Publications |

2025

Preserving and combining knowledge in robotic lifelong reinforcement learning

Yuan Meng, Zhenshan Bing^*, Xiangtong Yao, Kejia Chen, Kai Huang, Yang Gao, Fuchun Sun, and Alois Knoll

Nature Machine Intelligence 2025

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Humans can continually accumulate knowledge and develop increasingly complex behaviours and skills throughout their lives, which is a capability known as ‘lifelong learning’. Although this lifelong learning capability is considered an essential mechanism that makes up general intelligence, recent advancements in artificial intelligence predominantly excel in narrow, specialized domains and generally lack this lifelong learning capability. Here we introduce a robotic lifelong reinforcement learning framework that addresses this gap by developing a knowledge space inspired by the Bayesian non-parametric domain. In addition, we enhance the agent’s semantic understanding of tasks by integrating language embeddings into the framework. Our proposed embodied agent can consistently accumulate knowledge from a continuous stream of one-time feeding tasks. Furthermore, our agent can tackle challenging real-world long-horizon tasks by combining and reapplying its acquired knowledge from the original tasks stream. The proposed framework advances our understanding of the robotic lifelong learning process and may inspire the development of more broadly applicable intelligence.
@article{Meng2025, author = {Meng, Yuan and Bing*, Zhenshan and Yao, Xiangtong and Chen, Kejia and Huang, Kai and Gao, Yang and Sun, Fuchun and Knoll, Alois}, year = {2025}, date = {2025-02-05}, title = {Preserving and combining knowledge in robotic lifelong reinforcement learning}, journal = {Nature Machine Intelligence}, issn = {2522-5839}, url = {https://doi.org/10.1038/s42256-025-00983-2}, doi = {10.1038/s42256-025-00983-2}, }
Genetic Informed Trees (GIT*): Path planning via reinforced genetic programming heuristics

Liding Zhang, Kuanqi Cai, Zhenshan Bing^*, Chaoqun Wang, and Alois Knoll

Biomimetic Intelligence and Robotics 2025
E-3DGS: 3D Gaussian splatting with exposure and motion events

Xiaoting Yin, Hao Shi, Yuhan Bao, Zhenshan Bing, Yiyi Liao, Kailun Yang, and Kaiwei Wang

Appl. Opt. May 2025

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Achieving 3D reconstruction from images captured under optimal conditions has been extensively studied in the vision and imaging fields. However, in real-world scenarios, challenges such as motion blur and insufficient illumination often limit the performance of standard frame-based cameras in delivering high-quality images. To address these limitations, we incorporate a transmittance adjustment device at the hardware level, enabling event cameras to capture both motion and exposure events for diverse 3D reconstruction scenarios. Motion events (triggered by camera or object movement) are collected in fast-motion scenarios when the device is inactive, while exposure events (generated through controlled camera exposure) are captured during slower motion to reconstruct grayscale images for high-quality training and optimization of event-based 3D Gaussian splatting (3DGS). Our framework supports three modes: high-quality reconstruction using exposure events, fast reconstruction relying on motion events, and balanced hybrid optimizing with initial exposure events followed by high-speed motion events. On the EventNeRF dataset, we demonstrate that exposure events significantly improve fine detail reconstruction compared to motion events and outperform frame-based cameras under challenging conditions such as low illumination and overexposure. Furthermore, we introduce EME-3D, a real-world 3D dataset with exposure events, motion events, camera calibration parameters, and sparse point clouds. Our method achieves faster and higher-quality reconstruction than event-based NeRF and is more cost-effective than methods combining event and RGB data. By combining motion and exposure events, E-3DGS sets a new benchmark for event-based 3D reconstruction with robust performance in challenging conditions and lower hardware demands. The source code and dataset are available on GitHub.
Meta-Learning-Based Safety-Critical Control in Multi-Obstacles Environments

Yu Zhang, Long Wen, Yuhong Huang, Siming Sun, Zhenshan Bing^*, Wei He, and Alois Knoll

IEEE Transactions on Automation Science and Engineering May 2025
IEEE RAL

Tree-Based Grafting Approach for Bidirectional Motion Planning With Local Subsets Optimization

Liding Zhang, Yao Ling, Zhenshan Bing^*, Fan Wu, Sami Haddadin, and Alois Knoll

IEEE Robotics and Automation Letters May 2025
IEEE TMech

Long-Horizon Language-Conditioned Imitation Learning for Robotic Manipulation

Xiangtong Yao, Tobias Blei, Yuan Meng, Yu Zhang, Hongkuan Zhou, Zhenshan Bing^*, Kai Huang, Fuchun Sun, and Alois Knoll

