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摘要: 传统机器人经过长时间的研究和发展, 已经在生产和生活的多个领域得到了广泛的应用, 但在复杂多变的环境中依然缺乏与真实生物类似的灵活性、稳定性和适应能力. 类脑智能作为一种新型的机器智能, 使用计算建模的方法模拟生物神经系统的各类特性, 进而实现对各类信息的推理和决策, 近年来受到了学术界的广泛关注. 鉴于此, 综述了国内外面向机器人系统的类脑智能研究现状, 并对类脑智能方法在机器人感知、决策和控制三个研究方向的成果进行了整理、归纳和分析, 最后从软硬件层面分别指出了机器人类脑智能目前存在的主要问题和未来的发展方向.Abstract: After a long time of research and development, traditional robots have been widely used in many fields of production and life, but they still lack the flexibility, stability and adaptability similar to real organisms in complex and changing environments. As a new type of machine intelligence, brain-inspired intelligence uses computational modeling methods to simulate various characteristics of biological nervous systems and realize the reasoning and decision-making based on all kinds of information. In recent years, it has received extensive attention from the academic community. The main applications of brain-inspired intelligence methods in robot perception, decision-making and control problems are introduced. The related research results are analyzed and summarized. Finally, the main problems of software and hardware are pointed out and future development directions of robotic brain-inspired intelligence are proposed.
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Key words:
- Robot /
- brain-inspired robot /
- brain-inspired intelligence /
- spiking neural network (SNN)
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图 9 基于情感决策的控制时域调整网络
Fig. 9 Control time domain adjustment network based on emotional decision
图 11 基于多脑区协同的机械臂控制器
Fig. 11 Manipulator controller based on collaboration of multiple brain regions
图 13 基于SNN的TD强化学习模型 (Q网络为SNN)
Fig. 13 SNN-based TD reinforcement learning model (Q network is SNN)
图 14 基于小脑网络的机械臂预测矫正控制器
Fig. 14 Predictive correction controller for manipulator based on cerebellar network
表 1 机器人类脑感知方向文献总结
Table 1 Summary of the literature of robotic brain-inspired perception
模态 研究团队 传感器种类 网络结构 主要功能 视觉 Kreiser 等[41] 相机
(iCub机器人)仿脑内回路 识别物体并将注意力转移到感兴趣的物体上 Zhou等[20] 激光雷达 (LiDAR)
(文中未确切指明)层式 感知物体的三维轮廓 Ambrosano等[46] 相机
(iCub机器人)仿视网膜回路 机器人视线追踪 Li等[21] 相机 层式 手势识别和意图推断 Qiao等[25] 相机 层式 物体 (人脸)识别 Tang等[43] 距离传感器和RGB相机 多网络融合 SLAM Yoon等[42] 相机 仿脑内回路 SLAM Hussaini等[22] 相机
(文中未确切指明)层式 地点识别
(Place recognition)听觉 Deng等[27, 47] 麦克风 层式 语音分类 Zou等[4] 麦克风 层式 语音识别 Gao等[29] 麦克风 阵列式 声源定位 Liu等[28] 麦克风 仿脑内回路 声源定位 触觉 Chou等[30] 触觉传感器
