Planning Method for Skill-Based Control of Robots Using a PLC as Skill Trigger

Andreas Gaugenrieder; Hari Hara Balasubramaniam; Jannik Möhrle; Rüdiger Daub

cs.ROFeb 28, 2026

Planning Method for Skill-Based Control of Robots Using a PLC as Skill Trigger

Authors: Andreas Gaugenrieder, Hari Hara Balasubramaniam, Jannik Möhrle, Rüdiger Daub

Organizations: Fraunhofer Institute for Casting, Composite and Processing Technology IGCV · Fraunhofer Institute for Casting, Composite and Processing Technology IGCV, Am Technologiezentrum 10, 86159 Augsburg, Germany · Institute for Machine Tools and Industrial Management, Technical University of Munich · Institute for Machine Tools and Industrial Management, Technical University of Munich, Bolzmanstraße 15, 85748 Garching b. München, Germany

Abstract

Skill-based programming of robots provides a flexible approach for automation. Existing solutions neglect the optimization of motion sequences, leading to inefficiencies in execution. This work introduces a planning method that enhances skill-based robot programming by integrating motion sequence optimization. This optimization leads to a new MoveContinuousSkill. The software for executing the MoveContinuousSkill is implemented on a Programmable Logic Controller and applied across multiple robotic systems. Experimental results demonstrate a significant improvement in execution time through optimized motion sequence.

Robotic Control

Robotic control studies how machines use sensing and computation to produce reliable movement and interaction in changing environments.

See all 584 papers

MOSAIC: Skill-Centric Manipulation Planning with Physics Simulation
Apr 23, 2025Itamar Mishani, Yorai Shaoul, Maxim LikhachevLong-Horizon Robotic Manipulation Robot Task Planning
Robotics Institute, School of Computer Science, Carnegie Mellon University, United States
Multi-Robot Multi-Goal Motion Planning with Stochastic Skills
Oct 6, 2026William Schnyder, Valentin N. Hartmann, Stelian CorosMulti-Robot Motion Planning Motion Planning
Computational Robotics Lab (CRL), ETH Zürich
Skill Sequence Planning for Collaborative Multi-Robot Construction
Sep 22, 2026Xi Wang, Bo Fu, Carol C. Menassa +2Robotic Construction Multi-Robot Coordination
Department of Construction Science, Texas A&M University, College Station, TX 77843, USA · Amazon Robotics, North Reading, MA 01864, USA · Department of Civil and Environmental Engineering, University of Michigan, MI 48109, USA +1

Apr 23, 2025J/K move · Enter open · S save

MOSAIC: Skill-Centric Manipulation Planning with Physics Simulation

Itamar Mishani, Yorai Shaoul, Maxim Likhachev

Robotics Institute, School of Computer Science, Carnegie Mellon University, United States

Long-Horizon Robotic Manipulation Robot Task Planning

Open paper AskPDF arXiv

Planning long-horizon manipulation motions using a set of predefined skills is a central challenge in robotics; solving it efficiently could enable general-purpose robots to tackle novel tasks by flexibly composing generic skills. Solutions to this problem lie in an infinitely vast space of parameterized skill sequences -- a space where common incremental methods struggle to find sequences that have non-obvious intermediate steps. Some approaches reason over lower-dimensional, symbolic spaces, which are more tractable to explore but may be brittle and are laborious to construct. In this work, we introduce MOSAIC, a skill-centric, multi-directional planning approach that targets these challenges by reasoning about which skills to employ and where they are most likely to succeed, by utilizing physics simulation to estimate skill execution outcomes. Specifically, MOSAIC employs two complementary skill families: Generators, which identify ``islands of competence'' where skills are demonstrably effective, and Connectors, which link these skill-trajectories by solving boundary value problems. By focusing planning efforts on regions of high competence, MOSAIC efficiently discovers physically-grounded solutions. We demonstrate its efficacy on complex long-horizon problems in both simulation and the real world, using a diverse set of skills including generative diffusion models, motion planning algorithms, and manipulation-specific models. Visit skill-mosaic.github.io for demonstrations and examples.

Abstract

Robotic Control

Explore similar work

MOSAIC: Skill-Centric Manipulation Planning with Physics Simulation

Multi-Robot Multi-Goal Motion Planning with Stochastic Skills

Skill Sequence Planning for Collaborative Multi-Robot Construction