[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-229039-105":59,"doc-detail-229039-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","state-supervised-steering-function-for-sampling-based-kinodynamic-planning","State Supervised Steering Function for Sampling-based Kinodynamic Planning","","Sampling-based motion planners such as RRT* and BIT* require steering functions to connect sampled states with time-optimal behavior in kinodynamic settings. Exact steering functions normally need analytical solutions or nonlinear programming solvers to solve boundary value problems, but both options are impractical for many real domains. State Supervised Steering Function (S3F) learns time-optimal steering functions, producing near-optimal steering outcomes orders of magnitude faster than NLP while improving RRT* cost and runtime on multiple robot domains, with a proof of probabilistic completeness for the S3F-modified planner.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/state-supervised-steering-function-for-sampling-based-kinodynamic-planning/229039/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/state-supervised-steering-function-for-sampling-based-kinodynamic-planning/229039.png","ImageObject",300,407,{"name":92,"@type":93},"Aladdin","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-06","2026-09-10",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",11,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"Why are steering functions important for sampling-based kinodynamic planning?","Question",{"text":113,"@type":114},"RRT*-style kinodynamic planners rely on steering functions to generate time-optimal connections between sampled states. Without efficient steering functions, optimal kinodynamic planning becomes computationally difficult.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"What makes exact steering function computation difficult in practice?",{"text":118,"@type":114},"Exact methods require analytical solutions to time-optimal control or expensive nonlinear programming solvers to solve boundary value problems. Many real-world domains lack analytical solutions, and NLP solvers are too costly.",{"name":120,"@type":111,"acceptedAnswer":121},"How does S3F address the computational bottleneck and what is its impact on RRT*?",{"text":122,"@type":114},"S3F learns time-optimal steering functions to produce near-optimal steering behavior much faster than NLP-based steering. Experiments show RRT* using S3F improves both solution cost and runtime on multiple challenging robot domains.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},229039,1789035132,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":105,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":130,"read_time":144,"preview_page_count":4},2336478503145,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","State Supervised Steering Function for Sampling-based  \nKinodynamic Planning  \nPranav Atreya  \nUniversity of Texas at Austin Austin, TX, United States [pranavatreya@utexas.edu](pranavatreya@utexas.edu)  \nJoydeep Biswas  \nUniversity of Texas at Austin Austin, TX, United States [joydeepb@cs.utexas.edu](joydeepb@cs.utexas.edu)  \narXiv :2206 .07227v1 [ cs .RO] 15 Jun 2022  \nABSTRACT  \nSampling-based motion planners such as RRT* and BIT*, when applied to kinodynamic motion planning, rely on steering functions to generate time-optimal solutions connecting sampled states. Implementing exact steering functions requires either analytical solutions to the time-optimal control problem, or nonlinear programming (NLP) solvers to solve the boundary value problem given the system’s kinodynamic equations. Unfortunately, analytical solutions are unavailable for many real-world domains, and NLP solvers are prohibitively computationally expensive, hence fast and optimal kinodynamic motion planning remains an open problem. We provide a solution to this problem by introducing State Supervised Steering Function (S3F), a novel approach to learn time-optimal steering functions. S3F is able to produce near-optimal solutions to the steering function orders of magnitude faster than its NLP counterpart. Experiments conducted on three challenging robot domains show that RRT* using S3F significantly outperforms stateof-the-art planning approaches on both solution cost and runtime. We further provide a proof of probabilistic completeness of RRT* modified to use S3F.  \nKEYWORDS  \nKinodynamic Motion Planning; Learning Steering Functions; Sampling-based Planning  \nACM Reference Format:  \nPranavAtreya and Joydeep Biswas. 2022. State Supervised Steering Function for Sampling-based Kinodynamic Planning. In Proc. of the 21st International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2022), Online, May 9–13, 2022, IFAAMAS, 11 pages.  \n1 INTRODUCTION AND RELATED WORK  \nThis work tackles the kinodynamic motion planning (KDMP) problem, which is the problem of computing a kinodynamically feasible motion plan that takes a robot from an initial configuration to agoal region. We begin by formally defining the KDMP problem and then survey the various approaches to solving it.  \nLet 􀀭􀀘 be the configuration space of the robot. The state space 􀀭 is defined as the Cartesian product of 􀀭􀀘 with 􀀭􀀙, the set of dynamics variables needed to fully describe the dynamics of the robot at any given instance in time. 􀀭􀀙 typically consists of time derivatives of elements of 􀀭􀀘 . Let 􀀪 be the control space of the robot. The kinodynamic constraints are described by the differential equation 􀁇¤(􀁃) = 􀀵 (􀁇(􀁃),􀁄 (􀁃)), where 􀁇(􀁃) ∈ 􀀭 and 􀁄 (􀁃) ∈ 􀀪 . The  \nProc. of the 21st International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2022), P. Faliszewski, V. Mascardi, C. Pelachaud, M.E. Taylor (eds.), May 9–13, 2022, Online. © 2022 International Foundation for Autonomous Agents and Multiagent Systems ([www.ifaamas.org](www.ifaamas.org)). All rights reserved.  \nKDMP problem differs from the purely kinematic motion planning (KMP) problem in that the KMP problem operates only on the configuration space 􀀭􀀘. Let 􀀭obs ∈ 􀀭 be the set of obstacle-colliding states and let 􀀭free = 􀀭 \\􀀭obs be the set of valid states. Let 􀁇init ∈􀀭free be the initial state of the robot and let 􀀭goal ⊂ 􀀭free be the goal region. The objective of the KDMP problem is to find a collision free path that takes the robot from 􀁇init to 􀀭goal while obeying the kinodynamic constraints. The solution to the KDMP problem is a mapping 􀀲 (􀁃) : [0, 􀁃􀀵] → 􀀪 from time to control inputs such that applying 􀀲 (􀁃) starting from the state 􀁇init traces out a path 􀁢(􀁃) : [0, 􀁃􀀵] → 􀀭free such that 􀁢(􀁃􀀵) ∈ 􀀭goal. A motion plan is considered optimal if it minimizes some cost function 􀀘(􀁃􀀵, 􀀲, 􀁢) . The time-optimal solution minimizes the total time 􀁃􀀵 .  \nWe review the state ofthe art approaches to solving the KDMP problem","cbCaiqQaCcfJgPtl","https://ap.wps.com/l/cbCaiqQaCcfJgPtl","pdf",4606997,"English","# Introduction and Related Work\n## Kinodynamic motion planning problem formulation\n## Comparison with kinematic motion planning\n## State-lattice search-based planning\n## Sampling-based planning and RRT\n## Learning-integrated RRT approaches","[{\"question\":\"Why are steering functions important for sampling-based kinodynamic planning?\",\"answer\":\"RRT*-style kinodynamic planners rely on steering functions to generate time-optimal connections between sampled states. Without efficient steering functions, optimal kinodynamic planning becomes computationally difficult.\"},{\"question\":\"What makes exact steering function computation difficult in practice?\",\"answer\":\"Exact methods require analytical solutions to time-optimal control or expensive nonlinear programming solvers to solve boundary value problems. Many real-world domains lack analytical solutions, and NLP solvers are too costly.\"},{\"question\":\"How does S3F address the computational bottleneck and what is its impact on RRT*?\",\"answer\":\"S3F learns time-optimal steering functions to produce near-optimal steering behavior much faster than NLP-based steering. Experiments show RRT* using S3F improves both solution cost and runtime on multiple challenging robot domains.\"}]","State Supervised Steering Function for Sampling-based Kinodynamic Planning | PDF",28]