杨辰光，英国利物浦大学机器人学讲席教授，机器人及自主系统研究团队带头人，欧洲科学与艺术院院士，国际电气与电子工程师协会（IEEE）、英国工程技术学会（IET）、英国机械工程师学会（IMechE）、亚太人工智能学会（AAIA）及英国计算机学会（BCS）会士。现任IEEE柔性制造协同自动化技术委员会（CAFM）联席主席，《Robot Learning》创刊主编、《IEEE系统、人与控制论汇刊：系统》及《IEEE自动化科学与工程汇刊》高级编辑，《Frontiers in Robotics and AI》期刊机器人计算智能领域首席主编。曾以大会主席身份成功筹办第25届IEEE工业技术国际会议（ICIT）和第27届自动化与计算国际会议（ICAC）。先后获得2012年“IEEE机器人学汇刊最佳论文奖”及2022年“IEEE神经网络与学习系统汇刊杰出论文奖”两大国际顶级期刊奖项。

  报告摘要：

Learning from Demonstration (LfD), or imitation learning, allows robots to acquire and generalize task skills through human demonstrations, creating a seamless integration of artificial intelligence and robotics. Most LfD approaches often overlook the importance of demonstrated forces and rely on manually configured impedance parameters. In response, my team has developed a series of biomimetic impedance and force controllers inspired by neuroscientific findings on motor control mechanisms in humans, enabling robots to imitate compliant manipulation skills. Our models reduce the dimensionality of skill representation, facilitating online optimization and reducing system sensitivity to parameter changes. To improve robot skill learning through enhanced perceptual capabilities, we designed anthropomorphic visual tactile sensors that assess contact force, surface texture, and shape, closely resembling the softness and wear resistance of human fingers for superior manipulation. The control and learning technologies we have developed have been particularly effective in robot teleoperation and human-robot collaboration, with shared control-based semi-autonomous methods that effectively integrate human intent with robotic autonomy, thereby achieving greater efficiency and usability.