My Google Scholar profile.

References

2024:

• Lai, W., Gao, Y., & Lam, T. L. (2024). Vision-Language Model-based Physical Reasoning for Robot Liquid Perception. arXiv Preprint arXiv:2404.06904.
• Wang, C., Zheng, S., Zhong, L., Yu, C., Liang, C., Wang, Y., Gao, Y., Lam, T. L., & Shi, Y. (2024). PepperPose: Full-Body Pose Estimation with a Companion Robot. Proceedings of the CHI Conference on Human Factors in Computing Systems, 1–16.
• Wang, J., Wang, Y., Peng, L., Zhang, H., Gao, H., Wang, C., Gao, Y., Luo, H., & Chen, Y. (2024). Transformable Inspection Robot Design and Implementation for Complex Pipeline Environment. IEEE Robotics and Automation Letters.
• Chen, J., Gao, Y., Hu, J., Deng, F., & Lam, T. L. (2024). Meta-Reinforcement Learning Based Cooperative Surface Inspection of 3D Uncertain Structures using Multi-robot Systems. 2024 IEEE International Conference on Robotics and Automation (ICRA), 7201–7207. IEEE.

2023:

• Hu, J., Fan, C., Jiang, H., Guo, X., Gao, Y., Lu, X., & Lam, T. L. (2023). Boosting lightweight depth estimation via knowledge distillation. International Conference on Knowledge Science, Engineering and Management, 27–39. Springer Nature Switzerland Cham.
• Gao, Y., Chen, J., Chen, X., Wang, C., Hu, J., Deng, F., & Lam, T. L. (2023). Asymmetric self-play-enabled intelligent heterogeneous multirobot catching system using deep multiagent reinforcement learning. IEEE Transactions on Robotics, 39(4), 2603–2622.
• Wang, C., Gao, Y., Fan, C., Hu, J., Lam, T. L., Lane, N. D., & Bianchi-Berthouze, N. (2023). Learn2agree: Fitting with multiple annotators without objective ground truth. International Workshop on Trustworthy Machine Learning for Healthcare, 147–162. Springer Nature Switzerland Cham.
• Wang, Y., Lin, M., Xie, X., Gao, Y., Deng, F., & Lam, T. L. (2023). Asymptotically efficient estimator for range-based robot relative localization. IEEE/ASME Transactions on Mechatronics, 28(6), 3525–3536.
• Zhang, H., Luo, J., Gao, Y., & Ma, W. (2023). An intention inference method for the space non-cooperative target based on BiGRU-Self Attention. Advances in Space Research, 72(5), 1815–1828.
• Gao, Y., Fan, C., Hu, J., Lam, T. L., Lane, N. D., & Bianchi-Berthouze, N. (2023). Learn2Agree: Fitting with Multiple Annotators Without Objective Ground. Trustworthy Machine Learning for Healthcare: First International Workshop, TML4H 2023, Virtual Event, May 4, 2023, Proceedings, 13932, 147. Springer Nature.

2022:

• Hu, J., Fan, C., Ozay, M., Feng, H., Gao, Y., & Lam, T. L. (2022). Progressive self-distillation for ground-to-aerial perception knowledge transfer. arXiv Preprint arXiv:2208.13404.
• Gao, Y., Zhang, R., & Wang, H. (2022). On the asymptotic properties of a bagging estimator with a massive dataset. Stat, 11(1), e485.
• Wang, X., Zhang, W., Wang, C., Gao, Y., & Liu, M. (2023). Dynamic dense graph convolutional network for skeleton-based human motion prediction. IEEE Transactions on Image Processing, 33, 1–15.

2021:

