# Using Knowledge Representation and Task Planning for Robot-agnostic Skills on the Example of Contact-Rich Wiping Tasks

arXiv: https://arxiv.org/abs/2308.14206 · HTML: https://mlsanity.com/paper/2308.14206/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 46 references that are in the map.
- First posted on arXiv 27 Aug 2023.

## Builds on (most cited first, 12 of 12)

- [Behavior Trees in Robotics and AI: An Introduction](https://mlsanity.com/paper/1709.00084/) (2017)
- [A Survey of Behavior Trees in Robotics and AI](https://mlsanity.com/paper/2005.05842/) (2020)
- [CoSTAR: Instructing Collaborative Robots with Behavior Trees and Vision](https://mlsanity.com/paper/1611.06145/) (2016)
- [Skill-based Multi-objective Reinforcement Learning of Industrial Robot Tasks with Planning and Knowledge Integration](https://mlsanity.com/paper/2203.10033/) (2022)
- [Learning Skill-based Industrial Robot Tasks with User Priors](https://mlsanity.com/paper/2208.01605/) (2022)
- [Learning to Adapt the Parameters of Behavior Trees and Motion Generators (BTMGs) to Task Variations](https://mlsanity.com/paper/2303.08209/) (2023)
- [Learning of Parameters in Behavior Trees for Movement Skills](https://mlsanity.com/paper/2109.13050/) (2021)
- [Combining Planning, Reasoning and Reinforcement Learning to solve Industrial Robot Tasks](https://mlsanity.com/paper/2212.03570/) (2022)
- [Productive Multitasking for Industrial Robots](https://mlsanity.com/paper/2108.11471/) (2021)
- [The CARESSES EU-Japan project: making assistive robots culturally competent](https://mlsanity.com/paper/1708.06276/) (2017)
- [A C++ Implementation of a Cartesian Impedance Controller for Robotic Manipulators](https://mlsanity.com/paper/2212.11215/) (2022)
- [Inverse Kinematics with Forward Dynamics Solvers for Sampled Motion Tracking](https://mlsanity.com/paper/1908.06252/) (2019)

## Built on by (most cited first, 1 of 1)

- [Adaptable Recovery Behaviors in Robotics: A Behavior Trees and Motion Generators(BTMG) Approach for Failure Management](https://mlsanity.com/paper/2404.06129/) (2024)

## Abstract (arXiv)

The transition to agile manufacturing, Industry 4.0, and high-mix-low-volume tasks require robot programming solutions that are flexible. However, most deployed robot solutions are still statically programmed and use stiff position control, which limit their usefulness. In this paper, we show how a single robot skill that utilizes knowledge representation, task planning, and automatic selection of skill implementations based on the input parameters can be executed in different contexts. We demonstrate how the skill-based control platform enables this with contact-rich wiping tasks on different robot systems. To achieve that in this case study, our approach needs to address different kinematics, gripper types, vendors, and fundamentally different control interfaces. We conducted the experiments with a mobile platform that has a Universal Robots UR5e 6 degree-of-freedom robot arm with position control and a 7 degree-of-freedom KUKA iiwa with torque control.