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 the CLeAR group website.
Selected Recent Publications
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 ]
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 ]