Publications

DEPARTMENTS

Emperical Interference

Haptic Intelligence

Modern Magnetic Systems

Perceiving Systems

Physical Intelligence

Robotic Materials

Social Foundations of Computation


Research Groups

Autonomous Vision

Autonomous Learning

Bioinspired Autonomous Miniature Robots

Dynamic Locomotion

Embodied Vision

Human Aspects of Machine Learning

Intelligent Control Systems

Learning and Dynamical Systems

Locomotion in Biorobotic and Somatic Systems

Micro, Nano, and Molecular Systems

Movement Generation and Control

Neural Capture and Synthesis

Physics for Inference and Optimization

Organizational Leadership and Diversity

Probabilistic Learning Group


Topics

Robot Learning

Conference Paper

2022

Autonomous Learning

Robotics

AI

Career

Award


Physical Intelligence Article IEEE TRANSACTIONS ON ROBOTICS SICILIANO, B., HOLLERBACH, J., WALKER, I., ZELINSKY, A., CASALS, A., DE LUCA, A., LUH, P. B., VOLZ, R. A., TAYLOR, R. H., BEKEY, G. A., others, 0 BibTeX

Physical Intelligence Article Robotics Research Woodward, M. A., Sitti, M., Martin, A. E., Post, D. C., Schmiedeler, J. P., Williams, S., Indelman, V., Kaess, M., Roberts, R., Leonard, J. J., others, 0 BibTeX

Physical Intelligence Article Robotics Research Tong, C. H., Furgale, P., Barfoot, T. D., Guizilini, V., Ramos, F., Chen, Y., T\uumová, J., Ulusoy, A., Belta, C., Tenorth, M., others, 0 BibTeX

Book Testtitel Testauthor, 0 BibTeX

Conference Paper Unsupervised Semantic Segmentation with Self-supervised Object-centric Representations Zadaianchuk, A., Kleindessner, M., Zhu, Y., Locatello, F., Brox, T. In 0
In this paper, we show that recent advances in self-supervised feature learning enable unsupervised object discovery and semantic segmentation with a performance that matches the state of the field on supervised semantic segmentation 10 years ago. We propose a methodology based on unsupervised saliency masks and self-supervised feature clustering to kickstart object discovery followed by training a semantic segmentation network on pseudo-labels to bootstrap the system on images with multiple objects. We present results on PASCAL VOC that go far beyond the current state of the art (50.0 mIoU), and we report for the first time results on MS COCO for the whole set of 81 classes: our method discovers 34 categories with more than 20% IoU, while obtaining an average IoU of 19.6 for all 81 categories.
ArXiv BibTeX