Author name cluster

Bei Hua

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4 papers

2 author rows

ICRA Conference 2024 Conference Paper

NaviFormer: A Data-Driven Robot Navigation Approach via Sequence Modeling and Path Planning with Safety Verification

Xuyang Zhang
Ziyang Feng
Quecheng Qiu
Yu'an Chen
Bei Hua
Jianmin Ji

Reinforcement learning has shown great potential in improving the performance of robot navigation. In response to the increasing deployments of mobile robots within various scenarios, a data-driven paradigm of navigation approach with safety verification is preferred where one can train RL algorithms with large amounts of prior data, keep learning continuously, and ensure safe navigation in applications. Conventional end-to-end reinforcement learning navigation paradigms have encountered multiple challenges in meeting these demands. In this work, we introduce a novel robot navigation approach termed NaviFormer. This approach handles navigation tasks based on sequence modeling to obtain the data-driven ability. It also integrates rule-based verification for safety insurance. We conduct a series of experiments to validate the data-driven ability of our approach and to compare it with existing navigation methods. We also perform quantitative tests on a real-world robot platform, TurtleBot. The experimental results show our method’s outstanding data-driven ability and highlight its superior arrival rate and generalization compared to other state-of-the-art methods like the PPO-based navigation method.

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ICRA Conference 2023 Conference Paper

Automatic Generation of Robot Facial Expressions with Preferences

Bing Tang
Rongyun Cao
Rongya Chen
Xiaoping Chen
Bei Hua
Feng Wu

The capability of humanoid robots to generate facial expressions is crucial for enhancing interactivity and emotional resonance in human-robot interaction. However, humanoid robots vary in mechanics, manufacturing, and ap-pearance. The lack of consistent processing techniques and the complexity of generating facial expressions pose significant challenges in the field. To acquire solutions with high confidence, it is necessary to enable robots to explore the solution space automatically based on performance feedback. To this end, we designed a physical robot with a human-like appearance and developed a general framework for automatic expression generation using the MAP-Elites algorithm. The main advan-tage of our framework is that it does not only generate facial expressions automatically but can also be customized according to user preferences. The experimental results demonstrate that our framework can efficiently generate realistic facial expressions without hard coding or prior knowledge of the robot kinematics. Moreover, it can guide the solution-generation process in accordance with user preferences, which is desirable in many real-world applications.

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IJCAI Conference 2022 Conference Paper

A Universal PINNs Method for Solving Partial Differential Equations with a Point Source

Xiang Huang
Hongsheng Liu
Beiji Shi
Zidong Wang
Kang Yang
Yang Li
Min Wang
Haotian Chu

In recent years, deep learning technology has been used to solve partial differential equations (PDEs), among which the physics-informed neural networks (PINNs)method emerges to be a promising method for solving both forward and inverse PDE problems. PDEs with a point source that is expressed as a Dirac delta function in the governing equations are mathematical models of many physical processes. However, they cannot be solved directly by conventional PINNs method due to the singularity brought by the Dirac delta function. In this paper, we propose a universal solution to tackle this problem by proposing three novel techniques. Firstly the Dirac delta function is modeled as a continuous probability density function to eliminate the singularity at the point source; secondly a lower bound constrained uncertainty weighting algorithm is proposed to balance the physics-informed loss terms of point source area and the remaining areas; and thirdly a multi-scale deep neural network with periodic activation function is used to improve the accuracy and convergence speed. We evaluate the proposed method with three representative PDEs, and the experimental results show that our method outperforms existing deep learning based methods with respect to the accuracy, the efficiency and the versatility.

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NeurIPS Conference 2022 Conference Paper

Meta-Auto-Decoder for Solving Parametric Partial Differential Equations

Xiang Huang
Zhanhong Ye
Hongsheng Liu
Shi Ji
Zidong Wang
Kang Yang
Yang Li
Min Wang

Many important problems in science and engineering require solving the so-called parametric partial differential equations (PDEs), i. e. , PDEs with different physical parameters, boundary conditions, shapes of computation domains, etc. Recently, building learning-based numerical solvers for parametric PDEs has become an emerging new field. One category of methods such as the Deep Galerkin Method (DGM) and Physics-Informed Neural Networks (PINNs) aim to approximate the solution of the PDEs. They are typically unsupervised and mesh-free, but require going through the time-consuming network training process from scratch for each set of parameters of the PDE. Another category of methods such as Fourier Neural Operator (FNO) and Deep Operator Network (DeepONet) try to approximate the solution mapping directly. Being fast with only one forward inference for each PDE parameter without retraining, they often require a large corpus of paired input-output observations drawn from numerical simulations, and most of them need a predefined mesh as well. In this paper, we propose Meta-Auto-Decoder (MAD), a mesh-free and unsupervised deep learning method that enables the pre-trained model to be quickly adapted to equation instances by implicitly encoding (possibly heterogenous) PDE parameters as latent vectors. The proposed method MAD can be interpreted by manifold learning in infinite-dimensional spaces, granting it a geometric insight. Extensive numerical experiments show that the MAD method exhibits faster convergence speed without losing accuracy than other deep learning-based methods.

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