Publications

Daniel Swoboda, Hector Geffner

Conference on Robot Learning (CoRL) · 2026

Robotic rearrangement tasks require planning sequences of actions that fulfill high-level specifications while also being geometrically feasible. Symbolic abstractions make the combinatorial structure of rearrangement tasks tractable, but they usually do not fully capture the geometric constraints that determine which actions a motion policy can realize. This is due to the complexity of expressing the underlying geometric feasibility constraints in the symbolic model. We propose REAP (Rearrangement with Elicited Action Preconditions), a framework in which a relational graph neural network heuristic is learned over a simple symbolic abstraction. A motion policy serves as a supervisor that elicits the hidden geometric preconditions of the problem during training. Deployed with greedy best-first search, the learned heuristic generalizes out-of-distribution along object count, plan length, and object arrangement. Compared to classical heuristic search and TAMP baselines on the same instances, REAP requires approximately an order of magnitude fewer motion policy queries because the heuristic anticipates which low-level actions are geometrically feasible. We show that the same architecture, training recipe, and search procedure adapt to various robotic rearrangement domains.

Wenbo Ma, Daniel Swoboda, Matteo Tschesche, Till Hofmann

RoboCup Symposium · Oral Presentation · 2026

A LEGO Duplo–based benchmark for robotic assembly, motivated by the RoboCup Smart Manufacturing League, where low-level manipulation must meet task-level symbolic reasoning under physical constraints — a combination today's end-to-end methods handle unreliably. It contributes 400 tasks across four difficulty tiers and an open-vocabulary, assembly-by-disassembly baseline whose planning-based pipeline beats a modern vision-language-action approach at every tier. The benchmark, simulator, and baseline will be released openly.

Tarik Viehmann, Daniel Swoboda, Samridhi Kalra, Himanshu Grover, Gerhard Lakemeyer

18th International Conference on Agents and Artificial Intelligence (ICAART) · 2026

Brings CLIPS — a rule-based language well suited to knowledge-driven robot coordination — into the ROS 2 ecosystem as a CLIPS-Executive, building on the original from the Fawkes framework. Its flexibility is shown by layering a PDDL-based planning framework on top.

Supun Dissanayaka, Alexander Ferrein, Till Hofmann, Kosuke Nakajima, Mario Sanz-Lopez, Jesus Savage, Daniel Swoboda, Matteo Tschesche, Wataru Uemura, Tarik Viehmann, Shohei Yasuda

2025 RoboCup Symposium, Salvador, Bahia · 2025

Sets out the vision for the RoboCup Smart Manufacturing League, successor to the Logistics League. It widens the competition from production logistics into a full modern-factory scenario built from initially independent tracks — assembly, human-robot collaboration, humanoid robotics — that gradually merge, keeping the league relevant to current industrial-robotics challenges and welcoming to both newcomers and veteran teams.

Soren Munker, Daniel Swoboda, Karim El Zaatari, Nehel Malhotra, Lucas Manasses Pinheiro de Souza, Amon Goppert, Chi-Guhn Lee, Robert H Schmitt

Proceedings of the Changeable, Agile, Reconfigurable and Virtual Production Conference · 2023

Addresses disassembly sequence planning for sustainable remanufacturing, such as recovering lithium-ion cells from automotive battery packs. A CAD-based method partitions a product using community detection to derive disassembly sequences with fewer steps, helping make remanufacturing scalable.

Daniel Swoboda, Till Hofmann, Tarik Viehmann, Gerhard Lakemeyer

2022 Workshop on Planning and Robotics · 2022

Extends a goal-reasoning framework so cooperating mobile robots share not just their world models but their intentions, through 'promises' that certain facts will hold at some future point. Promises fit into the goal life cycle and map onto PDDL timed initial literals for planning, and are evaluated in a simplified logistics scenario.

Daniel Swoboda, Tarik Viehmann, Matteo Tschesche, Alexander Ferrein, Gerhard Lakemeyer

RoboCup Logistics League — Team Description Paper · 2023

Team description paper for Carologistics, the joint RWTH/FH Aachen RoboCup Logistics League team, summarising its centralised goal-reasoning agent, multi-agent coordination, and ROS-based software stack for the production-logistics scenario.

Daniel Maximilian Swoboda

Master's Thesis, RWTH Aachen University · 2024

Integrates task planning with imitation learning for robotic manipulation in cluttered, dynamic environments, jointly learning skill execution and the symbolic grounding that ties perception to a planning domain — so a robot can both reason about and carry out long-horizon tasks.

Daniel M Swoboda, Till Hofmann, Gerhard Lakemeyer, Matthias Jarke

Bachelor's Thesis, RWTH Aachen University · 2020

Develops mechanisms for logistics robots to share promises and requests within a multi-agent goal-reasoning framework, letting agents coordinate by communicating intended future world states — groundwork for the later 'promises' line of research.