Development of Methodologies, Metrics, and Tools for Investigating Human-Robot Interaction in Space Robotics

Human-robot systems are expected to have a central role in future space exploration missions that extend beyond low-earth orbit [1]. As part of a directed research project funded by NASA s Human Research Program (HRP), researchers at the Johnson Space Center have started to use a variety of techniques, including literature reviews, case studies, knowledge capture, field studies, and experiments to understand critical human-robot interaction (HRI) variables for current and future systems. Activities accomplished to date include observations of the International Space Station s Special Purpose Dexterous Manipulator (SPDM), Robonaut, and Space Exploration Vehicle (SEV), as well as interviews with robotics trainers, robot operators, and developers of gesture interfaces. A survey of methods and metrics used in HRI was completed to identify those most applicable to space robotics. These methods and metrics included techniques and tools associated with task performance, the quantification of human-robot interactions and communication, usability, human workload, and situation awareness. The need for more research in areas such as natural interfaces, compensations for loss of signal and poor video quality, psycho-physiological feedback, and common HRI testbeds were identified. The initial findings from these activities and planned future research are discussed. Human-robot systems are expected to have a central role in future space exploration missions that extend beyond low-earth orbit [1]. As part of a directed research project funded by NASA s Human Research Program (HRP), researchers at the Johnson Space Center have started to use a variety of techniques, including literature reviews, case studies, knowledge capture, field studies, and experiments to understand critical human-robot interaction (HRI) variables for current and future systems. Activities accomplished to date include observations of the International Space Station s Special Purpose Dexterous Manipulator (SPDM), Robonaut, and Space Exploration Vehicle (SEV), as well as interviews with robotics trainers, robot operators, and developers of gesture interfaces. A survey of methods and metrics used in HRI was completed to identify those most applicable to space robotics. These methods and metrics included techniques and tools associated with task performance, the quantification of human-robot interactions and communication, usability, human workload, and situation awareness. The need for more research in areas such as natural interfaces, compensations for loss of signal and poor video quality, psycho-physiological feedback, and common HRI testbeds were identified. The initial findings from these activities and planned future research are discussed

Human Motion Trajectory Prediction: A Survey

With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.Comment: Submitted to the International Journal of Robotics Research (IJRR), 37 page

A Data-driven Approach Towards Human-robot Collaborative Problem Solving in a Shared Space

We are developing a system for human-robot communication that enables people to communicate with robots in a natural way and is focused on solving problems in a shared space. Our strategy for developing this system is fundamentally data-driven: we use data from multiple input sources and train key components with various machine learning techniques. We developed a web application that is collecting data on how two humans communicate to accomplish a task, as well as a mobile laboratory that is instrumented to collect data on how two humans communicate to accomplish a task in a physically shared space. The data from these systems will be used to train and fine-tune the second stage of our system, in which the robot will be simulated through software. A physical robot will be used in the final stage of our project. We describe these instruments, a test-suite and performance metrics designed to evaluate and automate the data gathering process as well as evaluate an initial data set.Comment: 2017 AAAI Fall Symposium on Natural Communication for Human-Robot Collaboratio

User evaluation of an interactive learning framework for single-arm and dual-arm robots

The final publication is available at link.springer.comSocial robots are expected to adapt to their users and, like their human counterparts, learn from the interaction. In our previous work, we proposed an interactive learning framework that enables a user to intervene and modify a segment of the robot arm trajectory. The framework uses gesture teleoperation and reinforcement learning to learn new motions. In the current work, we compared the user experience with the proposed framework implemented on the single-arm and dual-arm Barrett’s 7-DOF WAM robots equipped with a Microsoft Kinect camera for user tracking and gesture recognition. User performance and workload were measured in a series of trials with two groups of 6 participants using two robot settings in different order for counterbalancing. The experimental results showed that, for the same task, users required less time and produced shorter robot trajectories with the single-arm robot than with the dual-arm robot. The results also showed that the users who performed the task with the single-arm robot first experienced considerably less workload in performing the task with the dual-arm robot while achieving a higher task success rate in a shorter time.Peer ReviewedPostprint (author's final draft