[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-229034-105":59,"doc-detail-229034-en":130},{"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":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","state-supervised-steering-function-for-sampling-based-kinodynamic-planning-aamas-2022-paper","State Supervised Steering Function for Sampling-based Kinodynamic Planning - AAMAS 2022 Paper","","Sampling-based motion planning methods such as RRT* and BIT* require steering functions to connect sampled states while achieving time-optimal behavior in kinodynamic settings. Exact steering functions typically demand analytical time-optimal control solutions or expensive nonlinear programming solvers for the boundary value problem. This work introduces State Supervised Steering Function (S3F), which learns time-optimal steering functions from state information. S3F produces near-optimal solutions orders of magnitude faster than NLP baselines, yielding RRT* improvements on cost and runtime across three challenging robot domains, and provides probabilistic completeness for the modified planner.",{"@graph":69,"@context":122},[70,84,105],{"@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":30,"@type":76,"position":81},"https://docshare.wps.com/document/technology/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/state-supervised-steering-function-for-sampling-based-kinodynamic-planning-aamas-2022-paper/229034/",{"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-aamas-2022-paper/229034.png","ImageObject",300,407,{"name":92,"@type":93},"Aran","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-16","2026-09-10",true,{"@type":102,"interactionType":103,"userInteractionCount":29},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What challenge does S3F address in kinodynamic motion planning?","Question",{"text":112,"@type":113},"S3F targets the difficulty of computing exact steering functions for time-optimal solutions, which often require analytical solutions or nonlinear programming that is too slow for practical use.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does S3F improve over nonlinear programming steering functions?",{"text":117,"@type":113},"S3F learns steering functions to generate near-optimal trajectories significantly faster than NLP solvers, enabling faster kinodynamic planning.",{"name":119,"@type":110,"acceptedAnswer":120},"What planning method is evaluated with S3F, and what guarantees are provided?",{"text":121,"@type":113},"The document evaluates RRT* augmented with S3F, showing improved solution cost and runtime, and it provides a probabilistic completeness proof for the modified RRT* setup.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},229034,1789035121,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":29,"category_name":30,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":29,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"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":129,"read_time":144},137455076865,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","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)  \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, 9 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, including search-based planning, sampling-based planning, ","cbCaiobPSKQuUe5l","https://ap.wps.com/l/cbCaiobPSKQuUe5l","pdf",2505670,11,"English","# Abstract\n# Introduction and Related Work\n## Problem Definition and Optimality\n## Search-Based Planning Approaches\n## Sampling-Based Planning Approaches\n## Learning-Based Enhancements to RRT Variants","[{\"question\":\"What challenge does S3F address in kinodynamic motion planning?\",\"answer\":\"S3F targets the difficulty of computing exact steering functions for time-optimal solutions, which often require analytical solutions or nonlinear programming that is too slow for practical use.\"},{\"question\":\"How does S3F improve over nonlinear programming steering functions?\",\"answer\":\"S3F learns steering functions to generate near-optimal trajectories significantly faster than NLP solvers, enabling faster kinodynamic planning.\"},{\"question\":\"What planning method is evaluated with S3F, and what guarantees are provided?\",\"answer\":\"The document evaluates RRT* augmented with S3F, showing improved solution cost and runtime, and it provides a probabilistic completeness proof for the modified RRT* setup.\"}]","State Supervised Steering Function for Sampling-based Kinodynamic Planning - AAMAS 2022 Paper | PDF",28]