# On the manipulation of articulated objects in human-robot cooperation scenarios

arXiv: https://arxiv.org/abs/1801.01757 · HTML: https://mlsanity.com/paper/1801.01757/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jan 2018.

## Built on by (most cited first, 7 of 7)

- [A Framework for Neurosymbolic Robot Action Planning using Large Language Models](https://mlsanity.com/paper/2303.00438/) (2023)
- [Topologically-Informed Atlas Learning](https://mlsanity.com/paper/2110.00429/) (2021)
- [Structure from Action: Learning Interactions for Articulated Object 3D Structure Discovery](https://mlsanity.com/paper/2207.08997/) (2022)
- [A Task and Motion Planning Framework Using Iteratively Deepened AND/OR Graph Networks](https://mlsanity.com/paper/2503.07700/) (2025)
- [Achieving Scalable Robot Autonomy via neurosymbolic planning using lightweight local LLM](https://mlsanity.com/paper/2505.08492/) (2025)
- [Intuitive Programming, Adaptive Task Planning, and Dynamic Role Allocation in Human-Robot Collaboration](https://mlsanity.com/paper/2511.08732/) (2025)
- [A Horizon-slicing Approach to Minimum Obstacle Displacement Planning for Robot Navigation](https://mlsanity.com/paper/2609.22974/) (2026)

## 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.