Harold Soh

Hello! I’m an Assistant Professor at the Department of Computer Science at the National University of Singapore, where I direct the Collaborative Learning and Adaptive Robots (CLeAR) group. My current research interests are in human-AI/robot interaction, machine learning, and robotics. Previously, I’ve worked on robots that help children get around, continuous robot/machine learning, controlling disease spread, and self-driving cars.

Latest News

12 Jan 2020: Congratulations to Yaqi who has just won a Research Achievement Award!
11 Jan 2020: Two Late Breaking Reports (one on Human-Robot Trust and the other on cooperative tasks) accepted to HRI 2020 in Cambridge! PDFs coming soon!
9 Dec 2019: I’m at NeurIPS 2019. If you’re attending and looking for a postdoctoral fellowship in human-AI/robot collaboration, please send me an email!
11 Nov 2019: Our joint work with Tan Zhi Xuan and Desmond Ong on multi-modal time-series learning was just accepted to AAAI 2020!
4 Sept 2019: Our work on embedding logic formulas (specifically, d-DNNFs) to improve deep learning was just accepted to NeurIPS 2019! A great collaboration with with Xu Ziwei, Kuldeep Meel, and Mohan Kankanhalli.
5th August 2019: Abdul Fatir has won a Research Achievement Award! Congratulations Fatir!
5th July 2019: Our work on Multi-Task Trust Transfer for Human-Robot Interaction was just accepted to IJRR! Read about our recent work on Human-Robot Trust.
27 May 2019: Check out our Tech Feature in the Block71 newsletter.
28 Jan 2019: Our work on tactile (touch-based) texture classification was accepted to ICRA 2019.
17 Jan 2019: Won a Faculty Teaching Excellence Award for 2017/18

The Collaborative, Learning, and Adaptive Robots (CLeAR) group at NUS investigates the science and engineering of human-AI/robot teams. We conduct human-robot-interaction (HRI) experiments and develop novel machine-learning (ML) methods to enable robots to work effectively with people. We are interested in both cognitive and physical aspects of human-robot interaction. For example, we’ve recently developed new machine-learning (ML) models of human trust and enabled robots to identify objects by touch. As part of our research, we also develop general-purpose ML methods for integrating prior knowledge into deep-learning, yielding deep disentangled generative networks and deep models that leverage symbolic knowledge.

For more information, please check out our recent publications or get in touch.

Selected Recent Publications

Multi-Task Trust Transfer for Human Robot Interaction, Harold Soh, Yaqi Xie, Min Chen, and David Hsu, International Journal of Robotics Research (IJRR), 2019
[ Pre-print PDF | IJRR Link ]

Embedding Symbolic Knowledge into Deep Networks, Yaqi Xie, Ziwei Xu, Kuldeep Meel, Mohan Kankanhalli, and Harold Soh, Neural Information Processing Systems (NeurIPS), 2019
[ Pre-print PDF ]

Hyperprior Induced Unsupervised Disentanglement of Latent Representations, Abdul Fatir Ansari and Harold Soh, Thirty-Third AAAI Conference on Artificial Intelligence (AAAI), 2019
[ PDF | Github ]

Robot Capability and Intention in Trust-based Decisions across Tasks, Xie Yaqi, Indu Prasad, Desmond Ong, David Hsu and Harold Soh, ACM/IEEE Conference on Human Robot Interaction (HRI), 2019
[ PDF ]

Towards Effective Tactile Identification of Textures using a Hybrid Touch Approach, Tasbolat Taunyazov, Hui Fang Koh, Yan Wu, Caixia Cai and Harold Soh, IEEE International Conference on Robotics and Automation (ICRA), 2019
[ PDF | Data@Github ]

Generation Meets Recommendation: Proposing Novel Items for Groups of Users, Vo Vinh Thanh and Harold Soh, ACM Recommender Systems Conference (RecSys), 2018, (Best Long Paper Award Runner-up)
[ PDF | Poster | Slides ]

Latest News

Selected Recent Publications

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