Author name cluster

Chek Sing Teo

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3 papers

1 author row

ICRA Conference 2022 Conference Paper

Incremental Few-Shot Object Detection for Robotics

Yiting Li
Haiyue Zhu
Sichao Tian
Fan Feng
Jun Ma 0008
Chek Sing Teo
Cheng Xiang 0001
Prahlad Vadakkepat

Incremental few-shot learning is highly expected for practical robotics applications. On one hand, robot is desired to learn new tasks quickly and flexibly using only few annotated training samples; on the other hand, such new additional tasks should be learned in a continuous and incremental manner without forgetting the previous learned knowledge dramatically. In this work, we propose a novel Class-Incremental Few- Shot Object Detection (CI-FSOD) framework that enables deep object detection network to perform effective continual learning from just few-shot samples without re-accessing the previous training data. We achieve this by equipping the widely-used Faster-RCNN detector with three elegant components. Firstly, to best preserve performance on the pre-trained base classes, we propose a novel Dual-Embedding-Space (DES) architecture which decouples the representation learning of base and novel categories into different spaces. Secondly, to mitigate the catastrophic forgetting on the accumulated novel classes, we propose a Sequential Model Fusion (SMF) method, which is able to achieve long-term memory without additional storage cost. Thirdly, to promote inter-task class separation in feature space, we propose a novel regularization technique that extends the classification boundary further away from the previous classes to avoid misclassification. Overall, our framework is simple yet effective and outperforms the previous SOTA with a significant margin of 2. 4 points in AP performance.

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IROS Conference 2020 Conference Paper

Grasping Detection Network with Uncertainty Estimation for Confidence-Driven Semi-Supervised Domain Adaptation

Haiyue Zhu
Yiting Li
Fengjun Bai
Wenjie Chen
Xiaocong Li
Jun Ma 0008
Chek Sing Teo
Pey Yuen Tao

Data-efficient domain adaptation with only a few labelled data is desired for many robotic applications, e. g. , in grasping detection, the inference skill learned from a grasping dataset is not universal enough to directly apply on various other daily/industrial applications. This paper presents an approach enabling the easy domain adaptation through a novel grasping detection network with confidence-driven semi-supervised learning, where these two components deeply interact with each other. The proposed grasping detection network specially provides a prediction uncertainty estimation mechanism by leveraging on Feature Pyramid Network (FPN), and the mean-teacher semi-supervised learning utilizes such uncertainty information to emphasizing the consistency loss only for those unlabelled data with high confidence, which we referred it as the confidence-driven mean teacher. This approach largely prevents the student model to learn the incorrect/harmful information from the consistency loss, which speeds up the learning progress and improves the model accuracy. Our results show that the proposed network can achieve high success rate on the Cornell grasping dataset, and for domain adaptation with very limited data, the confidence- driven mean teacher outperforms the original mean teacher and direct training by more than 10% in evaluation loss especially for avoiding the overfitting and model diverging.

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IROS Conference 2020 Conference Paper

Learning-Based Controller Optimization for Repetitive Robotic Tasks

Xiaocong Li
Haiyue Zhu
Jun Ma 0008
Tat Joo Teo
Chek Sing Teo
Masayoshi Tomizuka
Tong Heng Lee

Dynamic control for robotic automation tasks is traditionally designed and optimized with a model-based approach, and the performance relies heavily upon accurate system modeling. However, modeling the true dynamics of increasingly complex robotic systems is an extremely challenging task and it often renders the automation system to operate in a non-optimal condition. Notably, many industrial robotic applications involve repetitive motions and constantly generate a large amount of motion data under the non-optimal condition. These motion data contain rich information, and therefore an intelligent automation system should be able to learn from these non-optimal motion data to drive the system to operate optimally in a data-driven manner. In this paper, we propose a learning-based controller optimization algorithm for repetitive robotic tasks. To achieve this, a multi-objective cost function is designed to take into consideration both the trajectory tracking accuracy and smoothness, and then a data-driven approach is developed to estimate the gradient and Hessian based on the motion data for optimization without relying on the dynamic model. Experiments based on a magnetically-levitated nanopositioning system are conducted to demonstrate the effectiveness and practical appeals of the proposed algorithm in repetitive robotic automation tasks.

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