One of the most important challenges of Smart City Applications is to adapt the system to interact with non-expert users. Robot imitation frameworks aim to simplify and reduce times of robot programming by allowing users to program directly through demonstrations. In classical frameworks, actions are modeled using joint or Cartesian space trajectories. Other features, such as visual ones, are not always well represented with these pure geometrical approaches. Continuous Goal-Directed Actions (CGDA) is an alternative to these methods, as it encodes actions as changes of any feature that can be extracted from the environment. As a consequence of this, the robot joint trajectories for execution must be fully computed to comply with this feature-agnostic encoding. This is achieved using Evolutionary Algorithms (EA), which usually requires too many evaluations to perform this evolution step in the actual robot. Current strategies involve performing evaluations in a simulation, transferring the final joint trajectory to the actual robot. Smart City applications involve working in highly dynamic and complex environments, where having a precise model is not always achievable. Our goal is to study the tractability of performing these evaluations directly in a real-world scenario. Two different approaches to reduce the number of evaluations using EA, are proposed and compared. In the first approach, Particle Swarm Optimization (PSO)-based methods have been studied and compared within CGDA: naive PSO, Fitness Inheritance PSO (FI-PSO), and Adaptive Fuzzy Fitness Granulation with PSO (AFFG-PSO). The second approach studied the introduction of geometrical and velocity constraints within CGDA. The effects of both approaches were analyzed and compared in the wax and paint actions, two CGDA commonly studied use cases. Results from this paper depict an important reduction in the number of evaluations.

翻译：智慧城市应用面临的关键挑战之一在于如何使系统能够与非专业用户进行交互。机器人模仿框架旨在通过允许用户直接通过演示进行编程，从而简化并缩短机器人编程的时间。在经典框架中，动作通过关节空间或笛卡尔空间轨迹进行建模。然而，诸如视觉特征等其他特征，往往难以通过这些纯几何方法得到充分表征。连续目标导向动作（CGDA）作为这些方法的一种替代方案，将动作编码为可从环境中提取的任何特征的变化。因此，机器人执行时必须完全计算出关节轨迹以满足这种与特征无关的编码方式。这通常需要借助进化算法（EA）来实现，而该算法在实际机器人上进行进化步骤时往往需要进行过多的评估。当前策略包括在仿真环境中执行评估，再将最终关节轨迹迁移至实际机器人。智慧城市应用涉及高度动态且复杂的环境，因此建立精确模型并非总能实现。本研究旨在探讨直接在现实场景中执行这些评估的可处理性。我们提出并对比了两种通过EA减少评估次数的不同方法。第一种方法研究了基于粒子群优化（PSO）的方法在CGDA框架内的效果，包括朴素PSO、适值继承PSO（FI-PSO）以及自适应模糊适值粒度与PSO（AFFG-PSO）。第二种方法探讨了在CGDA中引入几何约束与速度约束的影响。我们以蜡染与喷涂动作这两个CGDA的常见研究案例为基础，分析并比较了两种方法的效果。实验结果表明，评估次数得到了显著减少。