IEEE/ASME Transactions on Mechatronics May 2025
Motion planning for robotics: A review for sampling-based planners

Liding Zhang, Kuanqi Cai, Zewei Sun, Zhenshan Bing^*, Chaoqun Wang, Luis Figueredo, Sami Haddadin, and Alois Knoll

Biomimetic Intelligence and Robotics May 2025

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Recent advancements in robotics have transformed industries such as manufacturing, logistics, surgery, and planetary exploration. A key challenge is developing efficient motion planning algorithms that allow robots to navigate complex environments while avoiding collisions and optimizing metrics like path length, sweep area, execution time, and energy consumption. Among the available algorithms, sampling-based methods have gained the most traction in both research and industry due to their ability to handle complex environments, explore free space, and offer probabilistic completeness along with other formal guarantees. Despite their widespread application, significant challenges still remain. To advance future planning algorithms, it is essential to review the current state-of-the-art solutions and their limitations. In this context, this work aims to shed light on these challenges and assess the development and applicability of sampling-based methods. Furthermore, we aim to provide an in-depth analysis of the design and evaluation of ten of the most popular planners across various scenarios. Our findings highlight the strides made in sampling-based methods while underscoring persistent challenges. This work offers an overview of the important ongoing research in robotic motion planning.
Dynamic event-triggered sliding mode control of networked switched systems with imperfect transmissions

Chunlian Wang, Tiedong Ma, Zhenshan Bing, Alois Knoll, and Xiaojie Su

International Journal of Robust and Nonlinear Control May 2025

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Abstract As a significant type of dynamic systems, switched systems have been successfully applied to the construction of many engineering fields. Recently, the studies of networked switched control systems have received a lot of attention. However, the intervention of the network often causes the delay or packet loss during data transmission, resulting in incomplete data transmission. The paper studies a sliding mode observer design for above-mentioned systems with external disturbance under an imperfect transmission communication link. By constructing a dynamic event generator, a dynamic event-triggered mechanism between the event-generator and controller is developed to save additional network bandwidth and improve system performance. A desirable sliding surface function is built to solve the complexity of system analysis problems caused by network-induced delay and packet-dropout-induced imperfect transmission. Then, a corresponding event-based integral-type sliding mode observer is constructed to estimate inaccessible system states, guaranteeing the reachability of the sliding surface. Based on the augmented system dynamics, sufficient conditions are derived to guarantee exponent stability with a weighted \ \mathcalL_2 \\-\ \mathcalL_∞ \ performance level. Finally, two simulations are presented to demonstrate the feasibility and effectiveness of the proposed method.
T-ASE

Adaptive Oscillation-Suppression Control for Distributed Nonholonomic Vehicle Safe Formation With Nested Input Saturation

Zhi Zheng, Tao Jiang, Jianxiang Wang, Xiaojie Su, Jiangshuai Huang, Zhenshan Bing, and Alois Knoll

IEEE Transactions on Automation Science and Engineering May 2025
JFR

AutoMine: A Multimodal Dataset for Robot Navigation in Open-Pit Mines

Yuchen Li, Siyu Teng, Junhui Wang, Yunfeng Ai, Bin Tian, Zhe Xuanyuan, Zhenshan Bing, Alois C. Knoll, Fei-Yue Wang, and Long Chen

Journal of Field Robotics May 2025

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ABSTRACT In the past decade, autonomous driving has witnessed significant advancements, largely attributable to the evolution of precise algorithms and efficient computing platforms. Nevertheless, the open-pit mine, a typical scenario within closed-field environments, has garnered limited attention in autonomous driving, primarily owing to the scarcity of data and experimental benchmarks. This work presents original data collected from five platforms, comprising one passenger vehicle, three wide-body trucks, and one mining truck, across eight different mining sites. We provide a comprehensive elucidation of platform types, sensors, calibration methodologies, synchronization techniques, data collection approaches, and a thorough analysis of the data characteristics. In addition, we offer a detailed benchmark comparison of short and long odometry and navigation performance across multiple vehicles in open-pit mines. With comprehensive data characteristics, experimental performance evaluations, and thorough analysis, we believe that this work establishes a robust research foundation for navigation and fusion methods in open-pit mines, thereby constituting a significant contribution to the autonomous driving and field robotics communities.

