Robotic waste sorting systems offer a scalable and consistent alternative to manual sorting for Construction and Demolition Waste (CDW) by reducing labor-intensive tasks and exposure to hazardous conditions, while enabling the extraction of high-purity materials (e.g., polymers) from the waste streams. Despite advancements in perception systems, manipulation and planning remain significant bottlenecks, limiting widespread adoption due to high complexity and cost. This paper introduces RaapWaste, a robot- and application-agnostic planning framework specifically designed for waste sorting, addressing challenges in motion planning, scheduling, and real-world integration. Built on open-source resources, RaapWaste employs a modular and flexible architecture, enabling integration of diverse planning techniques and scheduling strategies. The framework aims to simulate the performance of real-world sorting equipment (e.g., robots, grippers). To evaluate its effectiveness, we conducted simulations with articulated and delta robots, as well as real-world tests on CDW sorting. Metrics such as the Sorting Throughput (ST) and Sorting Ratio (SR) reveal the RaapWaste’s capability across different waste sorting cases. In simulation, the delta robot achieved an SR exceeding 95%, while the UR5e showed consistent performance. In real-world CDW experiments, the system achieved a peak SR of 99% and maintained 80% using the SPT scheduler.

The transition toward a circular economy necessitates high-quality material recovery from increasingly complex and heterogeneous waste streams. However, state-of-the-art robotic sorting algorithms primarily optimize system throughput, a proxy metric that treats all objects as equally valuable and often results in lower revenue and higher output stream contamination. This work addresses these limitations by introducing VARS (value-aware robotic sorting), a real-time decision-making framework designed to maximize total collected utility. By explicitly modeling sensor uncertainty and asymmetric contamination penalties, the framework shifts the sorting objective from simple pick-and-place (PnP) frequency to the maximization of recovered economic value and output stream purity. The proposed approach is validated through a dual-stage process involving realistic discrete-event simulations for statistical benchmarking and physical deployment on a high-speed industrial Delta robot. Results demonstrate that integrating value-based logic directly into the control loop ensures both the economic viability and operational reliability required by modern material recovery facilities.