9 citations · 11 across the 7 of their papers we have counts for
7 papers
Latent Emission-Augmented Perspective-Taking (LEAPT) for Human-Robot Interaction
Kaiqi Chen, Jing Yu Lim, Kingsley Kuan +1
Perspective-taking is the ability to perceive or understand a situation or concept from another individual's point of view, and is crucial in daily human interactions. Enabling rob…
Semantic Visual Simultaneous Localization and Mapping: A Survey
Kaiqi Chen, Junhao Xiao, Jialing Liu +6
Visual Simultaneous Localization and Mapping (vSLAM) has achieved great progress in the computer vision and robotics communities, and has been successfully used in many fields such…
MIRROR: Differentiable Deep Social Projection for Assistive Human-Robot Communication
Kaiqi Chen, Jeffrey Fong, Harold Soh
Communication is a hallmark of intelligence. In this work, we present MIRROR, an approach to (i) quickly learn human models from human demonstrations, and (ii) use the models for s…
Multi-Modal Mutual Information (MuMMI) Training for Robust Self-Supervised Deep Reinforcement Learning
Kaiqi Chen, Yong Lee, Harold Soh
This work focuses on learning useful and robust deep world models using multiple, possibly unreliable, sensors. We find that current methods do not sufficiently encourage a shared…
Collaborative Visual Inertial SLAM for Multiple Smart Phones
Jialing Liu, Ruyu Liu, Kaiqi Chen +2
The efficiency and accuracy of mapping are crucial in a large scene and long-term AR applications. Multi-agent cooperative SLAM is the precondition of multi-user AR interaction. Th…
Accurate Object Association and Pose Updating for Semantic SLAM
Kaiqi Chen, Jialing Liu, Qinying Chen +2
Current pandemic has caused the medical system to operate under high load. To relieve it, robots with high autonomy can be used to effectively execute contactless operations in hos…
Improving Layer-wise Adaptive Rate Methods using Trust Ratio Clipping
Jeffrey Fong, Siwei Chen, Kaiqi Chen
Training neural networks with large batch is of fundamental significance to deep learning. Large batch training remarkably reduces the amount of training time but has difficulties…