Research

@article{loghmani_intermodal_rot_2020,

title = {Unsupervised Domain Adaptation Through Inter-Modal Rotation for RGB-D Object Recognition},

author = {Mohammad Reza Loghmani and Luca Robbiano and Mirco Planamente and Kiru Park and Barbara Caputo and Markus Vincze},

doi = {10.1109/LRA.2020.3007092},

year  = {2020},

date = {2020-01-01},

journal = {IEEE Robotics and Automation Letters},

volume = {5},

number = {4},

pages = {6631-6638},

keywords = {IEEE Robotics and Automation Letters, Object recognition, RGB-D perception},

pubstate = {published},

tppubtype = {article}

}

@article{senese2020response,

title = {A Response Retrieval Approach for Dialogue Using a Multi-Attentive Transformer},

author = {Matteo A Senese and Alberto Benincasa and Barbara Caputo and Giuseppe Rizzo},

year  = {2020},

date = {2020-01-01},

journal = {arXiv preprint arXiv:2012.08148},

keywords = {Transformers},

pubstate = {published},

tppubtype = {article}

}

@article{Fontanel2020BoostingDO,

title = {Boosting Deep Open World Recognition by Clustering},

author = {Dario Fontanel and Fabio Cermelli and Massimiliano Mancini and Bulò Samuel Rota and Elisa Ricci and Barbara Caputo},

year  = {2020},

date = {2020-01-01},

journal = {IEEE Robotics and Automation Letters},

volume = {5},

pages = {5985-5992},

keywords = {IEEE Robotics and Automation Letters, Object recognition},

pubstate = {published},

tppubtype = {article}

}

@article{berton_adaptive-attentive_2020,

title = {Adaptive-Attentive Geolocalization from few queries: a hybrid approach},

author = {Gabriele Moreno Berton and Valerio Paolicelli and Carlo Masone and Barbara Caputo},

url = {http://arxiv.org/abs/2010.06897},

year  = {2020},

date = {2020-01-01},

urldate = {2020-11-11},

journal = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},

pages = {2918--2927},

note = {arXiv:2010.06897},

keywords = {CVF Winter Conference on Applications of Computer Vision},

pubstate = {published},

tppubtype = {article}

}

@article{planamente_self-supervised_2020,

title = {Self-Supervised Joint Encoding of Motion and Appearance for First Person Action Recognition},

author = {Mirco Planamente and Andrea Bottino and Barbara Caputo},

url = {http://arxiv.org/abs/2002.03982},

year  = {2020},

date = {2020-01-01},

urldate = {2020-12-15},

journal = {arXiv:2002.03982 [cs]},

note = {arXiv: 2002.03982},

keywords = {Computer Science - Computer Vision and Pattern Recognition, First Person Action Recognition},

pubstate = {published},

tppubtype = {article}

}

@article{fontanel2020challenges,

title = {On the Challenges of Open World Recognition Under Shifting Visual Domains},

author = {Dario Fontanel and Fabio Cermelli and Massimiliano Mancini and Barbara Caputo},

year  = {2020},

date = {2020-01-01},

journal = {IEEE Robotics and Automation Letters},

volume = {6},

number = {2},

pages = {604--611},

publisher = {IEEE},

keywords = {Cross-domain, IEEE Robotics and Automation Letters, Open-world},

pubstate = {published},

tppubtype = {article}

}

@article{cermelli2020prototype,

title = {Prototype-based Incremental Few-Shot Semantic Segmentation},

author = {Fabio Cermelli and Massimiliano Mancini and Yongqin Xian and Zeynep Akata and Barbara Caputo},

year  = {2020},

date = {2020-01-01},

journal = {arXiv preprint arXiv:2012.01415},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{duan_learning_2020,

title = {Learning to Avoid Obstacles with Minimal Intervention Control},

author = {Anqing Duan and Raffaello Camoriano and Diego Ferigo and Yanlong Huang and Daniele Calandriello and Lorenzo Rosasco and Daniele Pucci},

url = {https://www.frontiersin.org/articles/10.3389/frobt.2020.00060/abstract},

doi = {10.3389/frobt.2020.00060},

issn = {2296-9144},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {Frontiers in Robotics and AI},

volume = {7},

abstract = {Programming by demonstration has received much attention as it offers a general framework which allows robots to efficiently acquire novel motor skills from a human teacher. While traditional imitation learning that only focuses on either Cartesian or joint space might become inappropriate in situations where both spaces are equally important (e.g. writing or striking task), hybrid imitation learning of skills in both Cartesian and joint spaces simultaneously has been studied recently. However, an important issue which often arises in dynamical or unstructured environments is overlooked, namely how can a robot avoid obstacles? In this paper, we aim to address the problem of avoiding obstacles in the context of hybrid imitation learning. Specifically, we propose to tackle three subproblems: (i) designing a proper potential field so as to bypass obstacles, (ii) guaranteeing joint limits are respected when adjusting trajectories in the process of avoiding obstacles, and (iii) determining proper control commands for robots such that potential human-robot interaction is safe. By solving the aforementioned subproblems, the robot is capable of generalizing observed skills to new situations featuring obstacles in a feasible and safe manner. The effectiveness of the proposed method is validated through a toy example as well as a real transportation experiment on the iCub humanoid robot.},

note = {Publisher: Frontiers},

keywords = {humanoid robots, minimal intervention control, obstacle avoidance, Programming by Demonstration, Robotics},

pubstate = {published},

tppubtype = {article}

}

@article{cermelli2019rgb,

title = {The RGB-D Triathlon: Towards Agile Visual Toolboxes for Robots},

author = {Fabio Cermelli and Massimiliano Mancini and Elisa Ricci and Barbara Caputo},

year  = {2019},

date = {2019-11-01},

booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},

keywords = {RGB-D perception},

pubstate = {published},

tppubtype = {article}

}

@article{loghmani_recurrent_2019,

title = {Recurrent Convolutional Fusion for RGB-Đ Object Recognition},

author = {Mohammad Reza Loghmani and Mirco Planamente and Barbara Caputo and Markus Vincze},

doi = {10.1109/LRA.2019.2921506},

issn = {2377-3766},

year  = {2019},

date = {2019-01-01},

journal = {IEEE Robotics and Automation Letters},

volume = {4},

number = {3},

pages = {2878--2885},

note = {Conference Name: IEEE Robotics and Automation Letters},

keywords = {Clutter, Data mining, Feature extraction, IEEE Robotics and Automation Letters, Object recognition, Object recognition, RGB-D perception, Robot sensing systems, Task analysis, Visual learning},

pubstate = {published},

tppubtype = {article}

}

@article{ttommasi_ijcv2019,

title = {Combining Multiple Cues for Visual Madlibs Question Answering},

author = {Tatiana Tommasi and Arun Mallya and Bryan Plummer and Svetlana Lazebnik and {Alexander C } Berg and {Tamara L } Berg},

doi = {10.1007/s11263-018-1096-0},

issn = {0920-5691},

year  = {2019},

date = {2019-01-01},

journal = {International Journal of Computer Vision},

volume = {127},

number = {1},

pages = {38--60},

publisher = {Springer Netherlands},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{masone2018shared,

title = {Shared planning and control for mobile robots with integral haptic feedback},

author = {Carlo Masone and Mostafa Mohammadi and Paolo Robuffo Giordano and Antonio Franchi},

year  = {2018},

date = {2018-01-01},

journal = {The International Journal of Robotics Research},

volume = {37},

number = {11},

pages = {1395--1420},

publisher = {SAGE Publications Sage UK: London, England},

keywords = {},

pubstate = {published},

tppubtype = {article}

}