Robots are increasingly being used in restaurants to assist with service and increase efficiency. Yet, their impact on the daily work of restaurant workers, customers' perceptions, and robots' limitations are poorly understood - and so is the gap between these and official marketing narratives. In this paper we conduct an investigation of the impact of restaurant robots in a set of restaurant chains in Japan, through a combination of in-person ethnography and analysis of online customer reviews and news articles. We show how robots are used in practice, how they structure work, and their impact on workers and customers. In particular, while we find robots to be well integrated and 'invisible', and majorly well received by customers and management, we also find they lead to a customer-perceived loss of human contact, a restructuring of work, an incentive for shortstaffing, deskilling of workers, and several technical challenges that are collectively addressed by workers and customers in work-like tasks. We compare these findings with marketing and management-led narratives, identifying gaps consistent with labor and power-centered critical studies.

Temporal planning is a hard problem that requires good heuristic and memoization strategies to solve efficiently. Merge-and-shrink abstractions have been shown to serve as effective heuristics for classical planning, but it is still unclear how to implement merge-and-shrink in the temporal domain and how effective the method is in this setting. In this paper we propose a method to compute merge-and-shrink abstractions for general temporal planning problems, in a way that is applicable to both partial- and total-order temporal planners. We extend a previous publication to allow the formalism to apply to temporal problems with non-compression safe actions, in particular through the use of a classical planning surrogate of a temporal planning task. The method relies on pre-computing heuristics as formulas of temporal variables that are evaluated at search time, and it allows to use standard merging, shrinking and pruning strategies. Compared to state-of-the-art Relaxed Planning Graph heuristics, we show that the method leads to improvements in coverage, computation time, and number of expanded nodes to solve optimal problems, as well as leading to improvements in unsolvability-proving of problems with deadlines, and the time to compute Minimally Unsolvable Goal Subsets (MUGS). We exhaustively test the method over these problems and various usage settings, showing improvements in coverage of up to 53%, computation time up to 60%, and expanded nodes up to 75%.

Bias has been shown to be a pervasive problem in machine learning, with severe and unanticipated consequences, for example in the form of algorithm performance disparities across social groups. In this paper, we investigate and characterise how similar issues may arise in Reinforcement Learning (RL) for Human-Robot Interaction (HRI), with the intent of averting the same ramifications. Using an assistive robotics simulation as a case study, we show that RL for HRI can perform differently across models with different waist circumferences. We show this behaviour can arise due to representation bias-unbalanced exposure during training-but also due to inherent task properties that may make assistance difficult depending on physical characteristics. The findings underscore the need to address bias in RL for HRI. We conclude with a discussion of potential practical solutions, their consequences and limitations, and avenues for future research.

The integration of robotics in agriculture presents promising solutions to challenges such as labour shortages and increasing global food demand. However, existing visions of agriculture robots often prioritize technological and business needs over workers’. In this paper, we explicitly investigate farm workers’ perspectives on robots, particularly regarding privacy, inclusivity, and safety, three critical dimensions of agricultural HRI. Through a thematic analysis of semi-structured interviews, we: 1) outline how privacy, safety and inclusivity issues manifest within modern picking-farms; 2) reveal worker attitudes and concerns about the adoption of robots; and 3) articulate a set of worker-centered requirements and alternative visions for robotic systems deployed in farm settings. Some of these visions open the door to the development of new systems and HRI research. For example, workers’ visions included robots for enhancing workplace inclusivity and solidarity, training, workplace accountability, reducing workplace accidents and responding to emergencies, as well as privacy-sensitive robots. We conclude with actionable recommendations for designers and policymakers. By centering worker perspectives, this study contributes to ongoing discussions in human-centered robotics, participatory HRI, and the future of work in agriculture.

As public space robots navigate our streets, they are likely to witness various human behavior, including verbal or physical violence. In this paper we investigate whether people believe delivery robots should intervene when they witness violence, and their perceptions of the effectiveness of different conflict de-escalation strategies. We consider multiple types of violence (verbal, physical), sources of violence (civilian, police), and robot designs (wheeled, humanoid), and analyze their relationship with participants’ perceptions. Our analysis is based on two experiments using online questionnaires, investigating the decision to intervene (N=80) and intervention mode (N=100). We show that participants agreed more with human than robot intervention, though they often perceived robots as more effective, and preferred certain strategies, such as filming. Overall, the paper shows the need to investigate whether and when robot intervention in human-human conflict is socially acceptable, to consider police-led violence as a special case of robot de-escalation, and to involve communities that are common victims of violence in the design of public space robots with safety and security capabilities.

