Author name cluster

Stephanie Rosenthal

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9 papers

2 author rows

AAAI Conference 2017 Conference Paper

Vision-Language Fusion for Object Recognition

Sz-Rung Shiang
Stephanie Rosenthal
Anatole Gershman
Jaime Carbonell
Jean Oh

While recent advances in computer vision have caused object recognition rates to spike, there is still much room for improvement. In this paper, we develop an algorithm to improve object recognition by integrating human-generated contextual information with vision algorithms. Speciﬁcally, we examine how interactive systems such as robots can utilize two types of context information–verbal descriptions of an environment and human-labeled datasets. We propose a re-ranking schema, MultiRank, for object recognition that can ef- ﬁciently combine such information with the computer vision results. In our experiments, we achieve up to 9. 4% and 16. 6% accuracy improvements using the oracle and the detected bounding boxes, respectively, over the vision-only recognizers. We conclude that our algorithm has the ability to make a signiﬁcant impact on object recognition in robotics and beyond.

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IJCAI Conference 2016 Conference Paper

Verbalization: Narration of Autonomous Robot Experience

Stephanie Rosenthal
Sai P. Selvaraj
Manuela Veloso

Autonomous mobile robots navigate in our spaces by planning and executing routes to destinations. When a mobile robot appears at a location, there is no clear way to understand what navigational path the robot planned and experienced just by looking at it. In this work, we address the generation of narrations of autonomous mobile robot navigation experiences. We contribute the concept of verbalization as a parallel to the well-studied concept of visualization. Through verbalizations, robots can describe through language what they experience, in particular in their paths. For every executed path, we consider many possible verbalizations that could be generated. We introduce the verbalization space that covers the variability of utterances that the robot may use to narrate its experience to different humans. We present an algorithm for segmenting a path and mapping each segment to an utterance, as a function of the desired point in the verbalization space, and demonstrate its application using our mobile service robot moving in our buildings. We believe our verbalization space and algorithm are applicable to different narrative aspects for many mobile robots, including autonomous cars.

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IJCAI Conference 2015 Conference Paper

CoBots: Robust Symbiotic Autonomous Mobile Service Robots

Manuela Veloso
Joydeep Biswas
Brian Coltin
Stephanie Rosenthal

We research and develop autonomous mobile service robots as Collaborative Robots, i. e. , CoBots. For the last three years, our four CoBots have autonomously navigated in our multi-floor office buildings for more than 1, 000km, as the result of the integration of multiple perceptual, cognitive, and actuations representations and algorithms. In this paper, we identify a few core aspects of our CoBots underlying their robust functionality. The reliable mobility in the varying indoor environments comes from a novel episodic non-Markov localization. Service tasks requested by users are the input to a scheduler that can consider different types of constraints, including transfers among multiple robots. With symbiotic autonomy, the CoBots proactively seek external sources of help to fill-in for their inevitable occasional limitations. We present sampled results from a deployment and conclude with a brief review of other features of our service robots.

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IJCAI Conference 2013 Conference Paper

Look versus Leap: Computing Value of Information with High-Dimensional Streaming Evidence

Stephanie Rosenthal
Dan Bohus
Ece Kamar
Eric Horvitz

A key decision facing autonomous systems with access to streams of sensory data is whether to act based on current evidence or to wait for additional information that might enhance the utility of taking an action. Computing the value of information is particularly difficult with streaming highdimensional sensory evidence. We describe a belief projection approach to reasoning about information value in these settings, using models for inferring future beliefs over states given streaming evidence. These belief projection models can be learned from data or constructed via direct assessment of parameters and they fit naturally in modular, hierarchical state inference architectures. We describe principles of using belief projection and present results drawn from an implementation of the methodology within a conversational system.

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IROS Conference 2012 Conference Paper

CoBots: Collaborative robots servicing multi-floor buildings

Manuela Veloso
Joydeep Biswas
Brian Coltin
Stephanie Rosenthal
Thomas Kollar
Çetin Meriçli
Mehdi Samadi
Susana Brandão

In this video we briefly illustrate the progress and contributions made with our mobile, indoor, service robots CoBots (Collaborative Robots), since their creation in 2009. Many researchers, present authors included, aim for autonomous mobile robots that robustly perform service tasks for humans in our indoor environments. The efforts towards this goal have been numerous and successful, and we build upon them. However, there are clearly many research challenges remaining until we can experience intelligent mobile robots that are fully functional and capable in our human environments.