2024

Context-Based Meta-Reinforcement Learning With Bayesian Nonparametric Models

Zhenshan Bing, Yuqi Yun, Kai Huang, and Alois Knoll

IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) May 2024

Bib

@article{10495171,
  author = {<b>Bing</b>, <b>Zhenshan</b> and Yun, Yuqi and Huang, Kai and Knoll, Alois},
  journal = {<b>IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)</b>},
  title = {Context-Based Meta-Reinforcement Learning With Bayesian Nonparametric Models},
  year = {2024},
  volume = {46},
  number = {10},
  pages = {6948-6965},
  keywords = {Task analysis;Clustering algorithms;Training;Optimization;Adaptation models;Markov decision processes;Inference algorithms;Meta-reinforcement learning;task inference;task adaptation;Bayesian nonparametric model;robotic control},
  doi = {10.1109/TPAMI.2024.3386780},
}

AFM

Humidity Stable Thermoelectric Hybrid Materials Toward a Self-Powered Triple Sensing System

Suo Tu, Ting Tian, Tianxiao Xiao, Xiangtong Yao, Sicong Shen, Yansong Wu, Yinlong Liu, Zhenshan Bing, Kai Huang, Alois Knoll, and others

Advanced Functional Materials May 2024
IEEE

UnstrPrompt: Large language model prompt for driving in unstructured scenarios

Yuchen Li, Luxi Li, Zizhang Wu, Zhenshan Bing, Zhe Xuanyuan, Alois Christian Knoll, and Long Chen

IEEE Journal of Radio Frequency Identification May 2024
TII

Efficient Stereo Matching Using Swin Transformer and Multilevel Feature Consistency in Autonomous Mobile Systems

Xiaojie Su, Shimin Liu, Rui Li, Zhenshan Bing, and Alois Knoll

IEEE Transactions on Industrial Informatics May 2024
TIV

Miningllm: Towards mining 5.0 via large language models in autonomous driving and smart mining

Yuchen Li, Luxi Li, Zizhang Wu, Zhenshan Bing, Yunfeng Ai, Bin Tian, Zhe Xuanyuan, Alois Christian Knoll, and Long Chen

IEEE Transactions on Intelligent Vehicles May 2024
Nano Energy

A multi-dimensional tactile perception system based on triboelectric sensors: towards intelligent sorting without seeing

Tianxiao Xiao, Zhenshan Bing^#, Yansong Wu, Wei Chen, Ziming Zhou, Fan Fang, Suzhe Liang, Renjun Guo, Suo Tu, Guangjiu Pan, and others

Nano Energy May 2024
ICRA

Contact Energy Based Hindsight Experience Prioritization

Erdi Sayar, Zhenshan Bing, Carlo D’Eramo, Ozgur S Oguz, and Alois Knoll

In 2024 IEEE International Conference on Robotics and Automation (ICRA) May 2024
ICRA

1 khz behavior tree for self-adaptable tactile insertion

Yansong Wu, Fan Wu, Lingyun Chen, Kejia Chen, Samuel Schneider, Lars Johannsmeier, Zhenshan Bing, Fares J Abu-Dakka, Alois Knoll, and Sami Haddadin

In 2024 IEEE International Conference on Robotics and Automation (ICRA) May 2024
TSMC

Event-Triggered Fuzzy Yaw Control of Six-Wheel Skid-Steer Vehicles

Yaoyao Tan, Xiaojie Su, Yufeng Tian, Xiaoyu Ma, Zhenshan Bing, and Alois Knoll

IEEE Transactions on Systems, Man, and Cybernetics: Systems May 2024
Modeling Grid Cell Distortions with a Grid Cell Calibration Mechanism

Daniel Strauß, Zhenshan Bing^#, Genghang Zhuang, Kai Huang, and Alois Knoll

Cyborg and Bionic Systems May 2024
Language-conditioned imitation learning with base skill priors under unstructured data

Hongkuan Zhou, Zhenshan Bing^*, Xiangtong Yao, Xiaojie Su, Chenguang Yang, Kai Huang, and Alois Knoll

IEEE Robotics and Automation Letters May 2024
Modulating emotional states of rats through a rat-like robot with learned interaction patterns

Guanglu Jia, Zhe Chen, Yulai Zhang, Zhenshan Bing, Zhenzhen Quan, Xuechao Chen, Alois Knoll, Qiang Huang, and Qing Shi

