaching actions on a planar surface toward localized visual beacons, and it can re-target or generate stop signals on the fly based on changes in the environment (e.g., a new target has emerged, or an existing one has been removed). We evaluated our system over various target-reaching scenarios. In all experiments, NeuCF had high end-effector positional accuracy, generated smooth trajectories, and provided reduced path lengths compared with a baseline cubic polynomial trajectory generator. In all, the developed system offers a robust and dynamic-aware robotic manipulation approach that affords real-time decision-making.

翻译：仿生机器人系统具备自适应学习、可扩展控制和高效信息处理能力。为此类系统实现实时决策对于响应环境的动态变化至关重要。本研究聚焦于开放环境中的动态目标追踪，采用配备鸟瞰视觉反馈摄像头的六自由度机械臂，并通过部署神经动力学计算框架（NeuCF）实现。NeuCF是近期基于动态神经场（DNFs）和随机最优控制（SOC）理论开发的仿生目标追踪模型。该模型已针对平面定位视觉信标的触达动作进行训练，能够根据环境变化（例如新目标出现或现有目标消失）实时重定向或生成停止信号。我们在多种目标触达场景中对系统进行评估。所有实验表明，与基准三次多项式轨迹生成器相比，NeuCF具有更高的末端执行器定位精度，能生成平滑轨迹，并有效缩短路径长度。总体而言，所开发的系统提供了一种鲁棒且具备动态感知能力的机器人操控方法，可实现实时决策。