To design trustworthy robots, we need to understand the impact factors of trust: people's attitudes, experience, and characteristics; the robot's physical design, reliability, and performance; a task's specification and the circumstances under which it is to be performed, e.g. at leisure or under time pressure. As robots are used for a wide variety of tasks and applications, robot designers ought to be provided with evidence and guidance, to inform their decisions to achieve safe, trustworthy and efficient human-robot interactions. In this work, the impact factors of trust in a collaborative manufacturing scenario are studied by conducting an experiment with a real robot and participants where a physical object was assembled and then disassembled. Objective and subjective measures were employed to evaluate the development of trust, under faulty and non-faulty robot conditions, and the effect of previous experience with robots, and personality traits. Our findings highlight differences when compared to other, more social, scenarios with robotic assistants (such as a home care assistant), in that the condition (faulty or not) does not have a significant impact on the human's perception of the robot in terms of human-likeliness, likeability, trustworthiness, and even competence. However, personality and previous experience do have an effect on how the robot is perceived by participants, even though that is relatively small.
Research in robotic co-workers has focused on the design aspects of such systems, including social and interaction aspects in terms of dialogues, gestures and social cues, or more engineering oriented such as scheduling shared collaborative building tasks and the design of protocols (e.g. turn taking), with the intention to obtain a collaboration between the user and the robot that is productive and efficient (e.g. @cite_21 @cite_5 @cite_22 ). Although trust metrics have been used to evaluate robotic systems in manufacturing tasks (e.g. in @cite_36 for collaborative masonry), research is needed to evaluate how trust is developed in these systems and their environments, compared to more social interactions such as in @cite_9 , or challenging remote interactions such as in @cite_10 @cite_0 @cite_15 @cite_2 .
@cite_22 @cite_36 @cite_9 @cite_21 @cite_0 @cite_2 @cite_5 @cite_15 @cite_10
2295887377 2217688965 2594336441 2555647367
The goal of this work is to develop computational models of social intelligence that enable robots to work side by side with humans, solving problems and achieving task goals through dialogue and collaborative manipulation. A defining problem of collaborative behavior in an embodied setting is the manner in which multiple agents make use of shared resources. In a situated dialogue, these resources include physical bottlenecks such as objects or spatial regions, and cognitive bottlenecks such as the speaking floor. For a robot to function as an effective collaborative partner with a human, it must be able to seize and yield such resources appropriately according to social expectations. We describe a general framework that uses timed Petri nets for the modeling and execution of robot speech, gaze, gesture, and manipulation for collaboration. The system dynamically monitors resource requirements and availability to control real-time turn-taking decisions over resources that are shared with humans, reasoning about different resource types independently. We evaluate our approach with an experiment in which our robot Simon performs a collaborative assembly task with 26 different human partners, showing that the multimodal reciprocal approach results in superior task performance, fluency, and balance of control. ABSTRACT: With the advent of collaborative robots, there is a great potential to improve work performance by human-robot collaboration in engineering tasks. Construction is no exception. Many construction tasks are based on the movement of objects (e.g., material), which are viable candidates for human-robot collaboration. However, due to the physically imposing nature of robot operations and the unstructured environments typical in construction, it is crucial to provide a safe and reliable environment for human workers when performing collaborative work with robots. In this paper, we use Immersive Virtual Environments (IVEs) to evaluate a human response to robots (e.g. perceived safety, trust, and team identification) while performing collaborative construction tasks with robots. By adopting IVEs, various types of robots, interactions, and tasks can be easily tested and evaluated to determine the best HRC practice, without the need to build and evaluate a physical prototype. Several experimental scenarios simulating collaborative masonry tasks were implemented using the Unity3D Game Engine and an Oculus Rift 3D Head-Mounted Display (HMD). The results demonstrate that it is important to take into account work environment of human-robot collaboration in order to understand how humans perceive robots when working with them. Shared expectations and mutual understanding are critical facets of teamwork. Achieving these in human-robot collaborative contexts can be especially challenging, as humans and robots are unlikely to share a common language to convey intentions, plans, or justifications. Even in cases where human co-workers can inspect a robot's control code, and particularly when statistical methods are used to encode control policies, there is no guarantee that meaningful insights into a robot's behavior can be derived or that a human will be able to efficiently isolate the behaviors relevant to the interaction. We present a series of algorithms and an accompanying system that enables robots to autonomously synthesize policy descriptions and respond to both general and targeted queries by human collaborators. We demonstrate applicability to a variety of robot controller types including those that utilize conditional logic, tabular reinforcement learning, and deep reinforcement learning, synthesizing informative policy descriptions for collaborators and facilitating fault diagnosis by non-experts. Trained human co-workers can often easily predict each other's intentions based on prior experience. When collaborating with a robot coworker, however, intentions are hard or impossible to infer. This difficulty of mental introspection makes human-robot collaboration challenging and can lead to dangerous misunderstandings. In this paper, we present a novel, object-aware projection technique that allows robots to visualize task information and intentions on physical objects in the environment. The approach uses modern object tracking methods in order to display information at specific spatial locations taking into account the pose and shape of surrounding objects. As a result, a human co-worker can be informed in a timely manner about the safety of the workspace, the site of next robot manipulation tasks, and next subtasks to perform. A preliminary usability study compares the approach to collaboration approaches based on monitors and printed text. The study indicates that, on average, the user effectiveness and satisfaction is higher with the projection based approach.