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AAAI Conference 2012 Conference Paper

Mobile Robot Planning to Seek Help with Spatially-Situated Tasks

Stephanie Rosenthal
Manuela Veloso

Indoor autonomous mobile service robots can overcome their hardware and potential algorithmic limitations by asking humans for help. In this work, we focus on mobile robots that need human assistance at specific spatially-situated locations (e. g. , to push buttons in an elevator or to make coffee in the kitchen). We address the problem of what the robot should do when there are no humans present at such help locations. As the robots are mobile, we argue that they should plan to proactively seek help and travel to offices or occupied locations to bring people to the help locations. Such planning involves many trade-offs, including the wait time at the help location before seeking help, and the time and potential interruption to find and displace someone in an office. In order to choose appropriate parameters to represent such decisions, we first conduct a survey to understand potential helpers’ travel preferences in terms of distance, interruptibility, and frequency of providing help. We then use these results to contribute a decision-theoretic algorithm to evaluate the possible choices in offices and plan where to proactively seek help. We demonstrate that our algorithm aims to minimize the number of office interruptions as well as task completion time.

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AAAI Conference 2011 Conference Paper

Learning Accuracy and Availability of Humans Who Help Mobile Robots

Stephanie Rosenthal
Manuela Veloso
Anind Dey

When mobile robots perform tasks in environments with humans, it seems appropriate for the robots to rely on such humans for help instead of dedicated human oracles or supervisors. However, these humans are not always available nor always accurate. In this work, we consider human help to a robot as concretely providing observations about the robot’s state to reduce state uncertainty as it executes its policy autonomously. We model the probability of receiving an observation from a human in terms of their availability and accuracy by introducing Human Observation Providers POMDPs (HOP-POMDPs). We contribute an algorithm to learn human availability and accuracy online while the robot is executing its current task policy. We demonstrate that our algorithm is effective in approximating the true availability and accuracy of humans without depending on oracles to learn, thus increasing the tractability of deploying a robot that can occasionally ask for help.

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AAMAS Conference 2010 Conference Paper

An Effective Personal Mobile Robot Agent Through Symbiotic Human-Robot Interaction

Stephanie Rosenthal
Joydeep Biswas
Manuela Veloso

Several researchers, present authors included, envision personalmobile robot agents that can assist humans in their dailytasks. Despite many advances in robotics, such mobile robot agentsstill face many limitations in their perception, cognition, and actioncapabilities. In this work, we propose a symbiotic interaction betweenrobot agents and humans to overcome the robot limitations whileallowing robots to also help humans. We introduce a visitor'scompanion robot agent, as a natural task for such symbioticinteraction, e. g. , the visitor lacks knowledge of the environment butcan easily open a door or read a door label, while the mobile robotwith no arms cannot open a door and may be confused about its exactlocation, but can plan paths well through the building and can provideuseful relevant information to the visitor. We present this visitorcompanion task in detail with an enumeration and formalization of theactions of the robot agent in its interaction with the human. Webriefly describe the wifi-based robot localization algorithm and showresults of the different levels of human help to the robot during the robotnavigation. We then model the tradeoffs of the value of the robot help to the human and present illustrative experiments. Our work has been fully implemented in a mobile robot agent, CoBot, which has successfully navigated for several hours and continues to navigate in our indoor environment.

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IROS Conference 2005 Conference Paper

Designing robots for long-term social interaction

Rachel Gockley
Allison Bruce
Jodi Forlizzi
Marek P. Michalowski
Anne Mundell
Stephanie Rosenthal
Brennan Sellner
Reid G. Simmons

Valerie the roboceptionist is the most recent addition to Carnegie Mellon's social robots project. A permanent installation in the entranceway to Newell-Simon hall, the robot combines useful functionality - giving directions, looking up weather forecasts, etc. - with an interesting and compelling character. We are using Valerie to investigate human-robot social interaction, especially long-term human-robot "relationships". Over a nine-month period, we have found that many visitors continue to interact with the robot on a daily basis, but that few of the individual interactions last for more than 30 seconds. Our analysis of the data has indicated several design decisions that should facilitate more natural human-robot interactions.

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