[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119780-en":3,"doc-seo-119780-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119780,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","Machine Learning Meets Advanced Robotic Manipulation - Survey and Research Directions","Automated industries require high-quality production and lower manufacturing cost, and robotic manipulator arms are central to this automation. Yet for complex manipulation tasks, hand-crafting efficient and safe trajectories remains difficult and time-consuming. Machine learning can learn controllers from expert demonstrations, but training and deployment still need improved safety, reliability, and efficiency. This survey reviews cutting-edge technologies and recent trends of ML for real-world manipulation tasks, then outlines future research directions.","arXiv :2309 . 12560v1 [ cs .RO] 22 Sep 2023  \nMachine Learning Meets Advanced Robotic Manipulation  \nSaeid Nahavandia,b , Roohallah Alizadehsanic,∗, Darius Nahavandic , Chee Peng Limc , Kevin Kellyd , Fernando Belloe  \na Distinguished Professor, Associate Deputy Vice-Chancellor Research, Swinburne  \nUniversity of Technology, Hawthorn, VIC 3122, Australia b Harvard Paulson School of Engineering and Applied Sciences, Harvard  \nUniversity, Allston, MA 02134, USA  \nc Institute for Intelligent Systems Research and Innovation (IISRI), Deakin  \nUniversity, Australia  \nd Department of Mechanical & Manufacturing Engineering, Trinity  \nCollege, Dublin, Ireland  \ne Center for Engagement and Simulation Science, Department of Surgery and Cancer,  \nImperial College London, London, United Kingdom  \nAbstract  \nAutomated industries lead to high quality production, lower manufacturing cost and better utilization of human resources. Robotic manipulator arms have major role in the automation process. However, for complex manipulation tasks, hard coding efficient and safe trajectories is challenging and time consuming. Machine learning methods have the potential to learn such controllers based on expert demonstrations. Despite promising advances, better approaches must be developed to improve safety, reliability, and efficiency of ML methods in both training and deployment phases. This survey aims to review cutting edge technologies and recent trends on ML methods applied to real-world manipulation tasks. After reviewing the related background on ML, the rest of the paper is devoted to ML applications in different domains such as industry, healthcare, agriculture, space, military, and search  \n∗ Corresponding author  \nEmail addresses: [snahavandi@swin.edu.au](snahavandi@swin.edu.au) (Saeid Nahavandi), [r.alizadehsani@deakin.edu.au](r.alizadehsani@deakin.edu.au) (Roohallah Alizadehsani), [darius.nahavandi@deakin.edu.au](darius.nahavandi@deakin.edu.au) (Darius Nahavandi), [chee.lim@deakin.edu.au](chee.lim@deakin.edu.au)  \n(Chee Peng Lim), [KEKELLY@tcd.ie](KEKELLY@tcd.ie) (Kevin Kelly), [F.Bello@imperial.ac.uk](F.Bello@imperial.ac.uk) (Fernando  \nBello)  \nPreprint submitted to Information Fusion September 25, 2023  \nand rescue. The paper is closed with important research directions for future works.  \nKeywords: Machine learning, Deep learning, Reinforcement learning, Manipulator  \n1. Introduction  \nManipulating different objects is a trivial task for humans owing to having unique vision capabilities and a highly flexible body structure with 224 degrees of freedom (DoF) and approximately 630 skeletal muscles [1] . However, robotic manipulation is one of the complex problems, and is an active research field. The reason is that replicating the vision and actuation capabilities of natural organisms (e.g. humans) is a very challenging task by using the respective (possibly simplified) robotic counterparts. As complexity of robot structures increases from hard to soft (section 2), developing controllers for them becomes more difficult which is the motivation behind using machine learning (ML) methods for robot control development.  \nThe robotic literature contains multiple surveys to track the rapid progress of ML methods specially reinforcement learning (RL) and deep learning (DL) in robotic problems. Amarjyoti [2] focused on the RL and deep RL (DRL) methods applied to learning robotic manipulation without investigating fieldspecific applications. Moreover, Amarjyoti [2] only considered discrete robots and neglected soft ones. Thuruthel et al. [3] and Kim et al. [4] covered this shortcoming by investigating control strategies for soft manipulators.  \nGiven the complexity of robotic manipulation, learning controllers from scratch is challenging and sometimes costly. Moreover, adapting to sudden changes and performing multi-tasking is not trivial. Imitation learning can be used to reproduce expert’s behaviors on the robot and generalize to new environments ","cbCaida4W23TgxLi","https://ap.wps.com/l/cbCaida4W23TgxLi","pdf",5983007,1,69,"English","en",105,"# Introduction\n## Motivation and Challenges\n## Learning Controllers from Demonstrations\n## Environment Perception and Vision-Based Grasping","[{\"question\":\"Why is robot manipulation difficult compared with human manipulation?\",\"answer\":\"Humans benefit from flexible bodies and strong vision, while robotic systems must replicate perception and actuation through often simplified counterparts. As robot complexity increases, building suitable controllers becomes harder.\"},{\"question\":\"How do machine learning methods help with robotic manipulation?\",\"answer\":\"Machine learning can learn controllers from expert demonstrations, addressing the effort needed to hand-code trajectories. It also supports generalization to new environments and adaptation to changes through learning-based approaches.\"},{\"question\":\"What perception capability is highlighted as essential for manipulation tasks?\",\"answer\":\"Manipulation depends on determining an object’s position relative to the robot end-effector. The survey discusses environment perception and reviews vision-based grasping approaches.\"}]","Machine Learning Meets Advanced Robotic Manipulation - Survey and Research Directions | PDF",1785726278,174,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-meets-advanced-robotic-manipulation-survey-and-research-directions","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-meets-advanced-robotic-manipulation-survey-and-research-directions/119780/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is robot manipulation difficult compared with human manipulation?","Question",{"text":75,"@type":76},"Humans benefit from flexible bodies and strong vision, while robotic systems must replicate perception and actuation through often simplified counterparts. As robot complexity increases, building suitable controllers becomes harder.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How do machine learning methods help with robotic manipulation?",{"text":80,"@type":76},"Machine learning can learn controllers from expert demonstrations, addressing the effort needed to hand-code trajectories. It also supports generalization to new environments and adaptation to changes through learning-based approaches.",{"name":82,"@type":73,"acceptedAnswer":83},"What perception capability is highlighted as essential for manipulation tasks?",{"text":84,"@type":76},"Manipulation depends on determining an object’s position relative to the robot end-effector. 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