Zhenshan Bing's webpage

selected publications

1. 

   Lateral flexion of a compliant spine improves motor performance in a bioinspired mouse robot

   Zhenshan Bing, Alex Rohregger, Florian Walter, Yuhong Huang, Peer Lucas, Fabrice O. Morin, Kai Huang, and Alois Knoll

   Science Robotics 2023

   Abs Bib

   A flexible spine is critical to the motion capability of most animals and plays a pivotal role in their agility. Although state-of-the-art legged robots have already achieved very dynamic and agile movement solely relying on their legs, they still exhibit the type of stiff movement that compromises movement efficiency. The integration of a flexible spine thus appears to be a promising approach to improve their agility, especially for small and underactuated quadruped robots that are underpowered because of size limitations. Here, we show that the lateral flexion of a compliant spine can promote both walking speed and maneuver agility for a neurorobotic mouse (NeRmo). We present NeRmo as a biomimetic robotic mouse that mimics the morphology of biological mice and their muscle-tendon actuation system. First, by leveraging the lateral flexion of the compliant spine, NeRmo can greatly increase its static stability in an initially unstable configuration by adjusting its posture. Second, the lateral flexion of the spine can also effectively extend the stride length of a gait and therefore improve the walking speeds of NeRmo. Finally, NeRmo shows agile maneuvers that require both a small turning radius and fast walking speed with the help of the spine. These results advance our understanding of spine-based quadruped locomotion skills and highlight promising design concepts to develop more agile legged robots. Lateral flexion of a compliant spine can improve motor skills of a bioinspired mouse robot.

   @article{doi:10.1126/scirobotics.adg7165,
     author = {Bing, Zhenshan and Rohregger, Alex and Walter, Florian and Huang, Yuhong and Lucas, Peer and Morin, Fabrice O. and Huang, Kai and Knoll, Alois},
     title = {Lateral flexion of a compliant spine improves motor performance in a bioinspired mouse robot},
     journal = {Science Robotics},
     year = {2023},
     volume = {8},
     month = {},
     number = {85},
     pages = {eadg7165},
     issn = {},
     doi = {10.1126/scirobotics.adg7165},
     language = {},
     keywords = {},
   }

2. 

   Simulation to Real: Learning Energy-Efficient Slithering Gaits for a Snake-Like Robot

   Zhenshan Bing, Long Cheng, Kai Huang, and Alois Knoll

   IEEE Robotics & Automation Magazine 2022

   Bib PDF

   @article{9895427,
     author = {Bing, Zhenshan and Cheng, Long and Huang, Kai and Knoll, Alois},
     journal = {IEEE Robotics & Automation Magazine},
     title = {Simulation to Real: Learning Energy-Efficient Slithering Gaits for a Snake-Like Robot},
     year = {2022},
     volume = {29},
     number = {4},
     pages = {92-103},
     doi = {10.1109/MRA.2022.3204237},
   }

3. 

   Meta-reinforcement learning in non-stationary and dynamic environments

   Zhenshan Bing, David Lerch, Kai Huang, and Alois Knoll

   IEEE Transactions on Pattern Analysis and Machine Intelligence 2022

   Bib HTML PDF Code Video

   @article{bing2022meta,
     title = {Meta-reinforcement learning in non-stationary and dynamic environments},
     author = {Bing, Zhenshan and Lerch, David and Huang, Kai and Knoll, Alois},
     journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
     year = {2022},
     publisher = {IEEE},
     video = {},
   }

4. 

   Solving Robotic Manipulation with Sparse Reward Reinforcement Learning via Graph-Based Diversity and Proximity

   Zhenshan Bing, Hongkuan Zhou, Rui Li, Xiaojie Su, Fabrice Oliver Morin, Kai Huang, and Alois Knoll

   IEEE Transactions on Industrial Electronics 2022
5. 

   Toward Cognitive Navigation: Design and Implementation of a Biologically Inspired Head Direction Cell Network

   Zhenshan Bing, Amir EI Sewisy, Genghang Zhuang, Florian Walter, Fabrice O. Morin, Kai Huang, and Alois Knoll

   IEEE Transactions on Neural Networks and Learning Systems 2022
6. 

   Robotic Manipulation in Dynamic Scenarios via Bounding-Box-Based Hindsight Goal Generation

   Zhenshan Bing, Erick Álvarez, Long Cheng, Fabrice O. Morin, Rui Li, Xiaojie Su, Kai Huang, and Alois Knoll

   IEEE Transactions on Neural Networks and Learning Systems 2021
7. 

   Complex Robotic Manipulation via Graph-Based Hindsight Goal Generation

   Zhenshan Bing, Matthias Brucker, Fabrice O. Morin, Rui Li, Xiaojie Su, Kai Huang, and Alois Knoll

   IEEE Transactions on Neural Networks and Learning Systems 2021
8. Neural Networks

   Indirect and direct training of spiking neural networks for end-to-end control of a lane-keeping vehicle

   Zhenshan Bing, Claus Meschede, Guang Chen, Alois Knoll, and Kai Huang

   Neural Networks 2020

   Abs Code

   Building spiking neural networks (SNNs) based on biological synaptic plasticities holds a promising potential for accomplishing fast and energy-efficient computing, which is beneficial to mobile robotic applications. However, the implementations of SNNs in robotic fields are limited due to the lack of practical training methods. In this paper, we therefore introduce both indirect and direct end-to-end training methods of SNNs for a lane-keeping vehicle. First, we adopt a policy learned using the Deep Q-Learning (DQN) algorithm and then subsequently transfer it to an SNN using supervised learning. Second, we adopt the reward-modulated spike-timing-dependent plasticity (R-STDP) for training SNNs directly, since it combines the advantages of both reinforcement learning and the well-known spike-timing-dependent plasticity (STDP). We examine the proposed approaches in three scenarios in which a robot is controlled to keep within lane markings by using an event-based neuromorphic vision sensor. We further demonstrate the advantages of the R-STDP approach in terms of the lateral localization accuracy and training time steps by comparing them with other three algorithms presented in this paper.