On-ramp merging is a challenging task for autonomous vehicles (AVs), especially in mixed traffic where AVs coexist with human-driven vehicles (HDVs). In this paper, we formulate the mixed-traffic highway on-ramp merging problem as a multi-agent reinforcement learning (MARL) problem, where the AVs (on both merge lane and through lane) collaboratively learn a policy to adapt to HDVs to maximize the traffic throughput. We develop an efficient and scalable MARL framework that can be used in dynamic traffic where the communication topology could be time-varying. Parameter sharing and local rewards are exploited to foster inter-agent cooperation while achieving great scalability. An action masking scheme is employed to improve learning efficiency by filtering out invalid/unsafe actions at each step. In addition, a novel priority-based safety supervisor is developed to significantly reduce collision rate and greatly expedite the training process. A gym-like simulation environment is developed and open-sourced with three different levels of traffic densities. We exploit curriculum learning to efficiently learn harder tasks from trained models under simpler settings. Comprehensive experimental results show the proposed MARL framework consistently outperforms several state-of-the-art benchmarks.

The drive for increasing adoption of HRI technolo-gies is evident through research and development of manufac-turing, social, medical, and service robot solutions. However, novel methods and metrics are required to overcome the barrier between fundamental HRI research and its adoption in real-world environments. Hence, the fourth installment of the annual workshop, 'Test Methods and Metrics for Effective HRI,’ seeks to identify novel and emerging test methods and metrics for the holistic assessment and assurance of HRI performance. Specifically, the focus is on identifying innovative methods for the evaluation of HRI performance and to advance the growth of the HRI community based on the principles of collaboration, data sharing, and repeatability. The goal of this workshop is to break the boundaries between the development and adoption of HRI technologies through the promotion of robust experimental design, test methods, and metrics for assessing interaction and interface designs. This workshop will have participants from var-ious sectors in the HRI research community including academia, industry, and government in order to accomplish its aims.

We propose a computational model for enabling robots to automatically make decisions under risk in a human-like way. Human decision-making (DM) under risk is influenced by psychological effects, including regret effects, probability weighting effects, and range effects. On the basis of regret theory, we devise a mathematical DM model to encompass these psychological effects. To further quantify the model, we cast the model into a state-space representation and design a fuzzy logic controller to obtain desired preference data from individual decision makers. The data from each individual were used to train a personalized instance of the model. The resulting model is quantitative. It sheds light on the psychological mechanism of risk-attitudes in human DM. The prediction accuracy of the model was statistically tested. On average, the accuracy of our model is 74.7%, which is significantly close to the average accuracy of the subjects when they repeated their own previously made decisions (73.3%). Furthermore, when only the decisions that were repeated consistently by the subjects are examined, the average accuracy of our model is 86.6%.Note to Practitioners—Task allocation in human–robot collaboration (HRC) systems is challenging because robots and humans are heterogeneous in advantages and drawbacks. Robots, despite recent development, are not comparable to humans in reliability. However, human working hours are generally more expensive. When the HRC systems have high robot-to-human ratios and are required to work for long periods, the amount of decision-making (DM) problems of assigning a task to a robot or a human is enormous. Hence, the DM process needs to be automated. It was found that the performance of a team is improved when all the team members share the same mental models. Therefore, in human-centered automation, the characteristics of human DM should be shared with robots. For this purpose, in this article we propose a human-like DM model. We first discuss the psychological effects that influence human DM under risk. We transfer these effects to the building components of the mathematical human-like DM model. However, the details of the components are undetermined. We then design the algorithms to measure these components quantitatively. We test the prediction accuracy of the automated DM model and show it is significantly more human-like than the traditional DM method. The proposed algorithms can be used for designing human-like DM models in applications that involve allocating tasks between humans and robots, such as collaborative search, collaborative assembly, and so on.

This special issue of the Transactions on Human-Robot Interaction highlights, documents, and explores the metrics, test methods, and artifacts used in human-robot interaction (HRI) research. This collection of articles brings to attention the commonalities between the application of measurement science for the assessment and assurance of human-centric robotics in a variety of application domains, including industry, education, and defense. This special issue draws specific attention to the use and impact of metrology toward the advancement of HRI technologies and algorithms, and it promotes the application of measurement science toward the benchmarking and replication of HRI research. Special attention is given to the use cases, data sets, test methodologies, measurement techniques, metrics, and statistical analyses used to evaluate system performance.