Nature Machine Intelligence May 2024

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Robots, integrated into biological systems as sociable partners, offer promising advancement in the mechanistic understanding of social behaviours. These biohybrid systems bring controllability to help elucidate the underlying biological intelligence previously inaccessible through traditional techniques. However, state-of-the-art interactive robots still struggle to convey multilevel, heterogeneous information within biological systems, making it challenging to mediate the complex interaction process effectively. Here we propose an autonomous, interactive rat-like robot that can engage with freely behaving rats by learning from the anatomical structure, dynamic motions and social interaction of rats. Imitation learning based on animal demonstration enables the robot with subtle templates of social behaviour, allowing it to capture the attention of rats and significantly arouse their interest. It also integrates visual perception, target tracking and behavioural decisions to substantially augment the interaction efficiency. We demonstrate that the robot can interact with rats for a continuous half-hour. Moreover, the robot can modulate the emotional states of rats through different interaction patterns during robot–rat social interaction. These results attest that the proposed interactive robot, with its long-term and repetitive interaction capabilities, overcomes the limitations of natural social interaction within biological systems. Such biohybrid systems capable of modulating the internal states of organisms may open the door to comprehending the ‘social’ interactions between humans and artificial intelligence.
Online Triangular Constraint Calibration for LiDAR and Cameras in Open-Pit Mines

Yuchen Li, Luxi Li, Siyu Teng, Zhenshan Bing, Alois Christian Knoll, Zhe Xuanyuan, and Long Chen

IEEE Transactions on Field Robotics May 2024
IROS

Ontology Based AI Planning and Scheduling for Robotic Assembly

Jingyun Zhao, Birgit Vogel-Heuser, Jicong Ao, Yansong Wu, Liding Zhang, Fandi Hartl, Dominik Hujo, Zhenshan Bing, Fan Wu, Alois Knoll, Sami Haddadin, Bernd Vojanec, Timo Markert, and André Kraft

In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) May 2024
IROS

IMU Based Pose Reconstruction and Closed-loop Control for Soft Robotic Arms

Guanran Pei, Francesco Stella, Omar Meebed, Zhenshan Bing, Cosimo Della Santina, and Josie Hughes

In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) May 2024
IROS

Flexible Informed Trees (FIT*): Adaptive Batch-Size Approach in Informed Sampling-Based Path Planning

Liding Zhang, Zhenshan Bing^*, Kejia Chen, Lingyun Chen, Kuanqi Cai, Yu Zhang, Fan Wu, Peter Krumbholz, Zhilin Yuan, Sami Haddadin, and Alois Knoll

In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) May 2024
IROS

Elliptical K-Nearest Neighbors - Path Optimization via Coulomb’s Law and Invalid Vertices in C-space Obstacles

Liding Zhang, Zhenshan Bing^*, Yu Zhang, Kuanqi Cai, Lingyun Chen, Fan Wu, Sami Haddadin, and Alois Knoll

In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) May 2024
IROS

Online Efficient Safety-Critical Control for Mobile Robots in Unknown Dynamic Multi-Obstacle Environments

Yu Zhang, Guangyao Tian, Long Wen, Xiangtong Yao, Liding Zhang, Zhenshan Bing^*, Wei He, and Alois Knoll

In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) May 2024

2023

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

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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 = {}, }

ICRA

Meta-Reinforcement Learning via Language Instructions.

Zhenshan Bing, Alexander Koch, Xiangtong Yao, Kai Huang, and Alois Knoll

In 2023 IEEE International Conference on Robotics and Automation(ICRA) May 2023

Abs Bib

@inproceedings{bing2023icra,
  editor = {},
  author = {<b>Bing</b>, <b>Zhenshan</b> and Koch, Alexander and Yao, Xiangtong and Huang, Kai and Knoll, Alois},
  title = {Meta-Reinforcement Learning via Language Instructions.},
  booktitle = {2023 IEEE International Conference on Robotics and Automation(ICRA)},
  year = {2023},
  month = may,
  volume = {},
  publisher = {},
  organization = {},
  series = {},
  number = {},
  pages = {},
  isbn = {},
  doi = {},
  language = {},
  keywords = {},
  note = {}
}

IROS

Contact-aware Shaping and Maintenance of Deformable Linear Objects With Fixtures

Kejia Chen, Zhenshan Bing, Fan Wu, Yuan Meng, Andre Kraft, Sami Haddadin, and Alois Knoll

