Incorporating mental simulation for a more effective robotic teammate (original) (raw)
Related papers
2020
Effective coordination is a critical requirement for human teaming, and is increasingly needed in teams of humans and robots. Building on decades of work in the behavioral literature, we have implemented a computational framework for coordination based on Shared Mental Models (SMMs) in which robots use a distributed knowledge base to coordinate activity. We also built a novel system connecting the robotic architecture, DIARC, to the 3D simulation environment, Unity, to serve as an evaluation platform for the framework implementation, and also for more general explorations of teaming with autonomous robots. Using this platform, we ran a user study to evaluate the framework by comparing performance of teams in which the robots used SMMs with those that did not. We found that teams in which the robots used SMMs significantly outperformed those without SMMs. This represents the first empirical demonstration that SMMs can be successfully used by fully autonomous robots interacting in nat...
Integrated Intelligence for Human-Robot Teams
2016
With recent advances in robotics technologies and autonomous systems, the idea of human-robot teams is gaining ever-increasing attention. In this context, our research focuses on developing an intelligent robot that can autonomously perform non-trivial, but specific tasks conveyed through natural language. Toward this goal, a consortium of researchers develop and integrate various types of intelligence into mobile robot platforms, including cognitive abilities to reason about high-level missions, perception to classify regions and detect relevant objects in an environment, and linguistic abilities to associate instructions with the robot’s world model and to communicate with human teammates in a natural way. This paper describes the resulting system with integrated intelligence and reports on the latest assessment.
Designing Intelligent Robots for Human-Robot Teaming in Urban Search & Rescue
2012
The paper describes ongoing integrated research on designing intelligent robots that can assist humans in making a situation assessment during Urban Search & Rescue (USAR) missions. These robots (rover, microcopter) are deployed during the early phases of an emergency response. The aim is to explore those areas of the disaster hotzone which are too dangerous or too difficult for a human to enter at that point. This requires the robots to be "intelligent" in the sense of being capable of various degrees of autonomy in acting and perceiving in the environment. At the same time, their intelligence needs to go beyond mere task-work. Robots and humans are interdependent. Human operators are dependent on these robots to provide information for a situation assessment. And robots are dependent on humans to help them operate (shared control) and perceive (shared assessment) in what are typically highly dynamic, largely unknown environments. Robots and humans need to form a team. The paper describes how various insights from robotics and Artificial Intelligence are combined, to develop new approaches for modeling human robot teaming. These approaches range from new forms of modeling situation awareness (to model distributed acting in dynamic space), human robot interaction (to model communication in teams), flexible planning (to model team coordination and joint action), and cognitive system design (to integrate different forms of functionality in a single system). * This paper describes research done under the EU-FP7 ICT 247870 NIFTi project. For more about NIFTi, please visit http://www.nifti.eu. The paper was written as a team effort.
Agents with shared mental models for enhancing team decision makings
Decision Support Systems, 2006
Proactive information sharing is a challenging issue faced by intelligence agencies in effectively making critical decisions under time pressure in areas related to homeland security. Motivated by psychological studies on human teams, a team-oriented agent architecture, Collaborative Agents for Simulating Teamwork (CAST), was implemented to allow agents in a team to anticipate the information needs of teammates and help them with their information needs proactively and effectively. In this paper, we extend CAST with a decision-making module. Through two sets of experiments in a simulated battlefield, we evaluate the effectiveness of the decision-theoretic proactive communication strategy in improving team performance, and the effectiveness of information fusion as an approach to alleviating the information overload problem faced by distributed decision makers. D
Developing Human-Robot Team Interdependence in a Synthetic Task Environment
Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 2019
In future urban search and rescue teams, robots may be expected to conduct cognitive tasks. As the capabilities of robots change, so too will their interdependence with human teammates. Human factors and cognitive engineering are well-positioned to guide the design of autonomy for effective teaming. Previous work in the urban search and rescue synthetic task environment (USAR-STE) used Minecraft, a customizable gaming platform. In this effort, we advanced the USAR-STE by increasing interdependence in dyadic human-robot teaming through the Coactive Design framework. In this framework, we defined required capacities of victim identification in USAR from literature, and used them as inputs for modeling interdependence, and determined recommendations that would enhance interdependence in the task environment. Although Coactive Design is typically used to design interdependence for robots or jobs, we demonstrated how it can also be used to design an experimental team task environment.
RPD-enabled agents teaming with humans for multi-context decision making
Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems - AAMAS '06, 2006
Team decision making under stress involving multiple contexts is an extremely challenging issue faced by various real world application domains. This research is targeted at coupling cognitive agent technology and human-centered teamwork to address the informational challenges associated with Command and Control (C2) teams in contemporary military operations. Two sets of experiments, each with various settings of context switching frequencies and tasking complexities, were conducted. To ensure that the human subjects were familiar with the C2 context, they were selected from US Army ROTC (Reserve Officer Training Corps) students. Experiments on C2 teams that involve human subjects only were conducted first. We observed the decision making behavior of human subjects and incorporated expert behaviors into R-CAST-an agent architecture built upon a naturalistic decision making model that captures how domain experts make decisions based on experiences and situational similarity recognition. We then conducted another set of experiments with R-CAST agents as teammates and decision aids for human subjects. The results show that RPD-enabled agents can significantly improve the tasking capacity of C2 teams in multi-context decision making under stress. It also suggests that higher demand situations require more competent teammates.
Agents with Shared Mental Models for Enhancing Team Decision-Makings1
Dss, 2005
Proactive information sharing is a challenging issue faced by intelligence agencies in effectively making critical decisions under time pressure in areas related to homeland security. Motivated by psychological studies on human teams , a team-oriented agent architecture --CAST (Collaborative Agents for Simulating Teamwork), was implemented to allow agents in a team to anticipate the information needs of teammates and help them with their information needs proactively, effectively, and timely. In this paper, we extend CAST with a decision-making module . Through two sets of experiments in a simulated battlefield, we evaluate the effectiveness of the decision theoretic proactive communication strategy in improving team performance, and the effectiveness of information fusion as an approach to alleviating the information overload problem faced by distributed decision makers.
Intelligent Agents as Teammates
2011
Team behavior has almost exclusively been studied as involving humans interacting in tasks that require collective action to achieve success. Long a feature in science i ction, it recently has become technologically possible to create artii cial entities that can serve as members of teams, as opposed to simply being automated systems operated by human team members. In the computing and robotics literature, such entities are called sot ware agents. h e term embodied agent is ot en used to describe physical robots in order to dif erentiate them from purely sot ware agents; however, for the purposes of this chapter, we will use agent to refer to both because we intend to argue that some form of embodiment, virtual or physical, is an important element in establishing and maintaining membership in a team. h e term intelligent agent is probably overly generous for describing the cognitive performance possible within the next decade, but implicit in the proposed elevation of status to team...