On the manipulation of articulated objects in human-robot cooperation scenarios
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Abstract (arXiv)
Articulated and flexible objects constitute a challenge for robot manipulation tasks but are present in different real-world settings, including home and industrial environments. Current approaches to the manipulation of articulated and flexible objects employ ad hoc strategies to sequence and perform actions on them depending on a number of physical or geometrical characteristics related to those objects, as well as on an a priori classification of target object configurations. In this paper, we propose an action planning and execution framework, which (i) considers abstract representations of articulated or flexible objects, (ii) integrates action planning to reason upon such configurations and to sequence an appropriate set of actions with the aim of obtaining a target configuration provided as a goal, and (iii) is able to cooperate with humans to collaboratively carry out the plan. On the one hand, we show that a trade-off exists between the way articulated or flexible objects are perceived and how the system represents them. Such a trade-off greatly impacts on the complexity of the planning process. On the other hand, we demonstrate the system's capabilities in allowing humans to interrupt robot action execution, and - in general - to contribute to the whole manipulation process. Results related to planning performance are discussed, and examples of a Baxter dual-arm manipulator performing actions collaboratively with humans are shown.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Jan 2018.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 1; 2024: 0; 2025: 3; 2026: 1.
7 papers.
- 2023A Framework for Neurosymbolic Robot Action Planning using Large Language Modelscited by 11
- 2021Topologically-Informed Atlas Learningcited by 4
- 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
- 2025Achieving Scalable Robot Autonomy via neurosymbolic planning using lightweight local LLMcited by 2
- 2025Intuitive Programming, Adaptive Task Planning, and Dynamic Role Allocation in Human-Robot Collaborationcited by 1
- 2026A Horizon-slicing Approach to Minimum Obstacle Displacement Planning for Robot Navigationcited by 0
- 2026A Horizon-slicing Approach to Minimum Obstacle Displacement Planning for Robot Navigation19 Sep 2026
- 2025Intuitive Programming, Adaptive Task Planning, and Dynamic Role Allocation in Human-Robot Collaboration11 Nov 2025
- 2025Achieving Scalable Robot Autonomy via neurosymbolic planning using lightweight local LLM13 May 2025
- 2025A Task and Motion Planning Framework Using Iteratively Deepened AND/OR Graph Networks10 Mar 2025
- 2023A Framework for Neurosymbolic Robot Action Planning using Large Language Models1 Mar 2023
- 2022Structure from Action: Learning Interactions for Articulated Object 3D Structure Discovery19 Jul 2022
- 2021Topologically-Informed Atlas Learning1 Oct 2021