Author name cluster

Henry Williams

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

9 papers

2 author rows

IROS Conference 2025 Conference Paper

Accelerating Real-World Overtaking in F1TENTH Racing Employing Reinforcement Learning Methods

Emily Steiner
Daniel van der Spuy
Futian Zhou
Afereti Pama
Minas Liarokapis
Henry Williams

While autonomous racing performance in Time-Trial scenarios has seen significant progress and development, autonomous wheel-to-wheel racing and overtaking are still severely limited. These limitations are particularly apparent in real-life driving scenarios where state-of-the-art algorithms struggle to safely or reliably complete overtaking manoeuvres. This is important, as reliable navigation around other vehicles is vital for safe autonomous wheel-to-wheel racing. The F1Tenth Competition provides a useful opportunity for developing wheel-to-wheel racing algorithms on a standardised physical platform. The competition format makes it possible to evaluate overtaking and wheel-to-wheel racing algorithms against the state-of-the-art. This research presents a novel racing and overtaking agent capable of learning to reliably navigate a track and overtake opponents in both simulation and reality. The agent was deployed on an F1Tenth vehicle and competed against opponents running varying competitive algorithms in the real world. The results demonstrate that the agent’s training against opponents enables deliberate overtaking behaviours with an overtaking rate of 87% compared 56% for an agent trained just to race.

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AAAI Conference 2025 Conference Paper

CTD4 – a Deep Continuous Distributional Actor-Critic Agent with a Kalman Fusion of Multiple Critics

David Valencia
Henry Williams
Yuning Xing
Trevor Gee
Bruce A MacDonald
Minas Liarokapis

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 by challenging projection steps, detailed parameter tuning, and domain knowledge. This paper addresses these challenges by introducing a pioneering Continuous Distributional Model-Free RL algorithm tailored for continuous action spaces. The proposed algorithm simplifies the implementation of distributional RL, adopting an actor-critic architecture wherein the critic outputs a continuous probability distribution. Additionally, we propose an ensemble of multiple critics fused through a Kalman fusion mechanism to mitigate overestimation bias. Through a series of experiments, we validate that our proposed method provides a sample-efficient solution for executing complex continuous-control tasks.

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ICRA Conference 2025 Conference Paper

How About Them Apples: 3D Pose and Cluster Estimation of Apple Fruitlets in a Commercial Orchard

Ans Qureshi
David Smith
Trevor Gee
Ho Seok Ahn
Benjamin McGuinness
Catherine Downes
Rahul Jangali
Kale Black

Aotearoa's apple industry struggles to maintain the skilled workforce required for fruitlet thinning each year. Skilled labourers play a pivotal role in managing crop loads by precisely thinning fruitlets to a desired number to achieve the desired spacing for high-quality apple growth. This complex task requires accurate mapping of the fruitlets along each branch. This paper presents a novel vision system capable of mapping the orientation and clustering information of apple fruitlets. Fruitlet pose estimation has been validated against data collected from a real-world commercial apple orchard. The results show an improved counting accuracy of 83. 97% on prior implementations, an orientation estimate accuracy of 88. 1%, and a clustering accuracy of 94. 3%. Future work will utilise this information to determine which fruitlets to remove and then robotically thin them from the canopy.

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IROS Conference 2025 Conference Paper

OrchardDepth++: Binned KL-Flood Regularization for Monocular Depth Estimation of Orchard Scene

Zhichao Zheng 0009
Henry Williams
Trevor Gee
Bruce A. MacDonald

Monocular depth estimation is a rudimentary problem for robotic perception systems and downstream applications. However, depth estimation from a single image is an inherently ill-posed problem due to data loss related to projection from 3D to 2D. Recent studies address the discrepancy between camera parameters by using learning-based methods and unifying the camera model to canonical camera space or bipolar representations, thus addressing the problem of training a metric depth model over different datasets with different camera parameters. In addition, the previous study, OrchardDepth, introduced the sparse-dense depth consistency loss function to learn the dense depth distribution through the city autonomous driving scene to improve model performance in the orchard. Instead of enforcing strict consistency between the sparse and dense depth, this work introduced the KL divergence to encourage the network to adapt to the depth distributions of different sensors and penalize deviations from reliable regions while tolerating errors in unreliable areas. Furthermore, we further enhance the depth consistency loss by integrating bins into the supervised discretised depth distribution. This method significantly improves the robustness and performance of our previous method. In addition, it improves the absolute relative error in the orchard dataset by 17. 3% and 16. 2% in contrast to SILog Loss and OrchardDepth baseline, respectively. Thus enhancing the new training paradigm for depth estimation in the orchard scene.

