Implementing intelligent control of robots is a difficult task, especially when dealing with complex black-box systems, because of the lack of visibility and understanding of how these robots work internally. This paper proposes an Intelligent Social Learning (ISL) algorithm to enable intelligent control of black-box robotic systems. Inspired by mutual learning among individuals in human social groups, ISL includes learning, imitation, and self-study styles. Individuals in the learning style use the Levy flight search strategy to learn from the best performer and form the closest relationships. In the imitation style, individuals mimic the best performer with a second-level rapport by employing a random perturbation strategy. In the self-study style, individuals learn independently using a normal distribution sampling method while maintaining a distant relationship with the best performer. Individuals in the population are regarded as autonomous intelligent agents in each style. Neural networks perform strategic actions in three styles to interact with the environment and the robot and iteratively optimize the network policy. Overall, ISL builds on the principles of intelligent optimization, incorporating ideas from reinforcement learning, and possesses strong search capabilities, fast computation speed, fewer hyperparameters, and insensitivity to sparse rewards. The proposed ISL algorithm is compared with four state-of-the-art methods on six continuous control benchmark cases in MuJoCo to verify its effectiveness and advantages. Furthermore, ISL is adopted in the simulation and experimental grasping tasks of the UR3 robot for validations, and satisfactory solutions are yielded.

翻译：实现机器人的智能控制是一项艰巨任务，尤其在处理复杂黑箱系统时，由于缺乏对机器人内部工作机制的可视性与理解，挑战更为严峻。本文提出一种智能社会学习（ISL）算法，以实现黑箱机器人的智能控制。受人类社交群体中个体间相互学习的启发，ISL包含学习、模仿与自学三种模式。学习模式中的个体采用莱维飞行搜索策略向最佳表现者学习，并形成最紧密的关联关系。模仿模式中的个体通过随机扰动策略模拟最佳表现者，建立二级信任关系。自学模式中的个体采用正态分布采样方法进行独立学习，同时与最佳表现者保持疏远关系。每种模式中的种群个体均被视为自主智能体，通过神经网络执行三种模式下的策略动作，与环境及机器人交互并迭代优化网络策略。总体而言，ISL以智能优化原理为基础，融合强化学习思想，具备强搜索能力、快速计算速度、较少超参数设置及稀疏奖励不敏感等特性。通过MuJoCo平台六个连续控制基准案例，将所提ISL算法与四种前沿方法进行对比验证其有效性与优势。进一步地，ISL被应用于UR3机器人仿真及实验抓取任务中，取得了令人满意的解决方案。