Analyzing the Effects of Human-Aware Motion Planning on Close-Proximity Human-Robot Collaboration

Objective: The objective of this work was to examine human response to motion-level robot adaptation to determine its effect on team fluency, human satisfaction, and perceived safety and comfort. Background: The evaluation of human response to adaptive robotic assistants has been limited, particularly in the realm of motion-level adaptation. The lack of true human-in-the-loop evaluation has made it impossible to determine whether such adaptation would lead to efficient and satisfying human–robot interaction. Method: We conducted an experiment in which participants worked with a robot to perform a collaborative task. Participants worked with an adaptive robot incorporating human-aware motion planning and with a baseline robot using shortest-path motions. Team fluency was evaluated through a set of quantitative metrics, and human satisfaction and perceived safety and comfort were evaluated through questionnaires. Results: When working with the adaptive robot, participants completed the task 5.57% faster, with 19.9% more concurrent motion, 2.96% less human idle time, 17.3% less robot idle time, and a 15.1% greater separation distance. Questionnaire responses indicated that participants felt safer and more comfortable when working with an adaptive robot and were more satisfied with it as a teammate than with the standard robot. Conclusion: People respond well to motion-level robot adaptation, and significant benefits can be achieved from its use in terms of both human–robot team fluency and human worker satisfaction. Application: Our conclusion supports the development of technologies that could be used to implement human-aware motion planning in collaborative robots and the use of this technique for close-proximity human–robot collaboration

Fluency and embodiment for robots acting with humans

Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2007.Includes bibliographical references (p. 225-234).This thesis is concerned with the notion of fluency in human-robot interaction (HRI), exploring cognitive mechanisms for robotic agents that would enable them to overcome the stop-and-go rigidity present in much of HRI to date. We define fluency as the ethereal yet manifest quality existent when two agents perform together at high level of coordination and adaptation, in particular when they are well-accustomed to the task and to each other. Based on mounting psychological and neurological evidence, we argue that one of the keys to this goal is the adaptation of an embodied approach to robot cognition. We show how central ideas from this psychological school are applicable to robot cognition and present a cognitive architecture making use of perceptual symbols, simulation, and perception-action networks. In addition, we demonstrate that anticipation of perceptual input, and in particular of the actions of others, are an important ingredient of fluent joint action. To that end, we show results from an experiment studying the effects of anticipatory action on fluency and teamwork, and use these results to suggest benchmark metrics for fluency. We also show the relationship between anticipatory action and a simulator approach to perception, through a comparative human subject study of an implemented cognitive architecture on the robot AUR, a robotic desk lamp, designed for this thesis. A result of this work is modeling the effect of practice on human-robot joint action, arguing that mechanisms that govern the passage of cognitive capabilities from a deliberate yet slower system to a faster, sub-intentional, and more rigid one, are crucial to fluent joint action in well-rehearsed ensembles. Theatrical acting theory serves as an inspiration for this work, as we argue that lessons from acting method can be applied to human-robot interaction.by Guy Hoffman.Ph.D

Challenges in Developing a Collaborative Robotic Assistant for Automotive Assembly Lines

Industrial robots are on the verge of emerging from their cages, and entering the final assembly to work along side humans. Towards this we are developing a collaborative robot capable of assisting humans in the final automotive assembly. Several algorithmic as well as design challenges exist when the robots enter the unpredictable, human-centric and time-critical environment of final assembly. In this work, we briefly discuss a few of these challenges along with developed solutions and proposed methodologies, and their implications for improving human-robot collaboration

Human-robot cross-training: Computational formulation, modeling and evaluation of a human team training strategy

We design and evaluate human-robot cross-training, a strategy widely used and validated for effective human team training. Cross-training is an interactive planning method in which a human and a robot iteratively switch roles to learn a shared plan for a collaborative task. We first present a computational formulation of the robot's interrole knowledge and show that it is quantitatively comparable to the human mental model. Based on this encoding, we formulate human-robot cross-training and evaluate it in human subject experiments (n = 36). We compare human-robot cross-training to standard reinforcement learning techniques, and show that cross-training provides statistically significant improvements in quantitative team performance measures. Additionally, significant differences emerge in the perceived robot performance and human trust. These results support the hypothesis that effective and fluent human-robot teaming may be best achieved by modeling effective practices for human teamwork.ABB Inc.U.S. Commercial Regional CenterAlexander S. Onassis Public Benefit Foundatio

Comparative performance of human and mobile robotic assistants in collaborative fetch-and-deliver tasks

There is an emerging desire across manufacturing industries to deploy robots that support people in their manual work, rather than replace human workers. This paper explores one such opportunity, which is to field a mobile robotic assistant that travels between part carts and the automotive final assembly line, delivering tools and materials to the human workers. We compare the performance of a mobile robotic assistant to that of a human assistant to gain a better understanding of the factors that impact its effectiveness. Statistically significant differences emerge based on type of assistant, human or robot. Interaction times and idle times are statistically significantly higher for the robotic assistant than the human assistant. We report additional differences in participant's subjective response regarding team fluency, situational awareness, comfort and safety. Finally, we discuss how results from the experiment inform the design of a more effective assistant.BMW Grou

A Context-based Approach to Robot-human Interaction

AbstractCARIL (Context-Augmented Robotic Interaction Layer) is a human-robot interaction system that leverages cognitive representations of shared context as a basis for a fundamentally new approach to human-robotic interaction. CARIL gives a robot a human-like representation of context and an ability to reason about context in order to adapt its behavior to that of the humans around it. This capability is “action compliance.” A prototype CARIL implementation focuses on a fundamental form of action compliance called non-interference -- “not being underfoot or in a human's way”. Non-interference is key for the safety of human-co-workers, and is also foundational to more complex interactive and teamwork skills. CARIL is tested via simulation in a space-exploration use-case. The live CARIL prototype directs a single simulated robot in a simulated space station where four simulated astronauts are engaging in a variety of tightly-scheduled work activities. The robot is scheduled to perform background tasks away from the astronauts, but must quickly adapt and not be underfoot as astronaut activities diverge from plan and encroach on the robot's space. The robot, driven by CARIL, demonstrates non-interference action compliance in three benchmarks situations, demonstrating the viability of the CARIL technology and concept

Evaluation of Human Robot Collaboration in Masonry Work Using Immersive Virtual Environments

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.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/116277/1/CONVR2015_Final.pd