In recent years, there has been a booming shift in the development of versatile, autonomous robots by introducing means to intuitively teach robots task-oriented behaviour by demonstration. In this paper, a method based on programming by demonstration is proposed to learn null space policies from constrained motion data. The main advantage to using this is generalisation of a task by retargeting a systems redundancy as well as the capability to fully replace an entire system with another of varying link number and lengths while still accurately repeating a task subject to the same constraints. The effectiveness of the method has been demonstrated in a 3-link simulation and a real world experiment using a human subject as the demonstrator and is verified through task reproduction on a 7DoF physical robot. In simulation, the method works accurately with even as little as five data points producing errors less than 10^-14. The approach is shown to outperform the current state-of-the-art approach in a simulated 3DoF robot manipulator control problem where motions are reproduced using learnt constraints. Retargeting of a systems null space component is also demonstrated in a task where controlling how redundancy is resolved allows for obstacle avoidance. Finally, the approach is verified in a real world experiment using demonstrations from a human subject where the learnt task space trajectory is transferred onto a 7DoF physical robot of a different embodiment.
Interactive Motion Planning for Human-Robot Collaboration Based on Human-Centric Configuration Space Ergonomic Field
Department of Mechanical and Automation Engineering, T-Stone Robotics Institute, The Chinese University of Hong Kong（机械与自动化工程系，T-Stone机器人研究所，香港中文大学）；Idiap Research Institute（Idiap研究所）；Department of Advanced Robotics, Istituto Italiano di Tecnologia（先进机器人系，意大利Istituto Italiano di Tecnologia）
Computational ergonomics for task delegation in Human-Robot Collaboration: spatiotemporal adaptation of the robot to the human through contactless gesture recognition
Integrating Ergonomics and Manipulability for Upper Limb Postural Optimization in Bimanual Human-Robot Collaboration
Department of Mechanical and Automation Engineering, T-Stone Robotics Institute, The Chinese University of Hong Kong（机械与自动化工程系、T-Stone机器人研究所、香港中文大学）；Bristol Robotics Laboratory, UWE Bristol（布里斯托尔机器人实验室、UWE布里斯托尔）；HKCLR, Hong Kong（香港物流机器人中心、香港）
CaFe-TeleVision: A Coarse-to-Fine Teleoperation System with Immersive Situated Visualization for Enhanced Ergonomics
Department of Mechanical and Automation Engineering, T-Stone Robotics Institute, The Chinese University of Hong Kong, Hong Kong SAR（机械与自动化工程系、T-Stone机器人研究所、香港中文大学（深圳））
Towards Shared Embodied Intelligence in Humanoid Robots through Optimization Development and Testing of the Human Aware ergoCub Robot
GenerativeBionics（生成生物技术）；Artificial and Mechanical Intelligence（人工与机械智能）；School of Computer Science, University of Manchester（曼彻斯特大学计算机科学学院）；Humanoid Sensing and Perception（人形感知与感知）；iCub Tech Facility（iCub技术设施）；DiMEILA, Istituto Nazionale Assicurazione Infortuni sul Lavoro (INAIL)（DiMEILA，意大利国家职业伤害保险机构（INAIL））
A Human-Sensitive Controller: Adapting to Human Musculoskeletal Disorder-Related Constraints via Reinforcement Learning
Fundação para a Ciência e Tecnologia（葡萄牙科学与技术基金会）；Centro de Microssistemas Eletromecânicos da Universidade do Minho（University of Minho微机电系统中心）；Massachusetts Institute of Technology（麻省理工学院）；Brigham and Women’s Hospital, Harvard Medical School（哈佛医学院布莱尔妇女医院）；GEVAB, IQS School of Engineering（GEVAB，IQS工程学院）；LABBELS-Associate Laboratory, University of Minho（University of Minho关联实验室）
Intent-Handover: Grounding Language in Human-Usage Regions for Trustworthy Robot-to-Human Handovers
DANiLab, University of Leicester（莱斯特大学DANiLab）；University of Leicester（莱斯特大学）；School of Computing and Mathematical Sciences, University of Leicester（莱斯特大学计算与数学科学学院）；School of Metallurgy and Materials, University of Birmingham（伯明翰大学冶金与材料学院）
Introspective Visuomotor Control: Exploiting Uncertainty in Deep Visuomotor Control for Failure Recovery
Applied AI Lab (A2I)（应用人工智能实验室（A2I））；Dynamic Robot Systems (DRS)（动态机器人系统实验室（DRS））；Oxford Robotics Institute (ORI), University of Oxford（牛津大学牛津机器人研究所（ORI））
Influence of Gripper Design on Human Demonstration Quality for Robot Learning
University of Massachusetts Amherst（马萨诸塞大学阿姆赫斯特分校）；Department of Mechanical and Industrial Engineering（机械与工业工程系）；Elaine Marieb Center for Nursing and Engineering Innovation（Elaine Marieb护理与工程创新中心）；Center for Personalized Health Monitoring（个性化健康监测中心）；Institute for Applied Life Sciences（应用生命科学研究所）
Identifying and Exploiting Structure in Robot Co-Design
Robotics and Biology Laboratory, Technische Universität Berlin（柏林工业大学机器人与生物学实验室）；Science of Intelligence (SCIoI), Cluster of Excellence, Berlin, Germany（德国柏林卓越集群“智能科学”（SCIoI））
Learning to Control Emulated Muscles in Real Robots: Towards Exploiting Bio-Inspired Actuator Morphology
Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；Hertie Institute for Clinical Brain Research（赫特希临床脑研究所在）；Centre for Integrative Neuroscience（整合神经科学中心）；University of Tübingen（图宾根大学）；Distributed Intelligence（分布式智能）；Institute of Computer Engineering (ZITI)（计算机工程研究所）
Adaptive vs. Static Robot-to-Human Handover: A Study on Orientation and Approach Direction
Department of Engineering "Enzo Ferrari", University of Modena and Reggio Emilia（工程学院"Enzo Ferrari"，摩德纳和雷吉奥艾米利亚大学）；Department of Surgery, Medicine, Dentistry and Morphological Sciences, University of Modena and Reggio Emilia（外科、医学、牙科和形态学科学学院，摩德纳和雷吉奥艾米利亚大学）
