Symbolic Manipulation Planning With Discovered Object and Relational Predicates

作     者：Ahmetoglu, Alper Oztop, Erhan Ugur, Emre 

作者机构：Brown Univ Dept Comp Sci Providence RI 02912 USA Ozyegin Univ Dept Comp Sci TR-34794 Istanbul Turkiye Osaka Univ OTRI SISReC Osaka 5650871 Japan Bogazici Univ Dept Comp Engn TR-34342 Istanbul Turkiye 

出 版 物：《IEEE ROBOTICS AND AUTOMATION LETTERS》 (IEEE Robot. Autom.)

年 卷 期：2025年第10卷第2期

页      面：1968-1975页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0811[工学-控制科学与工程] 

基　　金：TUBITAK (The Scientific and Technological Research Council of Turkey) [120E274, JP23K24926] New Energy and Industrial Technology Development Organization (NEDO) [JPNP16007] JST, CREST [JPMJCR17A4] European Union through INVERSE Project Boston Dynamics AI Institute 

主　　题：Developmental robotics learning categories and concepts deep learning methods Developmental robotics learning categories and concepts deep learning methods 

摘      要：Discovering the symbols and rules that can be used in long-horizon planning from a robot s unsupervised exploration of its environment and continuous sensorimotor experience is a challenging task. The previous studies proposed learning symbols from single or paired object interactions and planning with these symbols. In this work, we propose a system that learns rules with discovered object and relational symbols that encode an arbitrary number of objects and the relations between them, converts those rules to Planning Domain Description Language (PDDL), and generates plans that involve affordances of the arbitrary number of objects to achieve tasks. We validated our system with box-shaped objects in different sizes and showed that the system can develop a symbolic knowledge of pick-up, carry, and place operations, taking into account object compounds in different configurations, such as boxes would be carried together with a larger box that they are placed on. We also compared our method with other symbol learning methods and showed that planning with the operators defined over relational symbols gives better planning performance compared to the baselines.