In 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023) 2023

Abs Bib

@inproceedings{chen,
  editor = {},
  author = {Chen, Kejia and <b>Bing</b>, <b>Zhenshan</b> and Wu, Fan and Meng, Yuan and Kraft, Andre and Haddadin, Sami and Knoll, Alois},
  title = {Contact-aware Shaping and Maintenance of Deformable Linear Objects With Fixtures},
  booktitle = {2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023)},
  year = {2023},
  month = {},
  volume = {},
  publisher = {},
  organization = {},
  series = {},
  number = {},
  pages = {},
  isbn = {},
  doi = {},
  language = {},
  keywords = {},
  note = {}
}

TNNLS

Meta-Reinforcement Learning in Nonstationary and Nonparametric Environments

Zhenshan Bing, Lukas Knak, Long Cheng, Fabrice O. Morin, Kai Huang, and Alois Knoll

IEEE Transactions on Neural Networks and Learning Systems 2023

Abs Bib

@article{tigr,
  author = {<b>Bing</b>, <b>Zhenshan</b> and Knak, Lukas and Cheng, Long and Morin, Fabrice O. and Huang, Kai and Knoll, Alois},
  title = {Meta-Reinforcement Learning in Nonstationary and Nonparametric Environments},
  journal = {IEEE Transactions on Neural Networks and Learning Systems},
  year = {2023},
  volume = {},
  month = {},
  number = {},
  pages = {1-15},
  issn = {},
  doi = {10.1109/tnnls.2023.3270298},
  language = {},
  keywords = {},
  note = {}
}

JAI

Towards cognitive navigation: A biologically inspired calibration mechanism for the head direction cell network

Zhenshan Bing, Dominik Nitschke, Genghang Zhuang, Kai Huang, and Alois Knoll

Journal of Automation and Intelligence 2023

Abs Bib PDF Code Video

@article{jai,
  author = {<b>Bing</b>, <b>Zhenshan</b> and Nitschke, Dominik and Zhuang, Genghang and Huang, Kai and Knoll, Alois},
  title = {Towards cognitive navigation: A biologically inspired calibration mechanism for the head direction cell network},
  journal = {Journal of Automation and Intelligence},
  year = {2023},
  volume = {2},
  month = {},
  number = {1},
  pages = {31-41},
  issn = {},
  doi = {10.1016/j.jai.2023.100020},
  language = {},
  keywords = {},
  note = {},
  video = {https://videoviewsite.wixsite.com/hdc-calibration}
}

2022

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 = {<b>Bing</b>, <b>Zhenshan</b> 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},
}

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 = {<b>Bing</b>, <b>Zhenshan</b> and Lerch, David and Huang, Kai and Knoll, Alois},
  journal = {<b>IEEE Transactions on Pattern Analysis and Machine Intelligence</b>},
  year = {2022},
  publisher = {IEEE},
  video = {},
}

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
TFS

Reduced-Model Based Fault Detector and Controller Design for Discrete-Time Switching Fuzzy Systems

Yaoyao Tan, Xiaojie Su, Zhenshan Bing, Xiaokui Yang, and Alois Knoll

IEEE Transactions on Fuzzy Systems 2022
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
IEEE IV

A Biologically-Inspired Global Localization System for Mobile Robots Using LiDAR Sensor

Genghang Zhuang, Carlo Cagnetta, Zhenshan Bing, Hu Cao, Xinyi Li, Kai Huang, and Alois Knoll

In 2022 IEEE Intelligent Vehicles Symposium (IV) 2022

2021

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
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
Spiking Neural Network for Fourier Transform and Object Detection for Automotive Radar

Javier López-Randulfe, Tobias Duswald, Zhenshan Bing, and Alois Knoll

Frontiers in Neurorobotics 2021

Abs

The development of advanced autonomous driving applications is hindered by the complex temporal structure of sensory data, as well as by the limited computational and energy resources of their on-board systems. Currently, neuromorphic engineering is a rapidly growing field that aims to design information processing systems similar to the human brain by leveraging novel algorithms based on spiking neural networks (SNNs). These systems are well-suited to recognize temporal patterns in data while maintaining a low energy consumption and offering highly parallel architectures for fast computation. However, the lack of effective algorithms for SNNs impedes their wide usage in mobile robot applications. This paper addresses the problem of radar signal processing by introducing a novel SNN that substitutes the discrete Fourier transform and constant false-alarm rate algorithm for raw radar data, where the weights and architecture of the SNN are derived from the original algorithms. We demonstrate that our proposed SNN can achieve competitive results compared to that of the original algorithms in simulated driving scenarios while retaining its spike-based nature.
arXiv

