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Reward Prediction Error Prioritisation in Experience Replay: The RPE-PER Method

Reward Prediction Error Prioritisation in Experience Replay:...

引用

2024 Australasian Conference on robotics and automation, ACRA 2024

作者： Yamani, Hoda Xing, Yuning Ong, Lee Violet C. MacDonald, Bruce A. Williams, Henry Centre for Automation and Robotic Engineering Science The University of Auckland New Zealand

Reinforcement Learning algorithms aim to learn optimal control strategies through iterative interactions with an environment. A critical element in this process is the experience replay buffer, which stores past experiences, allowing the algorithm to learn from a diverse range of interactions rather than just the most recent ones. This buffer is especially essential in dynamic environments with limited experiences. However, efficiently selecting high-value experiences to accelerate training remains a challenge. Drawing inspiration from the role of Reward Prediction Error (RPE) in biological systems, we introduce Reward Predictive Error Prioritised Experience Replay (RPE-PER). RPE signals reflect the difference between expected and actual rewards in the brain, driving adaptive behaviour and learning adjustment. Our method employs a critic network, EMCN, that predicts rewards in addition to the Q-values produced by standard critic networks. The discrepancy between these predicted and actual rewards is computed as RPE and utilised as a signal for experience prioritisation. Experimental evaluations across various continuous control tasks demonstrate RPE-PER’s effectiveness in enhancing the learning speed and performance of off-policy actor-critic algorithms compared to baseline approaches. © 2024 Australasian robotics and automation Association. All rights reserved.

关键词： Reinforcement learning

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Developing AI-powered Image Processing Web Application for Plant Monitoring in Smart Farm

Developing AI-powered Image Processing Web Application for P...

引用

2024 Australasian Conference on robotics and automation, ACRA 2024

作者： Kwon, Do Jun Zuo, Kim Lim, JongYoon MacDonald, Bruce A. Ahn, Ho Seok Centre for Automation and Robotic Engineering Science The University of Auckland New Zealand

The growing global food demand, coupled with climate change and resource scarcity, necessitates innovative solutions for efficient farmland management. This paper proposes a smart agriculture system utilising an AI-powered image processing web application to enhance crop management and reduce labour. Our system integrates computer vision AI models to monitor plant growth like tomato currently, including size, ripeness, and disease detection, alongside a web interface displaying plant status. This allows for informed decision-making by collecting, processing, and displaying real-time data on plant growth and environmental conditions. A user survey rated the system’s usability and performance highly, with an average satisfaction of 4.7 out of 5, noting the interface as easy to navigate, beneficial, and visually appealing. By combining sensing technology and artificial intelligence, we show the potential of comprehensive crop monitoring solutions that improve agricultural productivity and sustainability. This approach leads to more efficient and data-driven agricultural practices, which can demonstrate excellent efficiency in the agricultural sector. © 2024 Australasian robotics and automation Association. All rights reserved.

关键词： Smart agriculture

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OrchardDepth: Precise Metric Depth Estimation of Orchard Scene from Monocular Camera Images

OrchardDepth: Precise Metric Depth Estimation of Orchard Sce...

引用

2024 Australasian Conference on robotics and automation, ACRA 2024

作者： Zheng, Zhichao Williams, Henry MacDonald, Bruce A. Centre for Automation and Robotic Engineering Science The University of Auckland New Zealand

Monocular depth estimation is a rudimentary task in robotic perception. Recently, with the development of more accurate and robust neural network models and different types of datasets, monocular depth estimation has significantly improved performance and efficiency. However, most of the research in this area focuses on very concentrated domains. In particular, most of the benchmarks in outdoor scenarios belong to urban environments for the improvement of autonomous driving devices, and these benchmarks have a massive disparity with the orchard/vineyard environment, which is hardly helpful for research in the primary industry. Therefore, we propose OrchardDepth, which fills the gap in the estimation of the metric depth of the monocular camera in the orchard/vineyard environment. In addition, we present a new retraining method to improve the training result by monitoring the consistent regularization between dense depth maps and sparse points. Our method improves the RMSE of depth estimation in the orchard environment from 1.5337 to 0.6738, proving our method’s validation. © 2024 Australasian robotics and automation Association. All rights reserved.

关键词： Orchards

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Deep Learning for Plant Pathogen Detection: Classification from Multispectral Data

Deep Learning for Plant Pathogen Detection: Classification f...

