Mu Hua is a post-graduate student at the University of Lincoln and working on ULTRACEPT’s work package 1.

University of Lincoln researcher Mu Hua attended and presented at the International Joint Conference on Neural Networks 2021 (IJCNN 2021) which was held from 18th to 22nd July 2021. Although originally scheduled to be held in Shenzhen, China, due to the ongoing international travel disruption caused by Covid-19, the conference was moved online.

IJCNN 2021 is the flagship annual conference of the International Neural Network Society (INNS) – the premiere organisation for individuals interested in a theoretical and computational understanding of the brain and applying that knowledge to develop new and more effective forms of machine intelligence. INNS was formed in 1987 by the leading scientists in the Artificial Neural Networks (ANN) field. The conference promotes all aspects of neural networks theory, analysis and applications.

This year IJCNN received 1183 papers submitted from over 77 different countries. Of these, 1183 papers, 59.3% were accepted. All of them are included in the program as virtual oral presentations. The top ten countries where the submitting authors come from are (in descending order): China, United Sates, India, Brazil, Australia, United Kingdom, Germany, Japan, Italy, Brazil, Japan, Italy and France. The event was attended by more than 1166 participants and featured special sessions, plenary talks, competitions, tutorials, and workshops.

Representing the University of Lincoln, Mu Hua presented his paper Mu Hua, Qinbing Fu, Wenting Duan, Shigang Yue “Investigating Refractoriness in Collision Perception Neural Network”, (IJCNN 2021) with a poster demonstrating that numerical modelling refractory period, a common neuronal phenomenon, can a promising way to enhance the stability of currently LGMD neural network for collision perception.

Abstract

Currently, collision detection methods based on visual cues are still challenged by several factors including ultra-fast approaching velocity and noisy signal. Taking inspiration from nature, though the computational models of lobula giant movement detectors (LGMDs) in locust’s visual pathways have demonstrated positive impacts on addressing these problems, there remains potential for improvement. In this paper, we propose a novel method mimicking neuronal refractoriness, i.e. the refractory period (RP), and further investigate its functionality and efficacy in the classic LGMD neural network model for collision perception. Compared with previous works, the two phases constructing RP, namely the absolute refractory period (ARP) and relative refractory period (RRP) are computationally implemented through a ‘link (L) layer’ located between the photoreceptor and the excitation layers to realise the dynamic characteristic of RP in discrete time domain. The L layer, consisting of local time-varying thresholds, represents a sort of mechanism that allows photoreceptors to be activated individually and selectively by comparing the intensity of each photoreceptor to its corresponding local threshold established by its last output. More specifically, while the local threshold can merely be augmented by larger output, it shrinks exponentially over time. Our experimental outcomes show that, to some extent, the investigated mechanism not only enhances the LGMD model in terms of reliability and stability when faced with ultra-fast approaching objects, but also improves its performance against visual stimuli polluted by Gaussian or Salt-Pepper noise. This research demonstrates the modelling of refractoriness is effective in collision perception neuronal models, and promising to address the aforementioned collision detection challenges.

This paper can be freely accessed on the University of Lincoln Institutional Repository Eprints.

Nikolas Andreakos is a PhD candidate at the University of Lincoln, who is working on developing computational models of associative memory formation and recognition in the mammalian hippocampus.

Recently Nikolas attended the 30th Annual Computational Neuroscience Meeting (CNS*2021). Due to the current travel restrictions, this year’s conference was moved online from 3rd to 7th of July 2021.

The purpose of the Organization for Computational Neurosciences is to create a scientific and educational forum for students, scientists, other professionals, and the general public to learn about, to share, contribute to, and advance the state of knowledge in computational neuroscience.

Computational neuroscience combines mathematical analyses and computer simulations with experimental neuroscience, to develop a principled understanding of the workings of nervous systems and apply it in a wide range of technologies.

The Organization for Computational Neurosciences promotes meetings and courses in computational neuroscience and organizes the Annual CNS Meeting which serves as a forum for young scientists to present their work and to interact with senior leaders in the field.

Poster Presentation

Nikolas presented his research Modelling the effects of perforant path in the recall performance of a CA1 microcircuit with excitatory and inhibitory neurons.

Abstract

From recollecting childhood memories to recalling if we turn off the oven before we left the house, memory defines who we are. Losing it can be very harmful to our survival. Recently we quantitatively investigated the biophysical mechanisms leading to memory recall improvement of a computational CA1 microcircuit model of the hippocampus [1]. In the present study, we investigated the synergistic effects of the EC excitatory input (sensory input) and the CA3 excitatory input (contextual information) on the recall performance of the CA1 microcircuit. Our results showed that when the EC input was exactly the same as the CA3 input then the recall performance of our model was strengthened. When the two inputs were dissimilar (degree similarity: 40% – 0%), then the recall performance was reduced. These results were positively correlated with how many “active cells” represented a memory pattern. When the number of active cells increased and the degree of similarity between the two inputs decreased, then the recall performance of the model was reduced. The latter finding confirms previous results of ours where the number of cells coding a piece of information plays a significant role in the recall performance of our model.

