This Future of Work at the Human-Technology Frontier (FW-HTF) project investigates a novel human-robot collaboration architecture to improve efficiency and profitability in the recycling industry, while re-creating recycling jobs to be safer, cleaner, and more meaningful. The specific goal is to improve the waste sorting process, that is, the separation of mixed waste into plastics, paper, metal, glass, and non-recyclables. The US scrap recycling industry -- which represents $117 billion in annual economic activity and more than 530,000 US jobs -- is struggling to meet increasingly challenging standards in domestic and international markets. A major problem for the industry is poor sorting of waste, resulting in materials impurity and a significant decrease in the quality and value of the recycled product. Human perception and judgement are essential to handle the object variety, clutter level and changing characteristics of the waste stream. Yet waste-sorting workers currently face health risks and discomfort arising from sharp and heavy objects, toxic materials, noise, vibration, dust, noisome odors, and poor heating, ventilation, and air conditioning. The innovative robotics component of this project, especially in object detection, manipulation, and human-robot interaction, will allow new sorting facility architectures, creating new, safer roles for human workers. The project complements these technological advances with economic analyses to determine the facility configurations that best remove processing bottlenecks, target materials of high value, and boost the end-to-end efficiency of the recycling process. Division of labor between humans and robots will be investigated to improve job desirability and worker motivation, incorporating consideration of the workers' well-being. In particular, the project will explore ways to utilize robots to amplify worker expertise and value. A holistic and interconnected research approach will be taken for all these aspects, i.e. developing robotics technology, designing the human-machine interfaces, investigating workers' workers' role in the new sorting plant architectures, and understanding and incorporating workers' needs and well-being into the design process.
This project will develop the appropriate robotics technology for recycling industry deployment, which will require advancing the state of the art in waste classification and manipulation to handle the conditions associated with recycling facilities. Deep Neural Networks-based object detection and semantic segmentation frameworks will be designed for rich, multi-modal sensor data in order to solve challenges regarding a high-level of clutter, occlusion and object variety. Novel robotic manipulation algorithms based on dynamic and soft manipulation strategies will be utilized to separate and pick classified items from the cluttered waste stream. Robust and dexterous robot hardware will be developed, including the robotic arms and end effectors. Human-machine interfaces will be designed and implemented to achieve these tasks in an intuitive, efficient and practical workflow that optimizes the contributions of both human workers and automated technologies. The robotics technology will also allow expanding the facilities from simply sorting the incoming materials into a whole recycling ecosystem; additional process lines for onsite materials processing units will enable conveying partially-finished products to next stage manufacturers. This expansion will require a novel systems approach, and will help achieve more efficient recycling plants and a much more comprehensive employment ladder for current and new workers. These technological and structural changes in the interactional system of work will shift both the task and relational landscape of the work. The effect of these shifts on worker satisfaction and motivation will be investigated via worker interviews with simulated systems. The new technological landscape will be formed accordingly for improved work experience.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