Lane-change decision models with enhanced human-likeness are increasingly important as they are integral in traffic simulations for training autonomous driving algorithms. This work proposes a computational model of driver lane-change decision-making by integrating relevant human features in perception, reasoning, emotion, and decision (PRED). The PRED model describes how drivers make lane-change decisions under collision risk. Here risk is represented by probabilities and outcomes of the possible consequences. The PRED model formulates drivers’ risk perception and risk propensity in its modules: the perception module is modeled with Bayesian inference; the reasoning module is modeled with Newtonian simulation; the emotion and decision module is modeled with the extended regret theory. The PRED model was fitted and tested with an empirical dataset from a naturalistic driving database. The prediction performance of the PRED model ranks higher than the selected benchmarks and is close to the state-of-the-art machine learning models. Moreover, the explicit modeling of risk propensity sheds light on an important question in transportation: what causes human drivers’ risk-taking behaviors? The results support the rationale that downplaying crash consequences is the main contributor.

In this paper, we propose a human-robot interaction (HRI) framework to reduce the vulnerability of an unmanned ground vehicle (UGV) platoon under cyber attacks. An observer-based autonomous resilient control strategy is first designed to mitigate the effects of vehicle-to-vehicle (V2V) cyber attacks. Rigorous proof of the safety conditions of the platoon under V2V cyber attacks is derived. Next, to facilitate human supervision in emergencies and uncertainties, a decision-making aid system is developed, which consists of an anomaly reporting system (ARS), a trust-based information management system (TIMS), and a graphical user interface (GUI). ARS is designed to detect the abnormality of the neighbor UGVs based on the model-based residual generation and analysis. TIMS is developed to rule out information with low trustworthiness, which is fulfilled by a Bayesian-based information pre-processor followed by an enhanced median filter with trustworthiness regulation. It is rigorously proved that TIMS can recover the tempered information under the mild cyber attack scenario autonomously. To deal with unexpected severe vehicle-to-cloud (V2C) cyber attacks, human observations can also be leveraged to bolster the resilience of the TIMS. Representative simulation and humans-in-the-loop experiment (fulfilled by the designed GUI) demonstrate that the proposed framework can effectively guide human operators when working with UGV platoons under cyber attacks. On average, compared to a multi-screen surveillance benchmark approach, the proposed framework can achieve 78.4% decrements of platoon vulnerability and 39.0% decrements in human workload.

Information-aware connected and automated vehicles (CAVs) have drawn great attention in recent years due to their potentially significant positive impacts on roadway safety and operational efficiency. In this paper, we conduct an in-depth review of three basic and key interrelated aspects of a CAV: sensing and communication technologies; human factors; and information-aware controller design. First, the different vehicular sensing and communication technologies and their protocol stacks, to provide reliable information to the information-aware CAV controller, are thoroughly discussed. Diverse human factors, such as user comfort, preferences, and reliability, to design the CAV systems for mass adaptation are also discussed. Then, the different layers of a CAV controller (route planning, driving mode execution, and driving model selection) considering human factors and information through connectivity are reviewed. In addition, the critical challenges for the sensing and communication technologies, human factors, and information-aware controller are identified to support the design of a safe and efficient CAV system while considering user acceptance and comfort. Finally, the promising future research directions of these three aspects are discussed to overcome existing challenges to realize a safe and operationally efficient CAV.

Verified and validated test methods, being necessary to measure the performance of complex systems, are important tools for driving innovation, benchmarking and improving performance, and establishing trust in collaborative human-robot teams. This full-day workshop aims to explore the metrology necessary for repeatably and independently assessing the collaborative performance of robotic systems in real-world human-robot interaction (HRI) scenarios. This workshop aims to bridge the gaps between the theory and applications of HRI in industry, accelerating the adoption of cutting edge technologies as the industry state-of-practice. The interest in collaborative HRI is evident in the current market as well as standards efforts toward manufacturing, social, medical, and service robot solutions. Though these domains have been considered separate for many years, recent technological and scientific advancements show that, while their applications may differ, the underlying principles of HRI performance impact each identically. As such, this workshop seeks to identify test methods and metrics for the holistic assessment and assurance of collaborative HRI performance. The focus is on identifying the key performance indicators of these seemingly disparate sectors, and additionally to establish a community based on the principles of transparency, repeatability, & establishing trust in the assessment of collaborative HRI. The goal is to aid in the advancement of HRI technologies through the development of experimental scenarios, protocols, test methods, & metrics for the verification and validation of interaction solutions and interface designs.

