Willow Garage

About: Willow Garage is a based out in . It is known for research contribution in the topics: Robot & Mobile robot. The organization has 76 authors who have published 191 publications receiving 28617 citations.

User observation & dataset collection for robot training

Abstract: Personal robots operate in human environments such as homes and offices, co-habiting with people. To effectively train robot algorithms for such scenarios, a large amount of training data containing both people and the environment is required. Collecting such data involves taking a robot into new environments, observing and interacting with people. So far, best practices for robot data collection have been undefined. Fortunately, the human-robot interaction community has conducted field studies whose methodology can serve as a model. In this paper, we draw parallels between field study observation and the data collection process, suggesting that best practices may be transferable. As a use case, we present a robot sensor dataset for training and testing algorithms for person detection in indoor environments.

At your service: using butlers as a model to overcome the mobile attention deficit

Abstract: Advances in mobile phones and cellular network capabilities have enabled many opportunities for information access on the move. These capabilities provide instant access for the mobile user, but have exacerbated the problem of interaction in a mobile context. Mobile users are often engaged in another task that makes it difficult for them to filter and interact with their mobile device at the same time. Mobile multitasking creates an attention deficit for the user. This paper proposes using butlers as a model to overcome this problem by offloading the burden of interaction from the user to the device. We describe how a suite of butlers can opportunistically and proactively offer information to the user in the moment, allowing mobile users to stay focused on their task at hand.

IUI workshop on interactive machine learning

Abstract: Many applications of Machine Learning (ML) involve interactions with humans. Humans may provide input to a learning algorithm (in the form of labels, demonstrations, corrections, rankings or evaluations) while observing its outputs (in the form of feedback, predictions or executions). Although humans are an integral part of the learning process, traditional ML systems used in these applications are agnostic to the fact that inputs/outputs are from/for humans.However, a growing community of researchers at the intersection of ML and human-computer interaction are making interaction with humans a central part of developing ML systems. These efforts include applying interaction design principles to ML systems, using human-subject testing to evaluate ML systems and inspire new methods, and changing the input and output channels of ML systems to better leverage human capabilities. With this Interactive Machine Learning (IML) workshop at IUI 2013 we aim to bring this community together to share ideas, get up-to-date on recent advances, progress towards a common framework and terminology for the field, and discuss the open questions and challenges of IML.

Multi-DOF model-based force control for telerobotic applications

Abstract: For a wide range of telerobotic applications, the slave device needs to be a large, powerful, industrial type robot in order to achieve the desired tasks. Due to the large frictional forces within the gearing of such robots, a force-feedback controller is necessary to precisely control the forces the robot applies when manipulating its environment. This paper proves passivity, and therefore guarantees stability, of a model-based force controller in one degree of freedom (DOF) when subject to viscous and Coulomb friction. The controller is then expanded to muli-DOF systems. In addition to maintaining the robustness of the 1-DOF controller, the multi-DOF controller provides additional freedom to design the closed loop dynamics of the robot. This freedom allows the control designer the ability to shape and optimize how the system feels from a users perspective. The robustness of the controller is experimentally validated and the freedom to modify the closed loop dynamics is explored using a 2-DOF device.

HRI 2010 workshop 1: what do collaborations with the arts have to say about HRI?

Abstract: Human-Robot Interaction researchers are beginning to reach out to fields not traditionally associated with interaction research, such as the performing arts, cartooning, and animation These collaborations offer the potential for novel insights about how to get robots and people to interact more effectively, but they also involve a number of unique challenges This full-day workshop will offer a venue for HRI researchers and their collaborators from these diverse fields to report on their work, share insights about the collaboration process, and to help begin to define an exciting new area in HRI