Motion planning algorithms are a fundamental component of robotic systems. Unfortunately, as shown by recent literature, their lack of explainability makes it difficult to understand and diagnose planning failures. The feasibility of a motion planning problem depends heavily on the robot model, which can be a major reason for failure. We propose a method that automatically generates explanations of motion planner failure based on robot design. When a planner is not able to find a feasible solution to a problem, we compute a minimum modification to the robot’s design that would enable the robot to complete the task. This modification then serves as an explanation of the type: "the planner could not solve the problem because robot links X are not long enough". We demonstrate how this explanation conveys what the robot is doing, why it fails, and how the failure could be recovered if the robot had a different design. We evaluate our method through a user study, which shows our explanations help users better understand robot intent, cause of failure and recovery, compared to other methods. Moreover, users were more satisfied with our method’s explanations, and reported that they understood the capabilities of the robot better after exposure to the explanations.

Grasp Quality Networks are important components of grasping-capable autonomous robots, as they allow them to evaluate grasp candidates and select the one with highest chance of success. The widespread use of pick-and-place robots and Grasp Quality Networks raises the question of whether such systems are vulnerable to adversarial attacks, as that could lead to large economic damage. In this paper we propose two kinds of attacks on Grasp Quality Networks, one assuming physical access to the workspace (to place or attach a new object) and another assuming digital access to the camera software (to inject a pixel-intensity change on a single pixel). We then use evolutionary optimization to obtain attacks that simultaneously minimize the noticeability of the attacks and the chance that selected grasps are successful. Our experiments show that both kinds of attack lead to drastic drops in algorithm performance, thus making them important attacks to consider in the cybersecurity of grasping robots. Source code can be found at https://github.com/Naif-W-Alharthi/Physical-and-Digital-Attacks-on-Grasping-Networks

Motion planning algorithms are important components of autonomous robots, which are difficult to understand and debug when they fail to find a solution to a problem. In this paper we propose a solution to the failure-explanation problem, which are automatically-generated environment-based explanations. These explanations reveal the objects in the environment that are responsible for the failure, and how their location in the world should change so as to make the planning problem feasible.Concretely, we propose two methods—one based on evolutionary optimization and another on joint trajectory-and-environment continuous-optimization. We show that the evolutionary method is well-suited to explain sampling-based motion planners, or even optimization-based motion planners in situations where computation speed is not a concern (e.g. post-hoc debugging). However, the optimization-based method is 4000 times faster and thus more attractive for interactive applications, even though at the cost of a slightly lower success rate. We demonstrate the capabilities of the methods through concrete examples and quantitative evaluation.

As the adoption of robots across society increases, so does the importance of considering cybersecurity issues such as vulnerability to adversarial attacks. In this paper we investigate the vulnerability of an important component of autonomous robots to adversarial attacks—robot motion planning algorithms. We particularly focus on attacks on the physical environment, and propose the first such attacks to motion planners: “planner failure” and “blindspot” attacks. Planner failure attacks make changes to the physical environment so as to make planners fail to find a solution. Blindspot attacks exploit occlusions and sensor field-of-view to make planners return a trajectory which is thought to be collision-free, but is actually in collision with unperceived parts of the environment. Our experimental results show that successful attacks need only to make subtle changes to the real world, in order to obtain a drastic increase in failure rates and collision rates—leading the planner to fail 95% of the time and collide 90% of the time in problems generated with an existing planner benchmark tool. We also analyze the transferability of attacks to different planners, and discuss underlying assumptions and future research directions. Overall, the paper shows that physical adversarial attacks on motion planning algorithms pose a serious threat to robotics, which should be taken into account in future research and development.

Despite the growing interest in the use of drone fleets for delivery of food and parcels, the negative impact of such technology is still poorly understood. In this paper we investigate the impact of such fleets in terms of noise pollution and environmental justice. We use simulation with real population data to analyze the spatial distribution of noise, and find that: 1) noise increases rapidly with fleet size; and 2) drone fleets can produce noise hotspots that extend far beyond warehouses or charging stations, at levels that lead to annoyance and interference of human activities. This, we will show, leads to concerns of fairness of noise distribution. We then propose an algorithm that successfully balances the spatial distribution of noise across the city, and discuss the limitations of such purely technical approaches. We complement the work with a discussion of environmental justice, showing how careless UAV fleet development and regulation can lead to reinforcing well-being deficiencies of poor and marginalized communities.

As robots become more ubiquitous across human spaces, it is becoming increasingly relevant for researchers to ask the question, “how can we ensure that we are designing robots to be sufficiently equipped to treat people fairly?”. This workshop brings together researchers across the fields of Human-Robot Interaction (HRI), fairness in machine learning, design, and transparency in AI to shed light on the relevant methodological challenges surrounding issues of fairness and transparency in HRI. In our workshop, we will attempt to identify synergies between these various fields. In particular, we will focus on how HRI can leverage these existing rich body of work to guide the formalization of fairness metrics and methodologies. Another goal of the workshop is to foster a community of interdisciplinary researchers to encourage collaboration. The complexity in defining fairness lies in its context sensitive nature, as such we look to the influx of definitions from the field of fairness in artificial intelligence, design, and organizational psychology to derive a set of definitions that could serve as guidelines for researchers in HRI.

