Zobrazeno 1 - 10
of 474
pro vyhledávání: '"MacDonald, Bruce A"'
Autor:
Valencia, David, Williams, Henry, Xing, Yuning, Gee, Trevor, Liarokapis, Minas, MacDonald, Bruce A.
Reinforcement Learning (RL) has been widely used to solve tasks where the environment consistently provides a dense reward value. However, in real-world scenarios, rewards can often be poorly defined or sparse. Auxiliary signals are indispensable for
Externí odkaz:
http://arxiv.org/abs/2407.21338
CTD4 -- A Deep Continuous Distributional Actor-Critic Agent with a Kalman Fusion of Multiple Critics
Categorical Distributional Reinforcement Learning (CDRL) has demonstrated superior sample efficiency in learning complex tasks compared to conventional Reinforcement Learning (RL) approaches. However, the practical application of CDRL is encumbered b
Externí odkaz:
http://arxiv.org/abs/2405.02576
Autor:
Johanson, Deborah L, Ahn, Ho Seok, MacDonald, Bruce A, Ahn, Byeong Kyu, Lim, JongYoon, Hwang, Euijun, Sutherland, Craig J, Broadbent, Elizabeth
Publikováno v:
Journal of Medical Internet Research, Vol 22, Iss 3, p e18362 (2020)
Externí odkaz:
https://doaj.org/article/134696f2d1c04bcf8fc08493ae3e81e8
This research explores using lightweight deep neural network architectures to enable the humanoid robot Pepper to understand American Sign Language (ASL) and facilitate non-verbal human-robot interaction. First, we introduce a lightweight and efficie
Externí odkaz:
http://arxiv.org/abs/2309.16898
Autor:
Qureshi, Ans, Smith, David, Gee, Trevor, Nejati, Mahla, Shahabi, Jalil, Lim, JongYoon, Ahn, Ho Seok, McGuinness, Ben, Downes, Catherine, Jangali, Rahul, Black, Kale, Lim, Hin, Duke, Mike, MacDonald, Bruce, Williams, Henry
Aotearoa New Zealand has a strong and growing apple industry but struggles to access workers to complete skilled, seasonal tasks such as thinning. To ensure effective thinning and make informed decisions on a per-tree basis, it is crucial to accurate
Externí odkaz:
http://arxiv.org/abs/2308.07512
Autor:
Kweon, Andy, Hu, Vishnu, Lim, Jong Yoon, Gee, Trevor, Liu, Edmond, Williams, Henry, MacDonald, Bruce A., Nejati, Mahla, Sa, Inkyu, Ahn, Ho Seok
As technology progresses, smart automated systems will serve an increasingly important role in the agricultural industry. Current existing vision systems for yield estimation face difficulties in occlusion and scalability as they utilize a camera sys
Externí odkaz:
http://arxiv.org/abs/2304.06177
Autor:
Xing, Yuning, Pham, Dexter, Williams, Henry, Smith, David, Ahn, Ho Seok, Lim, JongYoon, MacDonald, Bruce A., Nejati, Mahla
Publikováno v:
Proceedings of the Australasian conference on robotics and automation (ACRA 2022)
Smart farming is a growing field as technology advances. Plant characteristics are crucial indicators for monitoring plant growth. Research has been done to estimate characteristics like leaf area index, leaf disease, and plant height. However, few m
Externí odkaz:
http://arxiv.org/abs/2304.03610
Autor:
Qureshi, Ans, Loh, Neville, Kwon, Young Min, Smith, David, Gee, Trevor, Bachelor, Oliver, McCulloch, Josh, Nejati, Mahla, Lim, JongYoon, Green, Richard, Ahn, Ho Seok, MacDonald, Bruce, Williams, Henry
Following a global trend, the lack of reliable access to skilled labour is causing critical issues for the effective management of apple orchards. One of the primary challenges is maintaining skilled human operators capable of making precise fruitlet
Externí odkaz:
http://arxiv.org/abs/2302.09716
This paper presents datasets utilised for synthetic near-infrared (NIR) image generation and bounding-box level fruit detection systems. It is undeniable that high-calibre machine learning frameworks such as Tensorflow or Pytorch, and large-scale Ima
Externí odkaz:
http://arxiv.org/abs/2203.09091
Autor:
Lim, JongYoon, Sa, Inkyu, Ahn, Ho Seok, Gasteiger, Norina, Lee, Sanghyub John, MacDonald, Bruce
Publikováno v:
MDPI Sensors 2021, 21(8), 2712
Sentiment prediction remains a challenging and unresolved task in various research fields, including psychology, neuroscience, and computer science. This stems from its high degree of subjectivity and limited input sources that can effectively captur
Externí odkaz:
http://arxiv.org/abs/2104.09777