Agentic Neuro-Symbolic Planning and Commissioning for Human-in-the-Loop Industrial Robotics with Digital Twins

Flexible robotic automation requires systems that interpret operator intent, verify physical feasibility, and recover from execution failures across both the planning and execution stages. This paper proposes an agentic neuro-symbolic framework for human-in-the-loop industrial robotics, in which LLMs are used for tasks that require language understanding or contextual reasoning, while all verification, sequencing, and execution remain deterministic. The framework adapts the Planner-Generator-Evaluator (PGE) harness pattern from software engineering into a Specifier-Designer-Inspector (SDI) architecture for industrial robotics, combined with LangGraph-based dynamic routing for failure recovery. A two-tier recovery mechanism addresses structure-level replanning through context-aware orchestration and execution-level geometric failures through deterministic recovery skills. A Unity3D digital twin supports human inspection, modification, and re-verification prior to physical execution. Evaluated on natural-language commands across multiple difficulty levels against ten baselines, the proposed method achieves the highest task success. Ablation results confirm that structured command expansion, symbolic verification, selective LLM routing, and recovery skills are each individually necessary.

Marinarium: a New Arena to Bring Maritime Robotics Closer to Shore

This paper presents the Marinarium, a modular and stand-alone underwater research facility designed to provide a realistic testbed for maritime and space-analog robotic experimentation in a resource-efficient manner. The Marinarium combines a fully instrumented underwater and aerial operational volume, extendable via a retractable roof for real-weather conditions, a digital twin in the SMaRCSim simulator and tight integration with a space robotics laboratory. All of these result from design choices aimed at bridging simulation, laboratory validation, and field conditions. We compare the Marinarium to similar existing infrastructures and illustrate how its design enables a set of experiments in four open research areas within field robotics. First, we exploit high-fidelity dynamics data from the tank to demonstrate the potential of learning-based system identification approaches applied to underwater vehicles. We further highlight the versatility of the multi-domain operating volume via a rendezvous mission with a heterogeneous fleet of robots across underwater, surface, and air. We then illustrate how the presented digital twin can be utilized to reduce the reality gap in underwater simulation. Finally, we demonstrate the potential of underwater surrogates for spacecraft navigation validation by executing spatiotemporally identical inspection tasks on a planar space-robot emulator and a neutrally buoyant \gls{rov}. In this work, by sharing the insights obtained and rationale behind the design and construction of the Marinarium, we hope to provide the field robotics research community with a blueprint for bridging the gap between controlled and real offshore and space robotics experimentation.

Robust Visual Servoing under Human Supervision for Assembly Tasks

We propose a framework enabling mobile manipulators to reliably complete pick-and-place tasks for assembling structures from construction blocks. The picking uses an eye-in-hand visual servoing controller for object tracking with Control Barrier Functions (CBFs) to ensure fiducial markers in the blocks remain visible. An additional robot with an eye-to-hand setup ensures precise placement, critical for structural stability. We integrate human-in-the-loop capabilities for flexibility and fault correction and analyze robustness to camera pose errors, proposing adapted barrier functions to handle them. Lastly, experiments validate the framework on 6-DoF mobile arms.

Efficient Coordination and Synchronization of Multi-Robot Systems Under Recurring Linear Temporal Logic

We consider multi-robot systems under recurring tasks formalized as linear temporal logic (LTL) specifications. To solve the planning problem efficiently, we propose a bottom-up approach combining offline plan synthesis with online coordination, dynamically adjusting plans via real-time communication. To address action delays, we introduce a synchronization mechanism ensuring coordinated task execution, leading to a multi-agent coordination and synchronization framework that is adaptable to a wide range of multi-robot applications. The software package is developed in Python and ROS2 for broad deployment. We validate our findings through lab experiments involving nine robots showing enhanced adaptability compared to previous methods. Additionally, we conduct simulations with up to ninety agents to demonstrate the reduced computational complexity and the scalability features of our work.