A Biologically Inspired Simultaneous Localization and Mapping System Based on LiDAR Sensor

Genghang Zhuang, Zhenshan Bing, Jiaxi Zhao, Ning Li, Yuhong Huang, Kai Huang, and Alois Knoll

arXiv preprint arXiv:2109.12910 2021
arXiv

Meta-Reinforcement Learning in Broad and Non-Parametric Environments

Zhenshan Bing, Lukas Knak, Fabrice Oliver Robin, Kai Huang, and Alois Knoll

arXiv preprint arXiv:2108.03718 2021
IJARS

Design and evaluation of the target spray platform

Mingchuan Zhou, Huanyu Jiang, Zhenshan Bing, Hang Su, and Alois Knoll

International Journal of Advanced Robotic Systems 2021

Abs

The target-oriented spraying is an effective way to minimize chemical input in agricultural production and it is important to actualizing sustainable agriculture. This article focused on the design and optimization of the target spray platform. The spray platform consists of three parts: the vision system, the spray system, and the moving platform. All components were effectively connected and preliminarily evaluated especially regarding the response time and target spray accuracy. A simulation system was built considering the real crop geometric shape and nozzle spray pattern using the Monte Carlo method. The spray simulator was used to optimize the spray height objective function of spray deposition rate and coefficient of variance for the spray deposition. The spray process can be easily visualized and compared for different spray parameters. The indoor experiment setup was built to test and optimize the parameters of the overall system, which is flexible to verify the system. Afterward, the outdoor tractor-based equipment was designed to test the system for the field environment. The experimental result showed that 80% of the crop had a spray deviation from 0 mm to 28 mm and the proposed spraying system can reduce 46.8% usage of the chemical compared to the uniform spray method.

2020

Neural Networks

Energy-efficient and damage-recovery slithering gait design for a snake-like robot based on reinforcement learning and inverse reinforcement learning

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

Neural Networks 2020
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.
IROS

Target Tracking Control of a Wheel-less Snake Robot Based on a Supervised Multi-layered SNN

Zhuangyi Jiang, Richard Otto, Zhenshan Bing, Kai Huang, and Alois Knoll

In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020
Frontiers

Perception-Action Coupling Target Tracking Control for a Snake Robot via Reinforcement Learning

Zhenshan Bing, Christian Lemke, Fabric O. Morin, Zhuangyi Jiang, Long Cheng, Kai Huang, and Alois Knoll

Frontiers in Neurorobotics 2020

Abs

Visual-guided locomotion for snake-like robots is a challenging task, since it involves not only the complex body undulation with many joints, but also a joint pipeline that connects the vision and the locomotion. Meanwhile, it is usually difficult to jointly coordinate these two separate sub-tasks as this requires time-consuming and trial-and-error tuning. In this paper, we introduce a novel approach for solving target tracking tasks for a snake-like robot as a whole using a model-free reinforcement learning (RL) algorithm. This RL-based controller directly maps the visual observations to the joint positions of the snake-like robot in an end-to-end fashion instead of dividing the process into a series of sub-tasks. With a novel customized reward function, our RL controller is trained in a dynamically changing track scenario. The controller is evaluated in four different tracking scenarios and the results show excellent adaptive locomotion ability to the unpredictable behavior of the target. Meanwhile, the results also prove that the RL-based controller outperforms the traditional model-based controller in terms of tracking accuracy.

2019

Toward Brain-Inspired Learning With the Neuromorphic Snake-Like Robot and the Neurorobotic Platform

Guang Chen, Zhenshan Bing, Florian Röhrbein, Jörg Conradt, Kai Huang, Long Cheng, Zhuangyi Jiang, and Alois Knoll

IEEE Transactions on Cognitive and Developmental Systems 2019
End to End Learning of a Multi-Layered Snn Based on R-Stdp for a Target Tracking Snake-Like Robot

Zhenshan Bing, Zhuangyi Jiang, Long Cheng, Caixia Cai, Kai Huang, and Alois Knoll

In 2019 International Conference on Robotics and Automation (ICRA) 2019
Supervised Learning in SNN via Reward-Modulated Spike-Timing-Dependent Plasticity for a Target Reaching Vehicle

Zhenshan Bing, Ivan Baumann, Zhuangyi Jiang, Kai Huang, Caixia Cai, and Alois Knoll

