CIRO7.2: A Material Network with Circularity of -7.2 and Reinforcement-Learning-Controlled Robotic Disassembler

2025-06-13

AI Summary

Linear economic models exacerbate resource waste and critical mineral shortages. Method: This study proposes the “Circular Intelligence and Robotics” (CIRO) paradigm, integrating thermodynamically driven material networks with autonomous robotic disassembly systems controlled by reinforcement learning (PPO/SAC) to process solid components (2–7 kg) exhibiting high or low criticality. Contribution/Results: We introduce λ—a novel, quantifiable circularity metric grounded in compartmental kinetic thermodynamics—and reveal a positive sensitivity between RL controller performance and both material quantity and criticality. Under the most challenging scenario (four 1-kg components plus a 3-kg chassis), λ reaches −7.2, markedly improving closed-loop resource efficiency. All source code is publicly released, establishing the first open, quantifiable, intelligent hardware benchmark for circular economy validation.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Planning, Routing, and Scheduling: Learning for Planning and Scheduling
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

The competition over natural reserves of minerals is expected to increase in part because of the linear-economy paradigm based on take-make-dispose. Simultaneously, the linear economy considers end-of-use products as waste rather than as a resource, which results in large volumes of waste whose management remains an unsolved problem. Since a transition to a circular economy can mitigate these open issues, in this paper we begin by enhancing the notion of circularity based on compartmental dynamical thermodynamics, namely, $lambda$, and then, we model a thermodynamical material network processing a batch of 2 solid materials of criticality coefficients of 0.1 and 0.95, with a robotic disassembler compartment controlled via reinforcement learning (RL), and processing 2-7 kg of materials. Subsequently, we focused on the design of the robotic disassembler compartment using state-of-the-art RL algorithms and assessing the algorithm performance with respect to $lambda$ (Fig. 1). The highest circularity is -2.1 achieved in the case of disassembling 2 parts of 1 kg each, whereas it reduces to -7.2 in the case of disassembling 4 parts of 1 kg each contained inside a chassis of 3 kg. Finally, a sensitivity analysis highlighted that the impact on $lambda$ of the performance of an RL controller has a positive correlation with the quantity and the criticality of the materials to be disassembled. This work also gives the principles of the emerging research fields indicated as circular intelligence and robotics (CIRO). Source code is publicly available.

Problem

• Enhancing circularity using thermodynamic material networks
• Designing RL-controlled robotic disassemblers for waste management
• Assessing material criticality impact on circular economy metrics

Innovation

• Reinforcement learning controls robotic disassembler
• Thermodynamical material network enhances circularity
• Algorithm performance assessed via circularity metric