Drowsiness and fatigue are important factors in driving safety and work performance. This has motivated academic research into detecting drowsiness, and sparked interest in the deployment of related products in the insurance and work-productivity sectors. In this paper we elaborate on the potential dangers of using such algorithms. We first report on an audit of performance bias across subject gender and ethnicity, identifying which groups would be disparately harmed by the deployment of a state-of-the-art drowsiness detection algorithm. We discuss some of the sources of the bias, such as the lack of robustness of facial analysis algorithms to face occlusions, facial hair, or skin tone. We then identify potential downstream harms of this performance bias, as well as potential misuses of drowsiness detection technology---focusing on driving safety and experience, insurance cream-skimming and coverage-avoidance, worker surveillance, and job precarity.

Recent research in AI ethics has put forth explainability as an essential principle for AI algorithms. However, it is still unclear how this is to be implemented in practice for specific classes of algorithms—such as motion planners. In this paper we unpack the concept of explanation in the context of motion planning, introducing a new taxonomy of kinds and purposes of explanations in this context. We focus not only on explanations of failure (previously addressed in motion planning literature) but also on contrastive explanations—which explain why a trajectory A was returned by a planner, instead of a different trajectory B expected by the user. We develop two explainable motion planners, one based on optimization, the other on sampling, which are capable of answering failure and constrastive questions. We use simulation experiments and a user study to motivate a technical and social research agenda.

In this work we propose a holistic framework for autonomous aerial inspection tasks, using semantically-aware, yet, computationally efficient planning and mapping algorithms. The system leverages state-of-the-art receding horizon exploration techniques for next-best-view (NBV) planning with geometric and semantic segmentation information provided by state-of-the-art deep convolutional neural networks (DCNNs), with the goal of enriching environment representations. The contributions of this article are threefold, first we propose an efficient sensor observation model, and a reward function that encodes the expected information gains from the observations taken from specific view points. Second, we extend the reward function to incorporate not only geometric but also semantic probabilistic information, provided by a DCNN for semantic segmentation that operates in real-time. The incorporation of semantic information in the environment representation allows biasing exploration towards specific objects, while ignoring task-irrelevant ones during planning. Finally, we employ our approaches in an autonomous drone shipyard inspection task. A set of simulations in realistic scenarios demonstrate the efficacy and efficiency of the proposed framework when compared with the state-of-the-art.

Visions have an important role in guiding and legitimizing technical research, as well as contributing to expectations of the general public towards technologies. In this paper we analyze technical robotics papers published between 1998 and 2019 to identify themes, trends and issues with the visions and values promoted by robotics research. In particular, we identify the themes of robotics visions and implicitly normative visions; and we quantify the relative presence of a variety of values and applications within technical papers. We conclude with a discussion of the language of robotics visions, marginalized visions and values, and possible paths forward for the robotics community to better align practice with societal interest. We also discuss implications and future work suggestions for Responsible Robotics and HRI research.

Motion planning is a hard problem that can often overwhelm both users and designers: due to the difficulty in understanding the optimality of a solution, or reasons for a planner to fail to find any solution. Inspired by recent work in machine learning and task planning, in this paper we are guided by a vision of developing motion planners that can provide reasons for their output—thus potentially contributing to better user interfaces, debugging tools, and algorithm trustworthiness. Towards this end, we propose a preliminary taxonomy and a set of important considerations for the design of explainable motion planners, based on the analysis of a comprehensive user study of motion planning experts. We identify the kinds of things that need to be explained by motion planners ("explanation objects"), types of explanation, and several procedures required to arrive at explanations. We also elaborate on a set of qualifications and design considerations that should be taken into account when designing explainable methods. These insights contribute to bringing the vision of explainable motion planners closer to reality, and can serve as a resource for researchers and developers interested in designing such technology.

Different legged robot locomotion controllers offer different advantages; from speed of motion to energy, computational demand, safety and others. In this paper we propose a method for planning locomotion with multiple controllers and sub-planners, explicitly considering the multi-objective nature of the legged locomotion planning problem. The planner first obtains body paths extended with a choice of controller or sub-planner, and then fills the gaps by sub-planning. The method leads to paths with a mix of static and dynamic walking which only plan footsteps where necessary. We show that our approach is faster than pure footstep planning methods both in computation (2x) and mission time (1.4x), and safer than pure dynamic-walking methods. In addition, we propose two methods for aggregating the multiple objectives in search-based planning and reach desirable trade-offs without weight tuning. We show that they reach desirable Pareto-optimal solutions up to 8x faster than fairly-tuned traditional weighted-sum methods. Our conclusions are drawn from a combination of planning, physics simulation, and real robot experiments.

