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Yanhong Chen

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IROS Conference 2020 Conference Paper

BioARS: Designing Adaptive and Reconfigurable Bionic Assembly Robotic System with Inchworm Modules

Yide Liu
Donghao Zhao
Yanhong Chen
Dongqi Wang 0002
Zhou Wen
Ziyi Ye
Jianhui Guo
Haofei Zhou

Designing a swarm of robots to address different tasks and adapt to various environments through self-assembly is one of the most challenging topics in the field of robotics research. Here, we present an assembly robotic system with inchworm robots as modules. The system is called BioARS (Bionic Assembly Robotic System). It can either work as a swarm of individual untethered inchworm robots or be assembled into a quadruped robot. The inchworm robots are connected by magnets using a "shoulder-to-shoulder" connecting method, which helps strengthen the magnetic connection. Central pattern generators are used to control the trot gait of the quadruped robot. Our experiments demonstrate that the bionic assembly system is adaptive in that it can pass through confined spaces in the form of inchworms and walk on rough terrain in the form of a quadruped robot. The proposed BioARS, therefore, combines the flexibility of inchworms and the adaptability of quadruped animals, which is promising for application in planetary exploration, earthquake search and rescue operations. We also provide several examples of directions for future research regarding our system.

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NeurIPS Conference 2020 Conference Paper

Multi-task Additive Models for Robust Estimation and Automatic Structure Discovery

Yingjie Wang
Hong Chen
Feng Zheng
Chen Xu
Tieliang Gong
Yanhong Chen

Additive models have attracted much attention for high-dimensional regression estimation and variable selection. However, the existing models are usually limited to the single-task learning framework under the mean squared error (MSE) criterion, where the utilization of variable structure depends heavily on priori knowledge among variables. For high-dimensional observations in real environment, e. g. , Coronal Mass Ejections (CMEs) data, the learning performance of previous methods may be degraded seriously due to the complex non-Gaussian noise and the insufficiency of prior knowledge on variable structure. To tackle this problem, we propose a new class of additive models, called Multi-task Additive Models (MAM), by integrating the mode-induced metric, the structure-based regularizer, and additive hypothesis spaces into a bilevel optimization framework. Our approach does not require any priori knowledge of variable structure and suits for high-dimensional data with complex noise, e. g. , skewed noise, heavy-tailed noise, and outliers. A smooth iterative optimization algorithm with convergence guarantees is provided to implement MAM efficiently. Experiments on simulations and the CMEs analysis demonstrate the competitive performance of our approach for robust estimation and automatic structure discovery.

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