引用

2024 Australasian Conference on robotics and automation, ACRA 2024

作者： Singh, Parambir Zehra, Maryam Gee, Trevor Nejati, Mahla Centre for Automation and Robotic Engineering Science The University of Auckland New Zealand

Armillaria root rot is a fungal disease that significantly impacts cherry crops, often leading to plant death if not detected and eradicated early. With advancements in artificial intelligence (AI), there is growing potential to enhance plant disease detection, thereby optimizing yields in the horticultural industry. Symptoms of Armillaria in cherry trees include white fungal growth in branches and roots, wilting and chlorosis of leaves, and an overall reduction in biomass—symptoms that are either visible to the naked eye or affect the plant’s light reflection and coloration. This study evaluates the effectiveness of machine learning models using both RGB and multispectral images to detect Armillaria disease across its three stages. A ResNet50 model was adapted for four-channel (Red, Green, Red-Edge, NIR) multispectral images, achieving a top-1 validation accuracy of 62.3%. This adaptation involved replacing the standard first convolutional layer with a custom Conv2d layer to accommodate the additional spectral information. In contrast, a YOLOv8 model trained on the same dataset using RGB images attained a top-1 validation accuracy of 89.1%. These results suggest that, for this dataset, RGB imaging may outperform multispectral imaging in detecting Armillaria root rot. However, the integration of AI-driven RGB and spectral imaging techniques could significantly improve early detection and management of Armillaria root rot, potentially reducing crop losses and enhancing yields in cherry cultivation. © 2024 Australasian robotics and automation Association. All rights reserved.

关键词： Fruits

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Pallet Detection And Localisation From Synthetic Data

Pallet Detection And Localisation From Synthetic Data

引用

2024 Australasian Conference on robotics and automation, ACRA 2024

作者： Mueller, Henri Kim, Yechan Gee, Trevor Nejati, Mahla Centre for Automation and Robotic Engineering Science The University of Auckland New Zealand

The global warehousing industry is experiencing rapid growth, with the market size projected to grow at an annual rate of 8.1% from 2024 to 2030 [Grand View Research, 2021]. This expansion has led to a surge in demand for efficient pallet detection and localisation systems. While automation can significantly streamline warehouse operations, the development of such systems often requires extensive manual data annotation, with an average of 35 seconds per image, for a typical computer vision project. This paper presents a novel approach to enhance pallet detection and localisation using purely synthetic data and geometric features derived from their side faces. By implementing a domain randomisation engine in Unity, the need for time-consuming manual annotation is eliminated while achieving high-performance results. The proposed method demonstrates a pallet detection performance of 0.995 mAP50 for single pallets on a real-world dataset. Additionally, an average position accuracy of less than 4.2 cm and an average rotation accuracy of 8.2° were achieved for pallets within a 5-meter range, with the pallet positioned head-on. © 2024 Australasian robotics and automation Association. All rights reserved.

关键词： Pallets

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Deep Reinforcement Learning for Local Path Following of an Autonomous Formula SAE Vehicle

Deep Reinforcement Learning for Local Path Following of an A...

引用

2023 Australasian Conference on robotics and automation, ACRA 2023

作者： Merton, Harvey Delamore, Thomas Stol, Karl Williams, Henry Centre for Automation and Robotic Engineering Science The University of Auckland New Zealand

With the continued introduction of driverless events to Formula:Society of Automotive Engineers (F:SAE) competitions around the world, teams are investigating all aspects of the autonomous vehicle stack. This paper presents the use of Deep Reinforcement Learning (DRL) and Inverse Reinforcement Learning (IRL) to map locally-observed cone positions to a desired steering angle for race track following. Two state-of-the-art algorithms not previously tested in this context: soft actor critic (SAC) and adversarial inverse reinforcement learning (AIRL), are used to train models in a representative simulation. Three novel reward functions for use by RL algorithms in an autonomous racing context are also discussed. Tests performed in simulation and the real world suggest that both algorithms can successfully train models for local path following. Suggestions for future work are presented to allow these models to scale to a full F:SAE vehicle. © 2023 Australasian robotics and automation Association. All rights reserved.