Stereo confidence measures are important functions for global reconstruction methods and some applications of stereo. In this article we evaluate and compare several models of confidence which are defined at the whole disparity range. We propose a new stereo confidence measure to which we call the Histogram Sensor Model (HSM), and show how it is one of the best performing functions overall. We also introduce, for parametric models, a systematic method for estimating their parameters which is shown to lead to better performance when compared to parameters as computed in previous literature. All models were evaluated when applied to two different cost functions at different window sizes and model parameters. Contrary to previous stereo confidence measure benchmark literature, we evaluate the models with criteria important not only to winner-take-all stereo, but also to global applications. To this end, we evaluate the models on a real-world application using a recent formulation of 3D reconstruction through occupancy grids which integrates stereo confidence at all disparities. We obtain and discuss our results on both indoors' and outdoors' publicly available datasets.

Energy efficiency and robustness of locomotion to different terrain conditions are important problems for humanoid robots deployed in the real world. In this paper, we propose a footstep-planning algorithm for humanoids that is applicable to flat, slanted, and slippery terrain, which uses simple principles and representations gathered from human gait literature. The planner optimizes a center-of-mass (COM) mechanical work model subject to motion feasibility and ground friction constraints using a hybrid A* search and optimization approach. Footstep placements and orientations are discrete states searched with an A* algorithm, while other relevant parameters are computed through continuous optimization on state transitions. These parameters are also inspired by human gait literature and include footstep timing (double-support and swing time) and parameterized COM motion using knee flexion angle keypoints. The planner relies on work, the required coefficient of friction (RCOF), and feasibility models that we estimate in a physics simulation. We show through simulation experiments that the proposed planner leads to both low electrical energy consumption and human-like motion on a variety of scenarios. Using the planner, the robot automatically opts between avoiding or (slowly) traversing slippery patches depending on their size and friction, and it chooses energy-optimal stairs and climbing angles in slopes. The obtained motion is also consistent with observations found in human gait literature, such as human-like changes in RCOF, step length and double-support time on slippery terrain, and human-like curved walking on steep slopes. Finally, we compare COM work minimization with other choices of the objective function.

We present a grid-based 3D reconstruction method which integrates all costs given by stereo vision into what we call a Cost-Curve Occupancy Grid (CCOG). Occupancy probabilities of grid cells are estimated in a Bayesian formulation, from the likelihood of stereo cost measurements taken at all distance hypotheses. This is accomplished with only a small set of probabilistic assumptions which we discuss in the paper. We quantitatively characterize the method's performance under different conditions of both image noise and number of used stereo pairs, compared also to traditional algorithms. We complement the study by giving insights on design choices of CCOGs such as likelihood model, window size of the cost function and use of a hole filling method. Experiments were made on a real-world outdoors dataset with ground-truth data.

Humanoid robots have this formidable advantage to possess a body quite similar in shape to humans. This body grants them, obviously, locomotion but also a medium to express emotions without even needing a face. In this paper we propose to study the effects of emotional gaits from our biped humanoid robot on the subjects' perception of the robot (recognition rate of the emotions, reaction time, anthropomorphism, safety, likeness, etc.). We made the robot walk towards the subjects with different emotional gait patterns. We assessed positive (Happy) and negative (Sad) emotional gait patterns on 26 subjects divided in two groups (whether they were familiar with robots or not). We found that even though the recognition of the different types of patterns does not differ between groups, the reaction time does. We found that emotional gait patterns affect the perception of the robot. The implications of the current results for Human Robot Interaction (HRI) are discussed.

Extensive literature has been written on occupancy grid mapping for different sensors. When stereo vision is applied to the occupancy grid framework it is common, however, to use sensor models that were originally conceived for other sensors such as sonar. Although sonar provides a distance to the nearest obstacle for several directions, stereo has confidence measures available for each distance along each direction. The common approach is to take the highest-confidence distance as the correct one, but such an approach disregards mismatch errors inherent to stereo. In this work, stereo confidence measures of the whole sensed space are explicitly integrated into 3D grids using a new occupancy grid formulation. Confidence measures themselves are used to model uncertainty and their parameters are computed automatically in a maximum likelihood approach. The proposed methodology was evaluated in both simulation and a real-world outdoor dataset which is publicly available. Mapping performance of our approach was compared with a traditional approach and shown to achieve less errors in the reconstruction.

Humanoid robots are complex sensorimotor systems where the existence of internal models are of utmost importance both for control purposes and for predicting the changes in the world arising from the system's own actions. This so-called expected perception relies on the existence of accurate internal models of the robot's sensorimotor chains.