Get our free extension to see links to code for papers anywhere online!Free add-on: code for papers everywhere!Free add-on: See code for papers anywhere!

Add to Chrome

Add to Firefox

Add to Edge

Jin Pan

A Large-Scale Dataset for Benchmarking Elevator Button Segmentation and Character Recognition

Mar 22, 2021
Jianbang Liu, Yuqi Fang, Delong Zhu, Nachuan Ma, Jin Pan, Max Q. -H. Meng

Figure 1 for A Large-Scale Dataset for Benchmarking Elevator Button Segmentation and Character Recognition

Figure 2 for A Large-Scale Dataset for Benchmarking Elevator Button Segmentation and Character Recognition

Figure 3 for A Large-Scale Dataset for Benchmarking Elevator Button Segmentation and Character Recognition

Figure 4 for A Large-Scale Dataset for Benchmarking Elevator Button Segmentation and Character Recognition

Human activities are hugely restricted by COVID-19, recently. Robots that can conduct inter-floor navigation attract much public attention, since they can substitute human workers to conduct the service work. However, current robots either depend on human assistance or elevator retrofitting, and fully autonomous inter-floor navigation is still not available. As the very first step of inter-floor navigation, elevator button segmentation and recognition hold an important position. Therefore, we release the first large-scale publicly available elevator panel dataset in this work, containing 3,718 panel images with 35,100 button labels, to facilitate more powerful algorithms on autonomous elevator operation. Together with the dataset, a number of deep learning based implementations for button segmentation and recognition are also released to benchmark future methods in the community. The dataset will be available at \url{https://github.com/zhudelong/elevator_button_recognition

Via

Access Paper or Ask Questions

Learning to Interrupt: A Hierarchical Deep Reinforcement Learning Framework for Efficient Exploration

Jul 30, 2018
Tingguang Li, Jin Pan, Delong Zhu, Max Q. -H. Meng

Figure 1 for Learning to Interrupt: A Hierarchical Deep Reinforcement Learning Framework for Efficient Exploration

Figure 2 for Learning to Interrupt: A Hierarchical Deep Reinforcement Learning Framework for Efficient Exploration

Figure 3 for Learning to Interrupt: A Hierarchical Deep Reinforcement Learning Framework for Efficient Exploration

Figure 4 for Learning to Interrupt: A Hierarchical Deep Reinforcement Learning Framework for Efficient Exploration

To achieve scenario intelligence, humans must transfer knowledge to robots by developing goal-oriented algorithms, which are sometimes insensitive to dynamically changing environments. While deep reinforcement learning achieves significant success recently, it is still extremely difficult to be deployed in real robots directly. In this paper, we propose a hybrid structure named Option-Interruption in which human knowledge is embedded into a hierarchical reinforcement learning framework. Our architecture has two key components: options, represented by existing human-designed methods, can significantly speed up the training process and interruption mechanism, based on learnable termination functions, enables our system to quickly respond to the external environment. To implement this architecture, we derive a set of update rules based on policy gradient methods and present a complete training process. In the experiment part, our method is evaluated in Four-room navigation and exploration task, which shows the efficiency and flexibility of our framework.

* 6 pages, 6 figures

Via

Access Paper or Ask Questions