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This study offers a comprehensive discussion of the future role of robots and artificial intelligence (AI) in U.S. recycling under different policy environments and its impact on the workforce. The state of recycling in the U.S. is changing rapidly, with techno-economic developments transforming the efficacy and sustainability of recycling and the workforce it employs. This study describes the technical, social, and policy drivers that influence U.S. municipal solid waste (MSW) management and explores pathways for more sustainable outcomes by focusing on different technology options for the sorting of recyclables in material recovery facilities (MRFs). This study presents four distinct scenario storylines for U.S. recycling by 2050 that contrast recycling and robotic futures, particularly with MRFs that maximize material recovery, worker experience, and economic competitiveness, respectively. This study finds that a recycling scenario defined by strong policy support for recycling and the addition of increasingly flexible, collaborative technology in the form of robotics coupled with AI-driven vision systems, offers the greatest potential for better results. Less certain is the role of MRFs by 2050 based on the full cost for public actors and substantial changes in private industry. Insights from this study can directly inform future techno-economic analyses, technology decisions, and policy recommendations. 

Over the past decade, robots have emerged as a new sorting technology for material recovery facilities (MRFs), enabled through dramatic advances in robotics and artificial intelligence (AI). These advances allow robots to become ‘smart’ by coupling them with AI driven vision systems, able to distinguish recyclables by material type. By integrating robotics, MRFs hope to increase sorting speed and accuracy, reduce their residuals, and to become more resilient towards worker shortages. To better understand the economic implications, this study presents a techno-economic analysis of a representative MRF in the U.S. that integrates robotics and compare it to a similar MRF without robotics integration. We compare the metrics net present value, discounted internal rate of return, and payback period for a mid-size MRF with and without robotic integration and add an uncertainty analysis to inform about the most important factors to consider. The results of the techno-economic analysis can inform MRF operators in their future decision-making. 

The field of Human-Robot Collaboration (HRC) has seen a considerable amount of progress in recent years. Thanks in part to advances in control and perception algorithms, robots have started to work in increasingly unstructured environments, where they operate side by side with humans to achieve shared tasks. However, little progress has been made toward the development of systems that are truly effective in supporting the human, proactive in their collaboration, and that can autonomously take care of part of the task. In this work, we present a collaborative system capable of assisting a human worker despite limited manipulation capabilities, incomplete model of the task, and partial observability of the environment. Our framework leverages information from a high-level, hierarchical model that is shared between the human and robot and that enables transparent synchronization between the peers and mutual understanding of each other’s plan. More precisely, we firstly derive a partially observable Markov model from the high-level task representation; we then use an online Monte-Carlo solver to compute a short-horizon robot-executable plan. The resulting policy is capable of interactive replanning on-the-fly, dynamic error recovery, and identification of hidden user preferences. We demonstrate that the system is capable of robustly providing support to the human in a realistic furniture construction task. 

In this paper, we introduce Ommie, a novel robot that supports deep breathing practices for the purposes of anxiety reduction. The robot’s primary function is to guide users through a series of extended inhales, exhales, and holds by way of haptic interactions and audio cues. We present core design decisions during development, such as robot morphology and tactility, as well as the results of a usability study in collaboration with a local wellness center. Interacting with Ommie resulted in a significant reduction in STAI-6 anxiety measures, and participants found the robot intuitive, approachable, and engaging. Participants also reported feelings of focus and companionship when using the robot, often elicited by the haptic interaction. These results show promise in the robot’s capacity for supporting mental health. 

Robot-to-human handovers are common exercises in many robotics application domains. The requirements of handovers may vary across these different domains. In this paper, we first devised a taxonomy to organize the diverse and sometimes contradictory requirements. Among these, task-oriented handovers are not well-studied but important because the purpose of the handovers in human-robot collaboration (HRC) is not merely to pass an object from a robot to a human receiver, but to enable the receiver to use it in a subsequent tool-use task. A successful task-oriented handover should incorporate task-related information - orienting the tool such that the human can grasp it in a way that is suitable for the task. We identified multiple difficulty levels of task-oriented handovers, and implemented a system to generate handovers with novel tools on a physical robot. Unlike previous studies on task-oriented handovers, we trained the robot with tool-use demonstrations rather than handover demonstrations, since task-oriented handovers are dependent on the tool usages in the subsequent task. We demonstrated that our method can adapt to all difficulty levels of task-oriented handovers, including tasks that matched the typical usage of the tool, tasks that required an improvised or unusual usage of the tool, and tasks where the handover was adapted to the pose of a manipulandum. 

Regular exercise provides many mental and physical health benefits. However, when exercises are done incorrectly, it can lead to injuries. Because the COVID-19 pandemic made it challenging to exercise in communal spaces, the growth of virtual fitness programs was accelerated, putting people at risk of sustaining exercise-related injuries as they received little to no feedback on their exercising techniques. Colocated robots could be one potential enhancement to virtual training programs as they can cause higher learning gains, more compliance, and more enjoyment than non-co-located robots. In this study, we compare the effects of a physically present robot by having a person exercise either with a robot (robot condition) or a video of a robot displayed on a tablet (tablet condition). Participants (N=25) had an exercise system in their homes for two weeks. Participants who exercised with the colocated robot made fewer mistakes than those who exercised with the video-displayed robot. Furthermore, participants in the robot condition reported a higher fitness increase and more motivation to exercise than participants in the tablet condition.