Six-Dimensional Target Pose Estimation for Robot Autonomous Manipulation: Methodology and Verification

Wang, Rui; Su, Congjia; Yu, Hao; Wang, Shuo

doi:10.1109/tcds.2022.3151331

Cited by 8 publications

(3 citation statements)

References 39 publications

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“…Manipulating objects based on point clouds has been studied for decades [15], [16]. Early works mainly use deep learning techniques to learn state estimation [17], [18] or manipulation skills from point clouds [19], [20]. As manipulation tasks become complex and labels are difficult to obtain, reinforcement learning techniques are gradually introduced to learn more dexterous skills from point clouds [21], [22].…”

Section: Related Workmentioning

confidence: 99%

Model-Based Reinforcement Learning with LSTM Networks for Non-Prehensile Manipulation Planning

Fong

Campolo

Acar

et al. 2021

2021 21st International Conference on Control, Automation and Systems (ICCAS)

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Manipulating objects without grasping them enables more complex tasks, known as non-prehensile manipulation. Most previous methods only learn one manipulation skill, such as reach or push, and cannot achieve flexible object manipulation. In this work, we introduce MRLM, a Multi-stage Reinforcement Learning approach for non-prehensile Manipulation of objects. MRLM divides the task into multiple stages according to the switching of object poses and contact points. At each stage, the policy takes the point cloud-based stategoal fusion representation as input, and proposes a spatiallycontinuous action that including the motion of the parallel gripper pose and opening width. To fully unlock the potential of MRLM, we propose a set of technical contributions including the state-goal fusion representation, spatially-reachable distance metric, and automatic buffer compaction. We evaluate MRLM on an Occluded Grasping task which aims to grasp the object in configurations that are initially occluded. Compared with the baselines, the proposed technical contributions improve the success rate by at least 40% and maximum 100%, and avoids falling into local optimum. Our method demonstrates strong generalization to unseen object with shapes outside the training distribution. Moreover, MRLM can be transferred to real world with zero-shot transfer, achieving a 95% success rate. Code and videos can be found at https://sites.google.com/view/mrlm.

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Section: Related Workmentioning

confidence: 99%

Model-Based Reinforcement Learning with LSTM Networks for Non-Prehensile Manipulation Planning

Fong

Campolo

Acar

et al. 2021

2021 21st International Conference on Control, Automation and Systems (ICCAS)

View full text Add to dashboard Cite

show abstract

“…B Eing an important type of deep neural network, convolutional neural networks (CNNs) have achieved remarkable success in computer vision，natural language processing and automated speech recognition, which also make significant contributions to the development of cognitive systems [1], robots [2] and computational neuroscience [3]. As the network structures become deeper [4] [5] [6], better features can be learned to improve their performance.…”

Section: Introductionmentioning

confidence: 99%

“…Utilizing CNN models in low-cost environments (e.g. robots [2] and edge-devices [9]) is very challenging due to the limited storage capacity, computing power and battery life. Therefore, our work focuses on building lightweight CNNs to reduce the number of parameters and computations while maintaining good performance.…”

Section: Introductionmentioning

confidence: 99%

LDCNet: A Lightweight Multiscale Convolutional Neural Network Using Local Dense Connectivity for Image Recognition

Wang,

Zhang,

et al. 2024

IEEE Trans. Cogn. Dev. Syst.

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Review of machine learning in robotic grasping control in space application

Jahanshahi,

Zhu

2024

Acta Astronautica

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Six-Dimensional Target Pose Estimation for Robot Autonomous Manipulation: Methodology and Verification

Cited by 8 publications

References 39 publications

Model-Based Reinforcement Learning with LSTM Networks for Non-Prehensile Manipulation Planning

Model-Based Reinforcement Learning with LSTM Networks for Non-Prehensile Manipulation Planning

LDCNet: A Lightweight Multiscale Convolutional Neural Network Using Local Dense Connectivity for Image Recognition

Review of machine learning in robotic grasping control in space application

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