Deep Feature Representation Based Imitation Learning for Autonomous Helicopter Aerobatics

Chen, Shaofeng; Cao, Yang; Kang, Yu; Li, Pengfei; Sun, Bingyu

doi:10.1109/tai.2021.3053511

Cited by 16 publications

(2 citation statements)

References 20 publications

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“…By integrating multiple layers of abstraction, it allows hierarchical data representation [76], [77]. This multi-layered approach enhances the analytical performance for numerous large-scale data processing tasks [78], [79]. Essentially, deep learning is an advanced non-linear data processing technique grounded in representation learning and pattern analysis.…”

Section: The Advent Of Deep Learningmentioning

confidence: 99%

A Comprehensive Review on Deep Learning Assisted Computer Vision Techniques for Smart Greenhouse Agriculture

Akbar,

Kamarulzaman,

Muzahid

et al. 2024

IEEE Access

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With the escalating global challenges of food security and resource sustainability, innovative solutions like deep learning and computer vision are transforming agricultural practices by enabling datadriven decision-making. This paper provides a focused review of recent advancements in deep learningenabled computer vision techniques tailored specifically for greenhouse environments. First, deep learning and computer vision fundamentals are briefly introduced. Over 100 studies from 2020 to date are then comprehensively reviewed in which these technologies were applied within greenhouses for growth monitoring, disease detection, yield estimation, and other tasks. The techniques, datasets, models, and overall performance results reported in the literature are analyzed. Tables and figures showcase real-world implementations and results synthesized from current research. Key challenges are also outlined related to aspects like model adaptability, lack of sufficient labeled greenhouse data, computational constraints, the need for multi-modal sensor fusion, and other areas needing further investigation. Future trends and prospects are discussed to provide guidance for researchers exploring computer vision in the niche greenhouse domain. By condensing prior work and elucidating the state-of-the-art, this timely review aims to promote continued progress in smart greenhouse agriculture. The focused analysis, specifically on greenhouse environments, fills a gap compared to previous agricultural surveys. Overall, this paper highlights the immense potential of computer vision and deep learning in driving the emergence of data-driven, smart greenhouse farming worldwide.

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Section: The Advent Of Deep Learningmentioning

confidence: 99%

A Comprehensive Review on Deep Learning Assisted Computer Vision Techniques for Smart Greenhouse Agriculture

Akbar,

Kamarulzaman,

Muzahid

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Lastly, different experts have diverse policies due to latent factors such as changes in the external environment, and skills and habits of different experts. Therefore, the learned policies need to not only imitate the behavior of experts, but also account for changes in these latent factors (Chen et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

DUEL: Depth visUal Ego-motion Learning for autonomous robot obstacle avoidance

Wang,

Zhang,

Chi

et al. 2023

The International Journal of Robotics Research

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Reliable obstacle avoidance, which is essential for safe autonomous robot interaction with the real world, raises various challenges such as difficulties with obstacle perception and latent factor cognition impacting multi-modal obstacle avoidance. In this paper, we propose a Depth visUal Ego-motion Learning (DUEL) model, consisting of a cognitive generation network, a policy decision network and a potential partition network, to learn autonomous obstacle avoidance from expert policies. The DUEL model takes advantage of binocular vision to perceive scene depth. This serves as the input to the cognitive generation network which generates obstacle avoidance policies by maximizing its causal entropy. The policy decision network then optimizes the generation of the policies referring to expert policies. The generated obstacle avoidance policies are simultaneously transferred to the potential partition network to capture the latent factors contained within expert policies and perform multi-modal obstacle avoidance. These three core networks iteratively optimize the multi-modal policies relying on causal entropy and mutual information theorems, which are proven theoretically. Experimental comparisons with state-of-the-art models on 7 metrics demonstrate the effectiveness of the DUEL model. It achieves the best performance with an average ADE (Average Displacement Error) of 0.29 and average FDE (Final Displacement Error) of 0.55 across five different scenarios. Results show that the DUEL model can maintain an average obstacle avoidance success rate of 97% for both simulated and real world scenarios with multiple obstacles, demonstrating its success at capturing latent factors from expert policies. Our source codes are available at https://github.com/ACoTAI/DUEL .

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Breaking the data barrier: a review of deep learning techniques for democratizing AI with small datasets

Rather,

Kumar,

Gandomi

2024

Artif Intell Rev

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Justifiably, while big data is the primary interest of research and public discourse, it is essential to acknowledge that small data remains prevalent. The same technological and societal forces that generate big datasets also produce a more significant number of small datasets. Contrary to the notion that more data is inherently superior, real-world constraints such as budget limitations and increased analytical complexity present critical challenges. Quality versus quantity trade-offs necessitate strategic decision-making, where small data often leads to quicker, more accurate, and cost-effective insights. Concentrating AI research, particularly in deep learning (DL), on big datasets exacerbates AI inequality, as tech giants such as Meta, Amazon, Apple, Netflix and Google (MAANG) can easily lead AI research due to their access to vast datasets, creating a barrier for small and mid-sized enterprises that lack similar access. This article addresses this imbalance by exploring DL techniques optimized for small datasets, offering a comprehensive review of historic and state-of-the-art DL models developed specifically for small datasets. This study aims to highlight the feasibility and benefits of these approaches, promoting a more inclusive and equitable AI landscape. Through a PRISMA-based literature search, 175+ relevant articles are identified and subsequently analysed based on various attributes, such as publisher, country, utilization of small dataset technique, dataset size, and performance. This article also delves into current DL models and highlights open research problems, offering recommendations for future investigations. Additionally, the article highlights the importance of developing DL models that effectively utilize small datasets, particularly in domains where data acquisition is difficult and expensive.

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Deep Feature Representation Based Imitation Learning for Autonomous Helicopter Aerobatics

Cited by 16 publications

References 20 publications

A Comprehensive Review on Deep Learning Assisted Computer Vision Techniques for Smart Greenhouse Agriculture

A Comprehensive Review on Deep Learning Assisted Computer Vision Techniques for Smart Greenhouse Agriculture

DUEL: Depth visUal Ego-motion Learning for autonomous robot obstacle avoidance

Breaking the data barrier: a review of deep learning techniques for democratizing AI with small datasets

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