This paper introduces the concept of team design patterns and proposes an intuitive graphical language for describing the design choices that influence how intelligent systems (e.g. artificial intelligence, robotics, etc.) collaborate with humans. We build on the notion of design patterns and characterize important dimensions within human-agent teamwork. These dimensions are represented using a simple, intuitive graphical iconic language. The simplicity of the language allows easier expression, sharing and comparison of human-agent teaming concepts. Having such a language has the potential to improve the collaborative interaction among a variety of stakeholders such as end users, project managers, policy makers and programmers that may not be human-agent teamwork experts themselves. We also introduce an ontology and specification formalization that will allow translation of the simple iconic language into more precise definitions. By expressing the essential elements of teaming patterns in precisely defined abstract team design patterns, we provide a foundation that will enable working towards a library of reusable, proven solutions for human-agent teamwork.

NASA and the international aviation community are investing in the development of a commercial transportation infrastructure that includes the increased use of rotorcraft, specifically helicopters and civil tilt rotors. However, there is significant concern over the impact of noise on the communities surrounding the transportation facilities. One way to address the rotorcraft noise problem is by exploiting powerful search techniques coming from artificial intelligence to design low-noise flight profiles that can be then validated though field tests. This article investigates the use of discrete heuristic search methods to design low-noise approach trajectories for rotorcraft. Our work builds on a long research tradition in trajectory optimization using either numerical methods or discrete search. Novel features of our approach include the use of a discrete search space with a resolution that can be varied, and the coupling of search with a robust simulator to evaluate candidates. The article includes a systematic comparison of different search techniques; in particular, in the experiments, we are able to do a trade study that compares complete search algorithms such as A * with faster but approximate methods such as local search.

In this paper, we present a novel interface for teleoperating ground vehicles. Obstacle avoidance with ground vehicles demands a high level of operator attention, typically distracting from the primary mission. The Ambient Obstacle Avoidance (AOA) was designed to allow operators to effectively perform a primary task, such as search, while still effectively avoiding obstacles. The AOA wraps around a standard video interface and provides range information without requiring a separate screen. AOA combines and reduces different data streams into proportionately scaled symbology that directly shows important relationships between vehicle width, vehicle orientation and obstacles in the environment. The use of the AOA interface was tested in both simulation and on physical robots. Results from both tests show an improvement in obstacle avoidance during navigation with the AOA. In addition, results from the simulation test indicate that operators using AOA were able to leverage ambient vision such that the primary visual task was not impeded.

Coactive Design is a new approach to address the increasingly sophisticated roles that people and robots play as the use of robots expands into new, complex domains. The approach is motivated by the desire for robots to perform less like teleoperated tools or independent automatons and more like interdependent teammates. In this article, we describe what it means to be interdependent, why this is important, and the design implications that follow from this perspective. We argue for a human-robot system model that supports interdependence through careful attention to requirements for observability, predictability, and directability. We present a Coactive Design method and show how it can be a useful approach for developers trying to understand how to translate high-level teamwork concepts into reusable control algorithms, interface elements, and behaviors that enable robots to fulfill their envisioned role as teammates. As an example of the coactive design approach, we present our results from the DARPA Virtual Robotics Challenge, a competition designed to spur development of advanced robots that can assist humans in recovering from natural and man-made disasters. Twenty-six teams from eight countries competed in three different tasks providing an excellent evaluation of the relative effectiveness of different approaches to human-machine system design.

This paper introduces the concept of Coactivity as a new focal point for Human-Robot Interaction to address the more sophisticated roles of partner or teammate envisioned for future human-robot systems. We propose that most approaches to date have focused on autonomy and suggest that autonomy is the wrong focal point. The envisioned roles, if properly performed, have a high level of interdependence that cannot be addressed solely by autonomy and necessitate a focus on the coactivity.

This paper presents work by researchers at The Florida Institute for Human and Machine Cognition (IHMC) on the DARPA funded Learning Locomotion program. This program started in 2005 and finished in 2009. The goal of the Learning Locomotion program was to develop control algorithms for autonomous traversal of large, irregular obstacles by an unmanned quadrupedal robot. The program was designed to allow multiple teams to compete for the fastest speeds across the terrain boards using identical hardware. Terrain sensing and localization issues were eliminated by providing a high resolution height map of the terrain as well as real time pose information of the robot on the terrain. The objective for each trial was to reach the goal position as fast as possible.

Current fully autonomous robots are unable to navigate effectively in visually complex environments due to limitations in sensing and cognition. Full teleoperation using current interfaces is difficult and the operator often makes navigation mistakes due to lack of operating environment information and a limited field of view. We present a novel method for combining the sensing and cognition of a robot with that of a human. Our collaborative approach is different from most in that we address bi-directional considerations. It provides the human a mechanism to supplement the robot's capabilities in a new and unique way and provides novel forms of feedback from the robot to enhance the human's understanding of the current state of the system and its intentions.

This video highlights work to date on the Yobotics-IHMC lower body humanoid robot. The robot is a twelve degree-of-freedom robot with force controllable series elastic actuators at each degree of freedom. Control algorithms utilize virtual model control, and foot placement is determined using capture regions. The robot can recover from moderate disturbances and walk on flat ground. Ongoing work is focused on improving robustness to disturbances, walking more quickly and efficiently, and walking over rough terrain.

Several key aspects of coordination such as teamwork, roles, and communication, are enabled and driven by, and even largely defined by, various systems of regulation. One key feature of all these elements in human coordination is their dynamic nature. We have developed a framework to provide a dynamic regulatory system for supporting coordination in human-robot teamwork. This framework supports the definition and functions of roles within teams, as well as the creation of subteams and the roles within them. It also serves to regulate communications in support of coordination. We have demonstrated our system with a team of two humans and five robots performing advanced coordination while trying to apprehend an intruder hiding on a cluttered pier. This work lays the foundation for human-robot coordination based on dynamic regulation.

We present a controller for a quadrupedal robot statically walking on known rough terrain. The controller has both deliberative and reactive components for task specific control issues, such as impassable terrain and unmodeled foot slippage. The controller architecture supports multiple gaits, and we present both a stable omnidirectional gait and a faster directional gait. The robot successfully negotiates obstacles up to 7.5 cm (ap40% leg length) tall and navigates over rocky terrain.

Complex services in service-oriented architectures such as the grid typically require to be configured in multiple ways that cannot be anticipated by service designers; we illustrate this requirement by studying the myGrid registry, a grid registry capable of supporting annotations of service descriptions by third-party users. Instead, services have to be conceived so that they can be configured at deployment and run time. We argue that KAoS is a powerful and flexible language that can help define such configurations. Using our registry case study, we examine the requirements that the definition of such complex configurations brings on policy languages and explain how they can be satisfied. Specifically, we use role-value maps to express constraints between property values; we introduce a notion of PolicySet with associated parameters that support constraints within a well defined scope; finally, we define a notion of context that allows us to refer to property values that were extant in past execution environments. Essentially, these concepts allow us to add constraints to values in policy definitions, to organise policies in coherent and structure blocks, and to refer to the execution history. The paper discusses these concepts and how they are implemented in a binding of the KAoS policy language to the myGrid registry.