Frontiers in Neurorobotics 2019

Abs

Spiking neural networks (SNNs) offer many advantages over traditional artificial neural networks (ANNs) such as biological plausibility, fast information processing, and energy efficiency. Although SNNs have been used to solve a variety of control tasks using the Spike-Timing-Dependent Plasticity (STDP) learning rule, existing solutions usually involve hard-coded network architectures solving specific tasks rather than solving different kinds of tasks generally. This results in neglecting one of the biggest advantages of ANNs, i.e., being general-purpose and easy-to-use due to their simple network architecture, which usually consists of an input layer, one or multiple hidden layers and an output layer. This paper addresses the problem by introducing an end-to-end learning approach of spiking neural networks constructed with one hidden layer and reward-modulated Spike-Timing-Dependent Plasticity (R-STDP) synapses in an all-to-all fashion. We use the supervised reward-modulated Spike-Timing-Dependent-Plasticity learning rule to train two different SNN-based sub-controllers to replicate a desired obstacle avoiding and goal approaching behavior, provided by pre-generated datasets. Together they make up a target-reaching controller, which is used to control a simulated mobile robot to reach a target area while avoiding obstacles in its path. We demonstrate the performance and effectiveness of our trained SNNs to achieve target reaching tasks in different unknown scenarios.
Mixed Frame Event-Driven Fast Pedestrian Detection

Zhuangyi Jiang, Pengfei Xia, Kai Huang, Walter Stechele, Guang Chen, Zhenshan Bing, and Alois Knoll

In 2019 International Conference on Robotics and Automation (ICRA) 2019
Retina-Based Pipe-Like Object Tracking Implemented Through Spiking Neural Network on a Snake Robot

Zhuangyi Jiang, Zhenshan Bing, Kai Huang, and Alois Knoll

Frontiers in Neurorobotics 2019

Abs

Vision based-target tracking ability is crucial to bio-inspired snake robots for exploring unknown environments. However, it is difficult for the traditional vision modules of snake robots to overcome the image blur resulting from periodic swings. A promising approach is to use a neuromorphic vision sensor (NVS), which mimics the biological retina to detect a target at a higher temporal frequency and in a wider dynamic range. In this study, an NVS and a spiking neural network (SNN) were performed on a snake robot for the first time to achieve pipe-like object tracking. An SNN based on Hough Transform was designed to detect a target with an asynchronous event stream fed by the NVS. Combining the state of snake motion analyzed by the joint position sensors, a tracking framework was proposed. The experimental results obtained from the simulator demonstrated the validity of our framework and the autonomous locomotion ability of our snake robot. Comparing the performances of the SNN model on CPUs and on GPUs, respectively, the SNN model showed the best performance on a GPU under a simplified and synchronous update rule while it possessed higher precision on a CPU in an asynchronous way.
Thesis

Biological-inspired Hierarchical Control of a Snake-like Robot for Autonomous Locomotion

Zhenshan Bing

2019
Peak Temperature Minimization for Hard Real-Time Systems Using DVS and DPM

Mingchuan Zhou, Long Cheng, Manuel Dell’Antonio, Xiebing Wang, Zhenshan Bing, M Ali Nasseri, Kai Huang, and Alois Knoll

Journal of Circuits, Systems and Computers 2019
DVS

Neuromorphic Vision Datasets for Pedestrian Detection, Action Recognition, and Fall Detection

Shu Miao, Guang Chen, Xiangyu Ning, Yang Zi, Kejia Ren, Zhenshan Bing, and Alois Knoll

Frontiers in Neurorobotics 2019
IJCAI

Energy-Efficient Slithering Gait Exploration for a Snake-Like Robot Based on Reinforcement Learning

Zhenshan Bing, Christian Lemke, Zhuangyi Jiang, Kai Huang, and Alois Knoll

In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI-19 Jul 2019

2018

End to End Learning of Spiking Neural Network Based on R-STDP for a Lane Keeping Vehicle

Zhenshan Bing, Claus Meschede, Kai Huang, Guang Chen, Florian Rohrbein, Mahmoud Akl, and Alois Knoll

