Tod Milam

Supporting group interaction among humans and autonomous agents

An important aspect of interaction among groups of humans and software agents is supporting collaboration among these heterogeneous agents while they operate remotely and communicate asynchronously. We are developing an architecture that supports multiple humans interacting with multiple automated control agents in such a manner. We are evaluating this architecture with a group consisting of the crew of a space-based vehicle and the automated software agents controlling the vehicle systems. Such agent interaction is modelled as a loosely co-ordinated group because this model minimizes agent commitment to group goals and constraints while addressing a significant portion of crew and control agent group behaviours. In this paper, we give background on human interaction with space-based automation. We identify related research in multi-agent autonomous architectures and single agent human-computer interaction systems, we describe our architecture design for human-software agent groups and we identify research issues in loosely co-ordinated human-software groups.

Measuring Performance in Real Time during Remote Human-Robot Operations with Adjustable Autonomy

Simulated operations during a recent NASA robotic field test demonstrated realtime computation of performance metrics for human-robot interaction that includes adjustable autonomy.

An environment for distributed collaboration among humans and software agents

This paper describes an implemented software prototype for the Distributed Collaboration and Interaction (DCI) system, which helps humans to act as an integrated part of a multi-agent system. Human interaction with agents who act autonomously most of the time, such as a process control agent in a refinery, has received little attention compared to human interaction with agents who provide a direct service to humans, such as information retrieval. This paper describes how liaison agents within the DCI system can support human interaction with other agents that are not, by design, human-centric but must be supervised by, or coordinated with, humans. The DCI system provides a step toward future seamless integration of humans and software agents into a cohesive multi-agent system.

Measuring robot performance in real-time for NASA robotic reconnaissance operations

Technical advances since Apollo make it possible to perform robotic reconnaissance to gain a better understanding of lunar sites prior to human exploration. NASA is conducting analog field tests to investigate these operations concepts with advanced robots and simulated flight operations. We have developed robot performance monitoring software for use during robotic reconnaissance operations. We measure robot performance by monitoring robot data in real-time and computing robot performance metrics from that data. Metrics are computed for two regimes of flight operations -- remote supervision of autonomous robot operations and debrief support after a flight operations shift. In this paper we describe our performance monitoring software, define the metrics we compute, discuss how these metrics are used in flight operations, and summarize results from recent field tests.

Human Supervision of Robotic Site Surveys

Ground operators will interact remotely with robots on the lunar surface to support site preparation and survey. Astronauts will interact with robots to support outpost buildup and maintenance, as well as mission operations. One mode of interaction required for such operations is the ability to supervise robots performing routine autonomous tasks. Supervision of autonomous robotic activities requires monitoring the robots performance of tasks with minimal human effort. This includes understanding its progress on tasks, awareness when important milestones are achieved or problems impede tasks, and reconstructing situations after the fact by relating task events to recorded data. We are developing a software framework to support such interaction among distributed human teams and robots. We are evaluating our framework for human supervision of mobile robots performing routine site survey operations. We are prototyping a system that (1) monitors data from the K10 robot performing surveys to determine the depth of permafrost at the Haughton Crater on Devon Island, (2) computes performance measures about how well the survey is going, (3) builds summaries of these performance measures, and (4) notifies to appropriate personnel when milestones are achieved or performance indicates a problem. We will evaluate our prototype using data collected during Operational Readiness Tests for the Haughton Crater field test to be conducted in July 2007. In this paper we describe our approach for human supervision of robotic activities and report the results of our evaluation with the K10 robot.

Real-time assessment of robot performance during remote exploration operations

To ensure that robots are used effectively for exploration missions, it is important to assess their performance during operations. We are investigating the definition and computation of performance metrics for assessing remote robotic operations in real-time. Our approach is to monitor data streams from robots, compute performance metrics, and provide Web-based displays of these metrics for assessing robot performance during operations. We evaluated our approach for measuring robot performance with the K10 rovers from NASA Ames Research Center during a field test at Moses Lake Sand Dunes (WA) in June 2008. In this paper we present the results of evaluating our software for robot performance and discuss our conclusions from this evaluation for future robot operations.