Manipulation of Articulated Objects Using Dual-arm Robots via Answer Set Programming

Author:

BERTOLUCCI RICCARDO,CAPITANELLI ALESSIO,DODARO CARMINEORCID,LEONE NICOLA,MARATEA MARCOORCID,MASTROGIOVANNI FULVIO,VALLATI MAURO

Abstract

AbstractThe manipulation of articulated objects is of primary importance in Robotics and can be considered as one of the most complex manipulation tasks. Traditionally, this problem has been tackled by developing ad hoc approaches, which lack flexibility and portability. In this paper, we present a framework based on answer set programming (ASP) for the automated manipulation of articulated objects in a robot control architecture. In particular, ASP is employed for representing the configuration of the articulated object for checking the consistency of such representation in the knowledge base and for generating the sequence of manipulation actions. The framework is exemplified and validated on the Baxter dual-arm manipulator in the first, simple scenario. Then, we extend such scenario to improve the overall setup accuracy and to introduce a few constraints in robot actions execution to enforce their feasibility. The extended scenario entails a high number of possible actions that can be fruitfully combined together. Therefore, we exploit macro actions from automated planning in order to provide more effective plans. We validate the overall framework in the extended scenario, thereby confirming the applicability of ASP also in more realistic Robotics settings and showing the usefulness of macro actions for the robot-based manipulation of articulated objects.

Publisher

Cambridge University Press (CUP)

Subject

Artificial Intelligence,Computational Theory and Mathematics,Hardware and Architecture,Theoretical Computer Science,Software

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A framework for neurosymbolic robot action planning using large language models;Frontiers in Neurorobotics;2024-06-04

2. CNL2ASP: Converting Controlled Natural Language Sentences into ASP;Theory and Practice of Logic Programming;2023-12-20

3. Structure from Action: Learning Interactions for 3D Articulated Object Structure Discovery;2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS);2023-10-01

4. Logic programming for deliberative robotic task planning;Artificial Intelligence Review;2023-01-18

5. Task Allocation for Multi-robot Task and Motion Planning: A Case for Object Picking in Cluttered Workspaces;AIxIA 2021 – Advances in Artificial Intelligence;2022

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