Manipulation of Articulated Objects using Dual-arm Robots via Answer Set Programming
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Abstract (arXiv)
The 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 a 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. Under consideration in Theory and Practice of Logic Programming (TPLP).
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Oct 2020.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 3; 2024: 0; 2025: 2.
7 papers.
- 2023A Framework for Neurosymbolic Robot Action Planning using Large Language Modelscited by 11
- 2023Logic programming for deliberative robotic task planningcited by 8
- 2021Task Allocation for Multi-Robot Task and Motion Planning: a case for Object Picking in Cluttered Workspacescited by 7
- 2023CNL2ASP: converting controlled natural language sentences into ASPcited by 5
- 2022Structure from Action: Learning Interactions for Articulated Object 3D Structure Discoverycited by 4
- 2025A Task and Motion Planning Framework Using Iteratively Deepened AND/OR Graph Networkscited by 2
- 2025Learning Symbolic Persistent Macro-Actions for POMDP Solving Over Timecited by 2
- 2025Learning Symbolic Persistent Macro-Actions for POMDP Solving Over Time6 May 2025
- 2025A Task and Motion Planning Framework Using Iteratively Deepened AND/OR Graph Networks10 Mar 2025
- 2023CNL2ASP: converting controlled natural language sentences into ASP17 Nov 2023
- 2023A Framework for Neurosymbolic Robot Action Planning using Large Language Models1 Mar 2023
- 2023Logic programming for deliberative robotic task planning18 Jan 2023
- 2022Structure from Action: Learning Interactions for Articulated Object 3D Structure Discovery19 Jul 2022
- 2021Task Allocation for Multi-Robot Task and Motion Planning: a case for Object Picking in Cluttered Workspaces8 Oct 2021