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 ]