In 2018 IEEE International Conference on Robotics and Automation (ICRA) Jul 2018

Code
survey

A Survey of Robotics Control Based on Learning-Inspired Spiking Neural Networks

Zhenshan Bing, Claus Meschede, Florian Röhrbein, Kai Huang, and Alois C. Knoll

Frontiers in Neurorobotics Jul 2018

Abs

Biological intelligence processes information using impulses or spikes, which makes those living creatures able to perceive and act in the real world exceptionally well and outperform state-of-the-art robots in almost every aspect of life. To make up the deficit, emerging hardware technologies and software knowledge in the fields of neuroscience, electronics, and computer science have made it possible to design biologically realistic robots controlled by spiking neural networks (SNNs), inspired by the mechanism of brains. However, a comprehensive review on controlling robots based on SNNs is still missing. In this paper, we survey the developments of the past decade in the field of spiking neural networks for control tasks, with particular focus on the fast emerging robotics-related applications. We first highlight the primary impetuses of SNN-based robotics tasks in terms of speed, energy efficiency, and computation capabilities. We then classify those SNN-based robotic applications according to different learning rules and explicate those learning rules with their corresponding robotic applications. We also briefly present some existing platforms that offer an interaction between SNNs and robotics simulations for exploration and exploitation. Finally, we conclude our survey with a forecast of future challenges and some associated potential research topics in terms of controlling robots based on SNNs.
IEEE-HFR

Teleoperation Snake Robots with Panorama Vision

Long Chen, Xuan Yuwen, Zhenshan Bing, Kai Huang, and Jun Wang

In 2018 11th International Workshop on Human Friendly Robotics (HFR) Jul 2018
DVS

Neuromorphic vision based multivehicle detection and tracking for intelligent transportation system

Guang Chen, Hu Cao, Muhammad Aafaque, Jieneng Chen, Canbo Ye, Florian Röhrbein, Jörg Conradt, Kai Chen, Zhenshan Bing, Xingbo Liu, and others

Journal of advanced transportation Jul 2018

2017

Towards autonomous locomotion: CPG-based control of smooth 3D slithering gait transition of a snake-like robot

Zhenshan Bing, Long Cheng, Guang Chen, Florian Röhrbein, Kai Huang, and Alois Knoll

Bioinspiration &amp\mathsemicolon Biomimetics Apr 2017

Abs

Snake-like robots with 3D locomotion ability have significant advantages of adaptive travelling in diverse complex terrain over traditional legged or wheeled mobile robots. Despite numerous developed gaits, these snake-like robots suffer from unsmooth gait transitions by changing the locomotion speed, direction, and body shape, which would potentially cause undesired movement and abnormal torque. Hence, there exists a knowledge gap for snake-like robots to achieve autonomous locomotion. To address this problem, this paper presents the smooth slithering gait transition control based on a lightweight central pattern generator (CPG) model for snake-like robots. First, based on the convergence behavior of the gradient system, a lightweight CPG model with fast computing time was designed and compared with other widely adopted CPG models. Then, by reshaping the body into a more stable geometry, the slithering gait was modified, and studied based on the proposed CPG model, including the gait transition of locomotion speed, moving direction, and body shape. In contrast to sinusoid-based method, extensive simulations and prototype experiments finally demonstrated that smooth slithering gait transition can be effectively achieved using the proposed CPG-based control method without generating undesired locomotion and abnormal torque.
CPG-based control of smooth transition for body shape and locomotion speed of a snake-like robot

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

In 2017 IEEE International Conference on Robotics and Automation (ICRA) Apr 2017
Slope angle estimation based on multi-sensor fusion for a snake-like robot

Zhenshan Bing, Long Cheng, Alois Knoll, Anyang Zhong, Kai Huang, and Feihu Zhang

In 2017 20th International Conference on Information Fusion (Fusion) Apr 2017
Towards autonomous locomotion: Slithering gait design of a snake-like robot for target observation and tracking

Zhenshan Bing, Long Cheng, Kai Huang, Zhuangyi Jiang, Guang Chen, Florian Röhrbein, and Alois Knoll

In 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Apr 2017
Springer

Online multi-object tracking-by-clustering for intelligent transportation system with neuromorphic vision sensor

Gereon Hinz, Guang Chen, Muhammad Aafaque, Florian Röhrbein, Jörg Conradt, Zhenshan Bing, Zhongnan Qu, Walter Stechele, and Alois Knoll

In Joint German/Austrian Conference on Artificial Intelligence (Künstliche Intelligenz) Apr 2017
ICNIP

Event-based target tracking control for a snake robot using a dynamic vision sensor

Zhuangyi Jiang, Zhenshan Bing, Kai Huang, Guang Chen, Long Cheng, and Alois Knoll

In International Conference on Neural Information Processing Apr 2017
ROBIO

Snake-like robot climbs inside different pipes

Shanshan Xiao, Zhenshan Bing, Kai Huang, and Yuhong Huang

In 2017 IEEE International Conference on Robotics and Biomimetics (ROBIO) Apr 2017