关键词： Reinforcement learning

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Improving Pallet Detection Using Synthetic Data

Improving Pallet Detection Using Synthetic Data

引用

2023 Australasian Conference on robotics and automation, ACRA 2023

作者： Gann, Henry Bull, Josiah Gee, Trevor Nejati, Mahla Centre for Automation and Robotic Engineering Science The University of Auckland New Zealand

The use of synthetic data in machine learning saves a significant amount of time when implementing an effective object detector. However, there is limited research in this domain. This study aims to improve upon previously applied implementations in the task of instance segmentation of pallets in a warehouse environment. This study proposes using synthetically generated domain-randomised data as well as data generated through Unity to achieve this. This study achieved performance improvements on the stacked and racked pallet categories by 69% and 50% mAP50, respectively when being evaluated on real data. Additionally, it was found that there was a considerable impact on the performance of a model when it was evaluated against images in a darker environment, dropping as low as 3% mAP50 when being evaluated on images with an 80% brightness reduction. This study also created a two-stage detector that used YOLOv8 and SAM, but this proved to have unstable performance. The use of domain-randomised data proved to have negligible performance improvements when compared to the Unity-generated data. © 2023 Australasian robotics and automation Association. All rights reserved.

关键词： Pallets

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Does ChatGPT and Whisper Make Humanoid Robots More Relatable?

Does ChatGPT and Whisper Make Humanoid Robots More Relatable...

引用

2023 Australasian Conference on robotics and automation, ACRA 2023

作者： Chen, Xiaohui Luo, Katherine Gee, Trevor Nejati, Mahla Centre for Automation and Robotic Engineering Science The University of Auckland New Zealand

Humanoid robots are designed to be relatable to humans for applications such as customer support and helpdesk services. However, many such systems, including Softbank’s Pepper, fall short because they fail to communicate effectively with humans. The advent of Large Language Models (LLMs) shows the potential to solve the communication barrier for humanoid robotics. This paper outlines the comparison of different Automatic Speech Recognition (ASR) APIs, the integration of Whisper ASR and ChatGPT with the Pepper robot and the evaluation of the system (Pepper-GPT) tested by 15 human users. The comparison result shows that, compared to the Google ASR and Google Cloud ASR, the Whisper ASR performed best as its average Word Error Rate (1.716%) and processing time (2.639 s) are both the lowest. The participants’ usability investigations show that 60% of the participants thought the performance of the Pepper-GPT was"excellent", while the rest rated this system as"good" in the subsequent experiments. It is proved that while some problems still need to be overcome, such as the robot’s multilingual ability and facial tracking capacity, users generally responded positively to the system, feeling like talking to an actual human. © 2023 Australasian robotics and automation Association. All rights reserved.

关键词： Anthropomorphic robots

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Human Motion Mimicking on Pepper Social Robot

Human Motion Mimicking on Pepper Social Robot

引用

2023 Australasian Conference on robotics and automation, ACRA 2023

作者： Tracey, Finn Ying, Cale Yoon Lim, Jong Ahn, Ho Seok Centre for Automation and Robotic Engineering Science The University of Auckland New Zealand

Human-robot interaction is a topic that has been around for a long time, and recent technological advances in social robots will serve an important role in our society. We aim to decrease the interactivity boundary between humans and robots through human motion mimicking to create meaningful motions on robots. Current solutions require large and expensive systems with long setup times and calibration. To overcome these problems, this paper presents a human motion mimicking system which utilizes equipment commonly available and easily accessible. The performance of this system is evaluated on the SoftBank robotics Pepper social robot. Our system achieves mimicking of natural and recognisable human motions and proves motion mimicking can be achieved on lower processing power devices and with minimal cost. © 2023 Australasian robotics and automation Association. All rights reserved.

关键词： Human robot interaction

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Detection of Spider Mites on Labrador Beans through Machine Learning Approaches Using Custom Datasets

Detection of Spider Mites on Labrador Beans through Machine ...

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2023 Australasian Conference on robotics and automation, ACRA 2023

作者： Liu, Violet Chen, Jason Qureshi, Ans Nejati, Mahla Centre for Automation and Robotic Engineering Science New Zealand The University of Auckland New Zealand

Amidst growing food production demands, early plant disease detection is essential to safeguard crops;this study proposes a visual machine learning approach for plant disease detection, harnessing RGB and NIR data collected in real-world conditions through a JAI FS-1600D10GE camera to build an RGBN dataset. A two-stage early plant disease detection model with YOLOv8 and a sequential CNN was used to train on a dataset with partial labels, which showed a 3.6% increase in mAP compared to a single-stage end-to-end segmentation model. The sequential CNN model achieved 90.62% validation accuracy utilising RGBN data. An average of 6.25% validation accuracy increase is found using RGBN in classification compared to RGB using ResNet15 and the sequential CNN models. Further research and dataset improvements are needed to meet food production demands. © 2023 Australasian robotics and automation Association. All rights reserved.

关键词： robotics

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