Recent emergence of safe, lightweight, and flexible robots has opened a new realm for human-robot collaboration in manufacturing. Utilizing such robots with the new human-robot interaction (HRI) functionality to interact closely and effectively with a human co-worker, we propose a novel framework for integrating HRI factors (both physical and social interactions) into the robot motion controller for human-robot collaborative assembly tasks in a manufacturing hybrid cell. To meet human physical demands in such assembly tasks, an optimal control problem is formulated for physical HRI (pHRI)-based robot motion control to keep pace with human motion progress. We further augment social HRI (sHRI) into the framework by considering a computational model of the human worker's trust in his/her robot partner as well as robot facial expressions. The human worker's trust in robot is computed and used as a metric for path selection as well as a constraint in the optimal control problem. Robot facial expression is displayed for providing additional visual feedbacks to the human worker. We evaluate the proposed framework by designing a robotic experimental testbed and conducting a comprehensive study with a human-in-the-loop. Results of this paper show that compared to the manual adjustments of robot velocity, an autonomous controller based on pHRI, pHRI and sHRI with trust, or pHRI and sHRI with trust, and emotion result in 34%, 39%, and 44% decrease in human workload and 21%, 32%, and 60% increase in robot's usability, respectively. Compared to the manual framework, human trust in robot increases by 38% and 42%, respectively, in the latter two autonomous frameworks. Moreover, the overall efficiency in terms of assembly time remains the same.

Symbolic motion planning for robots is the process of specifying and planning robot tasks in a discrete space, then carrying them out in a continuous space in a manner that preserves the discrete-level task specifications. Despite progress in symbolic motion planning, many challenges remain, including addressing scalability for multi-robot systems and improving solutions by incorporating human intelligence. In this article, distributed symbolic motion planning for multi-robot systems is developed to address scalability. More specifically, compositional reasoning approaches are developed to decompose the global planning problem, and atomic propositions for observation, communication, and control are proposed to address inter-robot collision avoidance. To improve solution quality and adaptability, a hypothetical dynamic, quantitative, and probabilistic human-to-robot trust model is developed to aid this decomposition. Furthermore, a trust-based real-time switching framework is proposed to switch between autonomous and manual motion planning for tradeoffs between task safety and efficiency. Deadlock- and livelock-free algorithms are designed to guarantee reachability of goals with a human-in-the-loop. A set of nontrivial multi-robot simulations with direct human inputs and trust evaluation is provided, demonstrating the successful implementation of the trust-based multi-robot symbolic motion planning methods.

Multi-robot teleoperation systems benefit from human's capabilities in adaptation to new environments and decision-making in the presence of uncertainty and complexity. In these applications, usually a human operator determines the general behaviour of the multi-robot team via selecting and controlling a leader robot while the follower robots coordinate with the team autonomously. Trust is a major factor in human's allocation of autonomy among the team of robots. A trust-based online leader selection strategy is proposed in this work for multi-robot bilateral haptic teleoperation with applications in collective position tracking and synchronization. Human-to-robot trust is computed and utilized as a dynamic criterion to select the leader. Also, robot-to-human trust is used to dynamically scale the haptic force feedback cues so that the operator receives smaller force feedback when his/her performance in leading the team is higher. This reduces the physical workload of the operator. Due to the trust-based leader switching and force feedback scaling, the stability of the closed-loop system may be lost. We guarantee this stability via passivity-based techniques. Finally, our experimental results indicate that the trust-based leader selection strategy can reduce task completion time by 35.25% and formation error by 41.64% compared to a non-leader switching strategy.

Most sun-synchronous satellites acquire images when the illumination source is located in the southeast direction, which is contrary to human visual habits. Therefore, when observing such images, the false topographic perception phenomenon (FTPP) occurs, particularly in large-relief regions. FTPP should be corrected because it may induce misunderstandings of terrain features, thus affecting related decision making. Unlike most existing algorithms, an automatic algorithm focusing on altering the illumination direction is proposed on the basis of topographic correction. The purpose of the algorithm is to alter the illumination direction from SE to northwest. The original and FTPP-corrected images can be quantitatively connected by establishing the relationship between these two images after topographic correction by using the C model such that the formula for calculating the DN (Digital Number) value of the FTPP-corrected image can be derived. An online survey of 323 volunteers using two sets of test data demonstrates that the success rate of the topographic-correction-based approach is as high as 85%. In comparative trials, the proposed approach displays a relatively strong ability to preserve the spatial details and the spectral characteristics of the original image.

With the emergence of vehicle-to-vehicle communication technology, cooperative adaptive cruise control (CACC) cars can be expected in the near future. In this paper, novel criteria for string stability are proposed for mixed traffic platoons that consist of both automated and manual driving cars. A mixed traffic string stability definition is proposed to guarantee the boundedness of the motion states fluctuation upstream as well as the safety of the entire platoon. With proper arrangements of the platoon sequence as well as mild restrictions on the leading car's velocity overshoot, the proposed mixed traffic string stability can be realized via some suitable controller design for the automated cars. The benchmark controller with feedback and feedforward path is first used to verify the above results with rigorous proof. Furthermore, a human-centered CACC system is designed to improve both physical and psychological comfort of a driver under the guarantee of mixed traffic string stability. The action-point car-following model is adapted to quantify the driver's psychological comfort in combination with the quantification of physical comfort based on jerk. A model predictive control-like blending ratio controller is developed to obtain the optimal time headway in the feedback controller for tradeoffs among driver comfort, fuel efficiency, and traffic throughput under string stability constraints. Finally, a seven-car CACC mixed traffic platoon scenario is simulated. Compared with the benchmark CACC controller design, the human-centered CACC design is shown to largely improve the driving comfort.