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IROS Conference 2024 Conference Paper

Archie Jnr: A Robotic Platform for Autonomous Cane Pruning of Grapevines

Henry Williams
David Smith
Jalil Shahabi
Trevor Gee
Ans Qureshi
Benjamin McGuinness
Scott Harvey
Catherine Downes

Cane pruning grapevines is a complex manual task requiring expert vine assessment to determine which canes to prune. This paper presents Archie Jnr, which was developed to autonomously assess the structure of the vine and prune the lower-quality canes as an expert pruner would. The platform has been extensively evaluated in a real-world commercial vineyard using a three-cane pruning method. The results show the effectiveness of the vision system for generating accurate assessments of a vine’s canes. The platform is also shown to be capable of successfully pruning 71. 1% of the 311 total canes that required pruning across 25 vines.

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IROS Conference 2024 Conference Paper

Archie Snr: A Robotic Platform for Autonomous Apple Fruitlet Thinning

Henry Williams
Ans Qureshi
David Smith
Trevor Gee
Benjamin McGuinness
Rahul Jangali
Kale Black
Scott Harvey

Apple fruitlet thinning is critical in cultivating high-quality apples, requiring an expert workforce to manage the orchard. The thinning process requires precise mapping of fruitlet clusters across the tree branches to manage the desired load for each tree. This paper presents Archie Snr, which was developed to autonomously assess the current load of the tree and thin the excess apples as an expert thinner would. The platform has been extensively evaluated in a real-world commercial orchard. The results show the platform can generate an average load count accuracy of 82. 1% with a recall of 93. 3%. The system was then able to successfully thin 66. 14% of the fruitlets from the canopy.

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IROS Conference 2024 Conference Paper

Image-Based Deep Reinforcement Learning with Intrinsically Motivated Stimuli: On the Execution of Complex Robotic Tasks

David Valencia
Henry Williams
Yuning Xing
Trevor Gee
Minas Liarokapis
Bruce A. MacDonald

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 discovering efficient exploration strategies and aiding the learning process. In this work, inspired by intrinsic motivation theory, we postulate that the intrinsic stimuli of novelty and surprise can assist in improving exploration in complex, sparsely rewarded environments. We introduce a novel sample-efficient method able to learn directly from pixels, an image-based extension of TD3 with an autoencoder called NaSA-TD3. The experiments demonstrate that NaSA-TD3 is easy to train and an efficient method for tackling complex continuous-control robotic tasks, both in simulated environments and real-world settings. NaSA-TD3 outperforms existing state-of-the-art RL image-based methods in terms of final performance without requiring pre-trained models or human demonstrations.

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ICRA Conference 2023 Conference Paper

Comparison of Model-Based and Model-Free Reinforcement Learning for Real-World Dexterous Robotic Manipulation Tasks

David Valencia
John Jia
Raymond Li
Alex Hayashi
Megan Lecchi
Reuel Terezakis
Trevor Gee
Minas Liarokapis

Model Free Reinforcement Learning (MFRL) has shown significant promise for learning dexterous robotic manipulation tasks, at least in simulation. However, the high number of samples, as well as the long training times, prevent MFRL from scaling to complex real-world tasks. Model- Based Reinforcement Learning (MBRL) emerges as a potential solution that, in theory, can improve the data efficiency of MFRL approaches. This could drastically reduce the training time of MFRL, and increase the application of RL for real- world robotic tasks. This article presents a study on the feasibility of using the state-of-the-art MBRL to improve the training time for two real-world dexterous manipulation tasks. The evaluation is conducted on a real low-cost robot gripper where the predictive model and the control policy are learned from scratch. The results indicate that MBRL is capable of learning accurate models of the world, but does not show clear improvements in learning the control policy in the real world as prior literature suggests should be expected.

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IROS Conference 2023 Conference Paper

Seeing the Fruit for the Leaves: Robotically Mapping Apple Fruitlets in a Commercial Orchard

Ans Qureshi
David Smith
Trevor Gee
Mahla Nejati
Jalil Shahabi
JongYoon Lim
Ho Seok Ahn
Benjamin McGuinness

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 accurately measure the crop load of individual apple trees. However, this task poses challenges due to the dense foliage that hides the fruitlets within the tree structure. In this paper, we introduce the vision system of an automated apple fruitlet thinning robot, developed to tackle the labor shortage issue. This paper presents the initial design, implementation, and evaluation specifics of the system. The platform straddles the 3. 4 m tall 2D apple canopy structures to create an accurate map of the fruitlets on each tree. We show that this platform can measure the fruitlet load on an apple tree by scanning through both sides of the branch. The requirement of an overarching platform was justified since two-sided scans had a higher counting accuracy of 81. 17% than one-sided scans at 73. 7%. The system was also demonstrated to produce size estimates within 5. 9% RMSE of their true size.

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