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Srinjoy Majumdar

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2 papers

1 author row

IROS Conference 2018 Conference Paper

Human Gaze Following for Human-Robot Interaction

Akanksha Saran
Srinjoy Majumdar
Elaine Schaertl Short
Andrea Thomaz
Scott Niekum

Gaze provides subtle informative cues to aid fluent interactions among people. Incorporating human gaze predictions can signify how engaged a person is while interacting with a robot and allow the robot to predict a human's intentions or goals. We propose a novel approach to predict human gaze fixations relevant for human-robot interaction tasks-both referential and mutual gaze-in real time on a robot. We use a deep learning approach which tracks a human's gaze from a robot's perspective in real time. The approach builds on prior work which uses a deep network to predict the referential gaze of a person from a single 2D image. Our work uses an interpretable part of the network, a gaze heat map, and incorporates contextual task knowledge such as location of relevant objects, to predict referential gaze. We find that the gaze heat map statistics also capture differences between mutual and referential gaze conditions, which we use to predict whether a person is facing the robot's camera or not. We highlight the challenges of following a person's gaze on a robot in real time and show improved performance for referential gaze and mutual gaze prediction.

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IROS Conference 2017 Conference Paper

Viewpoint selection for visual failure detection

Akanksha Saran
Branka Lakic
Srinjoy Majumdar
Jürgen Hess 0003
Scott Niekum

The visual difference between outcomes in many robotics tasks is often subtle, such as the tip of a screw being near a hole versus in the hole. Furthermore, these small differences are often only observable from certain viewpoints or may even require information from multiple viewpoints to fully verify. We introduce and compare three approaches to selecting viewpoints for verifying successful execution of tasks: (1) a random forest-based method that discovers highly informative fine-grained visual features, (2) SVM models trained on features extracted from pre-trained convolutional neural networks, and (3) an active, hybrid approach that uses the above methods for two-stage multi-viewpoint classification. These approaches are experimentally validated on an IKEA furniture assembly task and a quadrotor surveillance domain.

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