The goal of this paper is to improve the performance of joint human-robot systems while balancing human experience through computational trust analysis in telerobotics applications. A mixed-initiative approach is enabled by scaling the manual and autonomous controls using a function of computational human-to-robot trust. A dynamic haptic force feedback scaling strategy is performed via a function of computational robot-to-human trust to adjust the assistive force and, hence, physical workload of the operator. Passivity-based methods are used to guarantee the stability of the overall framework. Moreover, guidelines are provided to adjust the transparency of force feedback and velocity signals while guaranteeing passivity. An experimental study with 30 human subjects demonstrates a 12.8% increase in task performance and a 10.7% decrease in operator workload when the proposed strategy is used as compared to a manual autonomy allocation approach. The results also indicate that the proposed method yields higher operator satisfaction compared to manual and optimal autonomy allocation methods and is 10.1% more trusted by the operators than the optimal allocation method.

Symbolic motion planning for robots is the process of specifying and planning robot tasks in a discrete space, then carrying them out in a continuous space in a manner that preserves the discrete-level task specifications. Despite progress in symbolic motion planning, many challenges remain, including addressing scalability for multi-robot systems and improving solutions by incorporating human intelligence in an adaptive fashion. In this paper, we use local communication, observation, control protocols, and compositional reasoning approaches to decompose the planning problem to address scalability. To address solution quality and adaptability, we use a dynamic and computational trust model to aid this decomposition and to implement real-time switching between automated and human motion planning. A simulation is provided demonstrating the successful implementation of these methods.

Cooperative adaptive cruise control (CACC) systems have the potential to increase traffic throughput by allowing smaller headway between vehicles and moving vehicles safely in a platoon at a harmonized speed. CACC systems have been attracting significant attention from both academia and industry since connectivity between vehicles will become mandatory for new vehicles in the USA in the near future. In this paper, we review three basic and important aspects of CACC systems: communications, driver characteristics, and controls to identify the most challenging issues for their real-world deployment. Different routing protocols that support the data communication requirements between vehicles in the CACC platoon are reviewed. Promising and suitable protocols are identified. Driver characteristics related issues, such as how to keep drivers engaged in driving tasks during CACC operations, are discussed. To achieve mass acceptance, the control design needs to depict real-world traffic variability such as communication effects, driver behavior, and traffic composition. Thus, this paper also discusses the issues that existing CACC control modules face when considering close to ideal driving conditions.

Soft limb locomotion is a relatively new and challenging research field. However, soft limbs can not yet transition to practical application due to difficulties associated with control methods. Motivated by this research problem, in this paper, we investigate the performance of energy based control of underactuated soft limbed systems. We augment the previously reported energy shaping function for rigid bipeds with a new set of functions for the novel class of underactuated compass gait soft bipeds. We evaluate the controller performance and identify desired features and characteristics for better speed performance of such a biped. The proposed energy shaping functions are compared through controlled Lagrangian (CL) method for Euler-Lagrangian (EL) models and interconnection and damping assignment passivity-based control (IDA-PBC) methods for port-controlled Hamiltonian (PCH) models. Results for system stability, speed performance, and input torque profiles are compared. The IDA-PBC controllers are observed to produce better input torque and performance over the CL methods.The findings assist in extending and developing novel controllers to implement on soft limbed robots for practical control applications of soft multi-continuum limbed robots.

In distributed multi-agent networks with local interactions, effective resource allocation to attain multiple tasks is a key system performance. In this paper, a dynamic evolutionary task assignment approach for such networks is introduced. According to this novel approach, every agent will allocate resources according to its individually-assigned task prioritization as well as local interactions with neighboring agents. The effects of different parameters used in the approach are studied and tested by simulations. Relevant applications to robot networks are discussed. A comprehensive simulation-based study is provided to demonstrate the performance of the proposed approach.

In this paper, shadow detection and compensation are treated as image enhancement tasks. The principal components analysis (PCA) and luminance based multi-scale Retinex (LMSR) algorithm are explored to detect and compensate shadow in high resolution satellite image. PCA provides orthogonally channels, thus allow the color to remain stable despite the modification of luminance. Firstly, the PCA transform is used to obtain the luminance channel, which enables us to detect shadow regions using histogram threshold technique. After detection, the LMSR technique is used to enhance the image only in luminance channel to compensate for shadows. Then the enhanced image is obtained by inverse transform of PCA. The final shadow compensation image is obtained by comparison of the original image, the enhanced image and the shadow detection image. Experiment results show the effectiveness of the proposed method.