(文中未确切指明)仿脑内回路 感知人类的轻抚动作 Feng等[31] 多传感器融合 仿脑内回路 机器人的“痛觉”感知 张超凡等[32] GelStereo触觉传感器 层式 触觉滑动感知 Liu等[39] 基于晶体管的电子皮肤 仿生物触觉回路 触觉记忆与学习
(以痛觉反射为例)Dabbous等[33] 压阻式传感器阵列 层式 触觉模态分类 Jiang等[34] 压电传感器 层式 表面粗糙度感知 Lee等[38] 基于压阻器和忆阻器的触觉神经元 库网络
(Reservoir)生物组织硬度感知 Han等[37] 基于摩擦纳米发电器的人工触觉感受器 层式 广范围 (3 kPa)触觉感知
(以呼吸状态辨识为例)Kang等[35] 事件驱动触觉传感器
NeuTouch[48]层式 触觉对象识别
触觉滑动检测Wen等[40] 基于压阻器和忆阻器的触觉神经元 层式 触觉感知和识别
(以MNIST分类为例)
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表 2 机器人类脑决策方向文献总结
Table 2 Summary of the literature of robotic brain-inspired decision
模型结构 研究团队 模型输入 模型输出 主要功能 循环结构 Rueckert等[56] 某一时刻Agent的状态
(例如空间位置)未来一段时间内的位置规划
(位置序列)有限和无限时间范围的任务规划问题 仿脑内回路 Zhao等[53] 视觉图像信息
(State)动作决策
(无人机的前进、后退、向左、向右)无人机飞行 (穿过窗户)过程决策任务 Daglarli等[55] 视觉信息
听觉信息机器人行为序列
决策奖励信号机器人的类人决策
(情感、注意力、意图等推理)左国玉等[50] 关于任务、记忆、 观测Affordance
和标签的词语该物品的Affordance (可以抓取)
或者该物品所需Affordance
的建议 (不可抓取)根据不同的任务选择合适的
物品和抓取位置Robertazzi等[52] 模拟视觉刺激
(方波信号)机器人动作
(向左、向右、保持不动)实现机器人在指定任务需求下的“动作抑制” Huang等[59] 状态预测误差
奖励预测误差预测时域调整量 在MB和MF控制之间
切换 (图9)层式 Liu等[58] 传感器感知到的数据 (转换为脉冲序列) 机器人控制量 (左右轮速度) 针对不同的任务需求激活不同的突触
实现控制策略的切换Skorheim等[57] 视觉感知信息
(7×7矩阵)运动决策信息
(3×3矩阵)实现虚拟环境中Agent的觅食决策 Zhao等[49] 环境上下文信息与对其他
个体状态和行为的观测对其他个体未来行为的预测 多智能体协同决策
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表 3 机器人类脑控制方向文献总结
Table 3 Summary of the literature of robotic brain-inspired control
机器人类型 研究团队 网络输入 网络输出 主要功能 移动机器人 Lobov等[75]
(2-DoF)超声传感器信息
触觉传感器信息控制信号
(电机驱动量)移动控制
(避障)Wang等[68]
(2-DoF)声纳传感器信息 控制信号
(电机驱动量)移动控制
(避障、追踪、沿墙行走)Bing等[65]
(2-DoF)DVS图像信息 控制信号 (电机转速) 移动控制
(车道保持)Liu等[70]
(4-DoF)超声传感器 控制指令
(左、右、前进)移动控制
(避障)Lu等[64]
(4-DoF)超声传感器感知到的距离信息 控制信号 (转换为
左右轮转速)移动控制
(避障)机械臂 Xing等[7]
(未说明)其他网络输出的运动参数 关节驱动力矩 对小空间操作机器人
实现精确控制Chen等[86]
(1-DoF)期望角度与传感器信息 控制信号补偿量 提高机械臂的适应性和鲁棒性 IAbadia等[6]
(6-DoF)轨迹规划器生成的轨迹参数 机械臂控制力矩 通过预测运动指令实现对
指令延迟的鲁棒性Zahra等[87]
(6-DoF)目标运动状态与机械臂内部传感器信息 机械臂驱动量 提高机械臂在操作任务中的
精度和运动协调性Carrillo等[77]
(2-DoF)期望机械臂状态与目标信息 肩部与肘部驱动转动的调整量 机械臂运动控制 Zahra等[82]
(6-DoF)关节角速度变化量 机器人状态预测 降低机械臂的运动误差和执行时间 Zhang等[80]
(2-DoF)原始控制信号 控制信号纠正量 提升机械臂的运动精度 仿生机器人 Naveros等[88]
(3-DoF)感知运动信号
(Sensory-motor signal)眼球转动量 根据机器人头部转动控制机器人
眼球转动 (前庭眼反射)Naya等[5]
(24-DoF)状态观测量
(State)行动
(Action)考虑能耗成本的步态学习 Lele等[62]
(6-DoF)DVS图像信息 步态选择 移动控制 (捕猎) Espinal等[89]
(12-DoF)− 步态生成 作为CPG生成指定的步态 Jiang等[60]
(未说明)NVS图像信息 (神经形态视觉传感器) 控制信号
(传递给CPG生成对应的步态)移动控制
(目标追踪)− Wilson等[83] 控制信号 控制对象的状态预测 实现一般线性系统的自适应控制 注: DoF (Degree of freedom)为对应文献中机器人的自由度.
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