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Despite the existence of robots that can physically lift heavy loads, robots that can collaborate with people to move heavy objects are not readily available. This article makes progress toward effective human-robot co-manipulation by studying 30 human-human dyads that collaboratively manipulated an object weighing\(27 \mathrm{kg}\)without being co-located (i.e., participants were at either end of the extended object). Participants maneuvered around different obstacles with the object while exhibiting one of four modi–the manner or objective with which a team moves an object together–at any given time. Using force and motion signals to classify modus or behavior was the primary objective of this work. Our results showed that two of the originally proposed modi were very similar, such that one could effectively be removed while still spanning the space of common behaviors during our co-manipulation tasks. The three modi used in classification werequickly,smoothlyandavoiding obstacles. Using a deep convolutional neural network (CNN), we classified three modi with up to 89% accuracy from a validation set. The capability to detect or classify modus during co-manipulation has the potential to greatly improve human-robot performance by helping to define appropriate robot behavior or controller parameters depending on the objective or modus of the team. 

This paper details a reliable control method for highly nonlinear dynamical systems such as soft robots. We call this method model evolutionary gain-based predictive control or MEGa-PC. The method uses an evolutionary algorithm to optimize a set of controller gains via model predictive control. We demonstrate the performance of MEGa-PC in simulation for a single-link inverted pendulum and a threelink inverted pendulum, and on physical hardware for a threejoint continuum soft robot arm with six degrees of freedom. MEGa-PC is compared to prior work that used Nonlinear Evolutionary Model Predictive Control or NEMPC. The new method performs similarly to NEMPC in terms of accumulated cost over the entire trajectory, however, MEGa-PC generalizes better to real-world applications where safety is paramount, the dynamic model is uncertain, the system has significant latency, and where the previous sampling-based method (NEMPC) resulted in significant steady-state error due to model inaccuracy. 

Human teams are able to easily perform collaborative manipulation tasks. However, simultaneously manipulating a large extended object for a robot and human is a difficult task due to the inherent ambiguity in the desired motion. Our approach in this paper is to leverage data from human-human dyad experiments to determine motion intent for a physical human-robot co-manipulation task. We do this by showing that the human-human dyad data exhibits distinct torque triggers for a lateral movement. As an alternative intent estimation method, we also develop a deep neural network based on motion data from human-human trials to predict future trajectories based on past object motion. We then show how force and motion data can be used to determine robot control in a human-robot dyad. Finally, we compare human-human dyad performance to the performance of two controllers that we developed for human-robot co-manipulation. We evaluate these controllers in three-degree-of-freedom planar motion where determining if the task involves rotation or translation is ambiguous. 

Abstract Meeting the United Nations (UN) sustainable development goals efficiently requires designers and engineers to solve multi-objective optimization problems involving trade-offs between social, environmental, and economical impacts. This paper presents an approach for designers and engineers to quantify the social and environmental impacts of a product at a population level and then perform a trade-off analysis between those impacts. In this approach, designers and engineers define the attributes of the product as well as the materials and processes used in the product’s life cycle. Agent-based modeling (ABM) tools that have been developed to model the social impacts of products are combined with life cycle assessment (LCA) tools that have been developed to evaluate the pressures that different processes create on the environment. Designers and engineers then evaluate the trade-offs between impacts by finding non-dominated solutions that minimize environmental impacts while maximizing positive and/or minimizing negative social impacts. Product adoption models generated by ABM allow designers and engineers to approximate population level environmental impacts and avoid Simpson’s paradox, where a reversal in choices is preferred when looking at the population level impacts versus the individual product-level impacts. This analysis of impacts has the potential to help designers and engineers create more impactful products that aid in reaching the UN sustainable development goals. 

Abstract While many tools and methodologies for assessing social impact exist and are used in the social science and global development fields, there is a lack of standard methods for considering the broader social impact of products in the engineering community. Some reasons these methods are not as widely used in the engineering community include designers not being aware of the methods, or methods not being widely applicable. The purpose of this research is to help designers and researchers find relevant design tools and methods for implementing social impact considerations. This is done through the classification of 374 papers in the Engineering for Global Development (EGD) literature along several dimensions including method purpose, industry sector, social impacts considered, sustainable development goals, paper setting, and data inputs required. This article describes how designers and researchers can use this set of classified papers to locate relevant design tools and methods to improve social impact considerations in their work. 

In this paper, we analyze and report on observable trends in human-human dyads performing collaborative manipulation (co-manipulation) tasks with an extended object (object with significant length). We present a detailed analysis relating trends in interaction forces and torques with other metrics and propose that these trends could provide a way of improving communication and efficiency for human-robot dyads. We find that the motion of the co-manipulated object has a measurable oscillatory component. We confirm that haptic feedback alone represents a sufficient communication channel for co-manipulation tasks, however we find that the loss of visual and auditory channels has a significant effect on interaction torque and velocity. The main objective of this paper is to lay the essential groundwork in defining principles of co-manipulation between human dyads. We propose that these principles could enable effective and intuitive human-robot collaborative manipulation in future co-manipulation research. 

Abstract The impact of engineered products is a topic of concern in society. Product impact may fall under the categories of economic, environmental or social impact, with the last category defined as the effect of a product on the day-to-day life of people. Design teams lack sufficient tools to estimate the social impact of products, and the combined impacts of economic, environmental and social impacts for the products they are designing. This paper aims to provide a framework for the estimation of product impact during product design. To estimate product impact, models of both the product and society are required. This framework integrates models of the product, scenario, society and impact into an agent-based model to estimate product impact. Although this paper demonstrates the framework using only social impact, the framework can also be applied to economic or environmental impacts individually or all three concurrently. Agent-based modelling has been used previously for product adoption models, but it has not been extended to estimate product impact. Having tools for impact estimation allows for optimising the product design parameters to increase the potential positive impact and reduce potential negative impact. 

Abstract Social Impact has been widely discussed by the engineering community, but studies show that there is currently little systematic consideration of the social impact of products in both academia and in industry beyond social impacts on health and safety. This paper illustrates how Failure Mode and Effect Analaysis (FMEA) style analysis can be applied to evaluating the social impact of products. The authors propose a new method titled Social Impact Effects Analysis (SIEA), describe how it is performed, and explain the benefits of performing SIEA. 

Abstract This paper discusses the perceived relations between the Social Impact Categories (SIC) and Social, Economic, and Environmental (SEE) Aspects derived from the United Nations’ Sustainable Development Goals (SDGs). Surveys showed high correlations between Health and Safety and Population Change to the majority of SEE Aspects. There were also high correlations between the SICs and economic and environmental factors. Further research will survey perceived relations between all three SEE impact categories.