One crucial concern is execution safety in domestic environments where robots work with humans and everyday objects. Unsafe situations can arise from the robot’s own actions, the actions of a human, or environmental factors. These situations should be avoided as much as possible. This project aims to design and develop an effective system for service robots to predict and prevent unsafe states.
This research is funded by a grant from the Scientific and Technological Research Council of Turkey (TUBITAK), Grant No. 119E-436.
Multimodal Detection and Classification of Robot Manipulation Failures
IEEE Robotics and Automation Letters, Volume 9, Issue 2, pp. 1396 - 1403, 2024
Arda Inceoglu, Eren Erdal Aksoy, Sanem Sariel
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Learning Failure Prevention Skills for Safe Robot Manipulation
IEEE Robotics and Automation Letters Volume 8, Issue 12, pp. 7994 - 8001, 2023
Abdullah Cihan Ak, Eren Erdal Aksoy, Sanem Sariel
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Adversarial Learning of Failure Prevention Policies
31st Signal Processing and Communications Applications Conference (SIU), 2023
Mert Can Kutay, Abdullah Cihan Ak, Sanem Sariel
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A Variational Graph Autoencoder for Manipulation Action Recognition and Prediction
20th International Conference on Advanced Robotics (ICAR), 2021
Gamze Akyol, Sanem Sariel, Eren Erdal Aksoy
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FINO-Net: A Deep Multimodal Sensor Fusion Framework for Manipulation Failure Detection
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021
Arda Inceoglu, Eren Erdal Aksoy, Abdullah Cihan Ak, Sanem Sariel
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