• Yang, F., Gao, Y., Ma, R., Zojaji, S., Castellano, G., & Peters, C. (2021). A dataset of human and robot approach behaviors into small free-standing conversational groups. PloS One, 16(2), e0247364.
• Wang, C., Gao, Y., Mathur, A., De C. Williams, A. C., Lane, N. D., & Bianchi-Berthouze, N. (2021). Leveraging activity recognition to enable protective behavior detection in continuous data. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 5(2), 1–27.
• Deng, F., Feng, H., Liang, M., Feng, Q., Yi, N., Yang, Y., Gao, Y., & Lam, T. L. (2022). Abnormal occupancy grid map recognition using attention network. 2022 International Conference on Robotics and Automation (ICRA), 8666–8672. IEEE.
• Ahmad, M. I., Gao, Y., Alnajjar, F., Shahid, S., & Mubin, O. (2022). Emotion and memory model for social robots: a reinforcement learning based behaviour selection. Behaviour & Information Technology, 41(15), 3210–3236.
• Guan, H., Gao, Y., Zhao, M., Yang, Y., Deng, F., & Lam, T. L. (2021). AB-Mapper: Attention and BicNet Based Multi-agent Path Finding for Dynamic Crowded Environment. arXiv Preprint arXiv:2110.00760.
• Wang, C., Gao, Y., Fan, C., Hu, J., Lam, T. L., Lane, N. D., & Bianchi-Berthouze, N. (2021). Agreementlearning: An end-to-end framework for learning with multiple annotators without groundtruth. arXiv Preprint arXiv:2109.03596.

2020:

• Peng, M., Wang, C., Gao, Y., Bi, T., Chen, T., Shi, Y., & Zhou, X.-D. (2020). Recognizing micro-expression in video clip with adaptive key-frame mining. arXiv Preprint arXiv:2009.09179.
• Chen, X., Gao, Y., Ghadirzadeh, A., Bjorkman, M., Castellano, G., & Jensfelt, P. (2020). Skew-explore: Learn faster in continuous spaces with sparse rewards.
• Li, Chengxi, Castellano, G., & Gao, Y. (2020). Efficient Learning of Socially Aware Robot Approaching Behavior Toward Groups via Meta-Reinforcement Learning. IEEE/RSJ International Conference on Intelligent Robots and Systems, 12156–12159.
• Gao, Y. (2020). Machine Behavior Development and Analysis using Reinforcement Learning. Acta Universitatis Upsaliensis.

2019:

• Gao, Y., Yang, F., Frisk, M., Hemandez, D., Peters, C., & Castellano, G. (2019). Learning socially appropriate robot approaching behavior toward groups using deep reinforcement learning. 2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), 1–8. IEEE.
• Gao, Y., Sibirtseva, E., Castellano, G., & Kragic, D. (2019). Fast adaptation with meta-reinforcement learning for trust modelling in human-robot interaction. 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 305–312. IEEE.
• Hernandez, D., Denamganaï, K., Gao, Y., York, P., Devlin, S., Samothrakis, S., & Walker, J. A. (2019). A generalized framework for self-play training. 2019 IEEE Conference on Games (CoG), 1–8. IEEE.

2018:

• Gao, Y., Wallkötter,S., Mohammad, O., & Castellano, G. (2018). Humanrobot proxemics using recurrent neural networks. IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN).
• Gao, Y., Wallkötter, S., Obaid, M., & Castellano, G. (2018). Investigating deep learning approaches for human-robot proxemics. 2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), 1093–1098. IEEE.
• Gao, Y., Barendregt, W., Obaid, M., & Castellano, G. (2018). When robot personalisation does not help: Insights from a robot-supported learning study. 2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), 705–712. IEEE.
• Li, Chengjie, Androulakaki, T., Gao, A. Y., Yang, F., Saikia, H., Peters, C., & Skantze, G. (2018). Effects of posture and embodiment on social distance in human-agent interaction in mixed reality. Proceedings of the 18th International Conference on Intelligent Virtual Agents, 191–196.

2017:

• Gao, A. Y., Barendregt, W., & Castellano, G. (2017). Personalised human-robot co-adaptation in instructional settings using reinforcement learning. IVA Workshop on Persuasive Embodied Agents for Behavior Change: PEACH 2017, August 27, Stockholm, Sweden.
• Zhang, P., Gao, A. Y., & Theel, O. (2017). Less is more: Learning more with concurrent transmissions for energy-efficient flooding. Proceedings of the 14th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, 323–332.

2016:

• Gao, Y., & Glowacka, D. (2016). Deep gate recurrent neural network. Asian Conference on Machine Learning, 350–365. PMLR. 2015:
• Gao, Y., Ilves, K., & Głowacka, D. (2015). Officehours: A system for student supervisor matching through reinforcement learning. Companion Proceedings of the 20th International Conference on Intelligent User Interfaces, 29–32.