EPSRC | Stefano Rosa

(Chen et al., 2022) (Hu et al., 2021) (Hu et al., 2020) (Chen et al., 2019) (Rosa et al., 2018) (Wang et al., 2018) (Wang et al., 2018) (Xie et al., 2018) (Wang et al., 2018) (Rosa et al., 2018)

References

2022

Learning selective sensor fusion for state estimation

Changhao Chen, Stefano Rosa, Chris Xiaoxuan Lu , and 3 more authors

IEEE Transactions on Neural Networks and Learning Systems, 2022

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we propose an end-to-end selective sensor fusion module that can be applied to modality pairs, such as monocular images and inertial measurements, depth images, and light detection and ranging (LIDAR) point clouds.

2021

Learning semantic segmentation of large-scale point clouds with random sampling

Qingyong Hu, Bo Yang, Linhai Xie , and 5 more authors

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021

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2020

Randla-net: Efficient semantic segmentation of large-scale point clouds

Qingyong Hu, Bo Yang, Linhai Xie , and 5 more authors

In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020

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We show that random sampling combined with attention can achieve SOA performances in semantic segmentation while processing large point clouds in near real-time.

2019

Selective sensor fusion for neural visual-inertial odometry

Changhao Chen, Stefano Rosa, Yishu Miao , and 4 more authors

In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019

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We show how data-learned sensor fusion strategies can improve accuracy and robustness in deep VIO when dealing with noisy/corrupted data, while adding interpretability.

2018

Semantic place understanding for human–robot coexistence—toward intelligent workplaces

Stefano Rosa, Andrea Patane, Chris Xiaoxuan Lu , and 1 more author

IEEE Transactions on Human-Machine Systems, 2018

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Robots and users can work synergistically by mutually learning over time, and benefitting from each other by exploiting each other’s strengths. We show how detecting user activities can help robots to learn semantic understanding of the environment, while at the same time learning to better localise the user.
Learning the intuitive physics of non-rigid object deformations

Zhihua Wang, Stefano Rosa, and Andrew Markham

In Neural Information Processing Systems (NIPS) Workshops , 2018

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Neural allocentric intuitive physics prediction from real videos

Zhihua Wang, Stefano Rosa, Yishu Miao , and 4 more authors

arXiv preprint arXiv:1809.03330, 2018

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We learn how to predict future video of interacting objects by decoupling the problem into appearence and dynamics and leaning invertible transformations from real domain to simulation domain and from egocentric view to allocentric view and vice-versa.
Learning with training wheels: speeding up training with a simple controller for deep reinforcement learning

Linhai Xie, Sen Wang, Stefano Rosa , and 2 more authors

In 2018 IEEE international conference on robotics and automation (ICRA) , 2018

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We propose a way to embed a switchable, simple controller into a deep reinforcement learning algorithm, to speed up training of mobile robot navigation in simulated environments.
3d-physnet: Learning the intuitive physics of non-rigid object deformations

Zhihua Wang, Stefano Rosa, Bo Yang , and 3 more authors

In Proceedings of the 27th International Joint Conference on Artificial Intelligence, Stockholm, SWE , 2018

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We show that conditioning a generative model that predicts soft object deformations on real physical properties can improve prediction accuracy as well as enabling generalisation abilities.
CommonSense: Collaborative learning of scene semantics by robots and humans

Stefano Rosa, Andrea Patanè, Xiaoxuan Lu , and 1 more author

In Proceedings of the 1st International Workshop on Internet of People, Assistive Robots and Things , 2018

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