References
1. Andreakos, N., Yue, S. & Cutsuridis, V. Quantitative investigation of memory recall performance of a computational microcircuit model of the hippocampus. Brain Inf 8, 9 (2021). https://doi.org/10.1186/s40708-021-00131-7

The 2021 International Conference on Robotics and Automation (IEEE ICRA 2021) was held in Xi’an, China from 31^st May to 4^th June 2021. As one of the premier and top conferences in the field of robotics and automation, this great event has gathered thousands of excellent researchers from all over the world. Due to the pandemic, the conference was held in a hybrid format, including physical on-site and virtual cloud meetings. Four ULTRACEPT researchers attended this event, 3 in person and 1 online.

Proactive Action Visual Residual Reinforcement Learning for Contact-Rich Tasks Using a Torque-Controlled Robot

Agile Robots researcher Yunlei Shi attended ICRA 2021 online and presented his paper ‘Proactive Action Visual Residual Reinforcement Learning for Contact-Rich Tasks Using a Torque-Controlled Robot’.

Yunlei Shi is a full-time Ph.D. student at the Universität Hamburg and working at project partner Agile Robots, contributing to ULTRACEPT’s Work Package 4. In 2020 he visited Tsinghua University as part of the STEP2DYNA project.

Yunlei presented his conference paper:

Yunlei Shi, Zhaopeng Chen, Hongxu Liu, Sebastian Riedel, Chunhui Gao, Qian Feng, Jun Deng, Jianwei Zhang, “Proactive Action Visual Residual Reinforcement Learning for Contact-Rich Tasks Using a Torque-Controlled Robot”, (ICRA) 2021, Xi’ an, China.

Abstract

Contact-rich manipulation tasks are commonly found in modern manufacturing settings. However, manually designing a robot controller is considered hard for traditional control methods as the controller requires an effective combination of modalities and vastly different characteristics. In this paper, we first consider incorporating operational space visual and haptic information into a reinforcement learning (RL) method to solve the target uncertainty problems in unstructured environments. Moreover, we propose a novel idea of introducing a proactive action to solve a partially observable Markov decision process (POMDP) problem. With these two ideas, our method can either adapt to reasonable variations in unstructured environments or improve the sample efficiency of policy learning. We evaluated our method on a task that involved inserting a random-access memory (RAM) using a torque-controlled robot and tested the success rates of different baselines used in the traditional methods. We proved that our method is robust and can tolerate environmental variations.

More details about this paper can be viewed in this video on the Universität Hamburg’s Technical Aspects of Multimodal Systems (TAMS) YouTube channel.

Yunlei was very happy to attend this fantastic conference with support from the project ULTRACEPT.

A Versatile Vision-Pheromone-Communication Platform for Swarm Robotics

Three researchers from the University of Lincoln; Tian Liu, Xuelong Sun, and Qinbing Fu, attended ICRA 2021 in person to present their co-authored paper, ‘A Versatile Vision-Pheromone-Communication Platform for Swarm Robotics’. 

We three were very happy to physically attend this fantastic conference with the support from the project ULTRACEPT.

We have one co-authored paper that presents our developed vision-pheromone-communication platform which was published in the proceedings of this conference. Tian Liu delivered the presentation which outlined our platform and it attracted some attention of attendees through interesting questions asked by the audience. We think that this event has provided us a great opportunity to raise publicity about our platform for future swarm robotics and social insects studies.

A Versatile Vision-Pheromone-Communication Platform for Swarm Robotics, Tian Liu, Xuelong Sun, Cheng Hu, Qinbing Fu, and Shigang Yue, University of Lincoln

Keywords: Biologically-Inspired Robots, Multi-Robot Systems, Swarm Robotics

Abstract: This paper describes a versatile platform for swarm robotics research. It integrates multiple pheromone communication with a dynamic visual scene along with real-time data transmission and localization of multiple-robots. The platform has been built for inquiries into social insect behavior and bio-robotics. By introducing a new research scheme to coordinate olfactory and visual cues, it not only complements current swarm robotics platforms which focus only on pheromone communications by adding visual interaction but also may fill an important gap in closing the loop from bio-robotics to neuroscience. We have built a controllable dynamic visual environment based on our previously developed ColCOSPhi (a multi-pheromones platform) by enclosing the arena with LED panels and interacting with the micro mobile robots with a visual sensor. In addition, a wireless communication system has been developed to allow the transmission of real-time bi-directional data between multiple micro robot agents and a PC host. A case study combining concepts from the internet of vehicles (IoV) and insect-vision inspired model has been undertaken to verify the applicability of the presented platform and to investigate how complex scenarios can be facilitated by making use of this platform.

We have grasped many interesting ideas and inspirations from colleagues in the robotics field from not only the excellent talks but also high-quality robots’ exhibitions from famed companies in the industry.

On the last day of the conference, we attended a wonderful tour of the Shaanxi History Museum and the Terra-Cotta Warriors, from which we have leaned a lot about the impressive history and culture of Qin dynasty. Further, this also makes us rethink the important role played by science and technology in assisting archaeological excavation and cultural relic protection.

Thanks to the supportive ULTRACEPT project, we really enjoyed the whole event bringing us not only new knowledge about the robotics and history, but enlightening inspirations which will potentially motivate our future researches. In addition, our group’s researching works also have been propagated via this top international conference.