Active Affordance Exploration for Robot Grasping

Liu, Huaping; Yuan, Yuan; Deng, Yuhong; Guo, Xijuan; Wei, Yixuan; Lü, Kai; Fang, Bin; Guo, Di; Sun, Fei

doi:10.1007/978-3-030-27541-9_35

Cited by 11 publications

(5 citation statements)

References 17 publications

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“…The robot only focused on grasping objects that were aligned with the bin wall or boundary, resulting in poor success rates. Separately, in order to grasp the objects placed in well-organized shapes, Chen et al [13] employed a Deep Q-Network (DQN) to guide the robot in actively exploring the environment of the objects placed around highly randomly until a suitable grasp affordance map was generated. This data-driven deep reinforcement learning method results in improper selection of many grasping points due to insufficient training cases, with time-consuming training iterations and low grasping efficiency and success rates.…”

Section: Related Workmentioning

confidence: 99%

Towards Multi-Objective Object Push-Grasp Policy Based on Maximum Entropy Deep Reinforcement Learning under Sparse Rewards

Zhang,

2024

Entropy

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In unstructured environments, robots need to deal with a wide variety of objects with diverse shapes, and often, the instances of these objects are unknown. Traditional methods rely on training with large-scale labeled data, but in environments with continuous and high-dimensional state spaces, the data become sparse, leading to weak generalization ability of the trained models when transferred to real-world applications. To address this challenge, we present an innovative maximum entropy Deep Q-Network (ME-DQN), which leverages an attention mechanism. The framework solves complex and sparse reward tasks through probabilistic reasoning while eliminating the trouble of adjusting hyper-parameters. This approach aims to merge the robust feature extraction capabilities of Fully Convolutional Networks (FCNs) with the efficient feature selection of the attention mechanism across diverse task scenarios. By integrating an advantage function with the reasoning and decision-making of deep reinforcement learning, ME-DQN propels the frontier of robotic grasping and expands the boundaries of intelligent perception and grasping decision-making in unstructured environments. Our simulations demonstrate a remarkable grasping success rate of 91.6%, while maintaining excellent generalization performance in the real world.

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Section: Related Workmentioning

confidence: 99%

Towards Multi-Objective Object Push-Grasp Policy Based on Maximum Entropy Deep Reinforcement Learning under Sparse Rewards

Zhang,

2024

Entropy

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“…Their approach was aimed at assisting the robotic hand to actively explore the environment until the optimal affordance map is obtained. This study [ 61 ] was adapted from their previous works [ 62 , 63 ]. However, they implemented tactile sensing in [ 61 ] as a way to enhance the grasping performance and improve grasp efficiency; in addition, more experimental tests were provided.…”

Section: Critical Reviewmentioning

confidence: 99%

Review of Learning-Based Robotic Manipulation in Cluttered Environments

Mohammed

Kwek

Chua

et al. 2022

Sensors

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Robotic manipulation refers to how robots intelligently interact with the objects in their surroundings, such as grasping and carrying an object from one place to another. Dexterous manipulating skills enable robots to assist humans in accomplishing various tasks that might be too dangerous or difficult to do. This requires robots to intelligently plan and control the actions of their hands and arms. Object manipulation is a vital skill in several robotic tasks. However, it poses a challenge to robotics. The motivation behind this review paper is to review and analyze the most relevant studies on learning-based object manipulation in clutter. Unlike other reviews, this review paper provides valuable insights into the manipulation of objects using deep reinforcement learning (deep RL) in dense clutter. Various studies are examined by surveying existing literature and investigating various aspects, namely, the intended applications, the techniques applied, the challenges faced by researchers, and the recommendations adopted to overcome these obstacles. In this review, we divide deep RL-based robotic manipulation tasks in cluttered environments into three categories, namely, object removal, assembly and rearrangement, and object retrieval and singulation tasks. We then discuss the challenges and potential prospects of object manipulation in clutter. The findings of this review are intended to assist in establishing important guidelines and directions for academics and researchers in the future.

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“…In this approach, the object region was estimated by the density-based spatial clustering of applications with noise (DBSCAN) algorithm, and a depth difference image (DDI) that represents the depth difference between adjacent areas is defined. Different frameworks have been presented in achieving grasping object in clutter such as active affordance exploration framework which leverages the privileges of affordance map and the active exploration [129], integrating perception, action selection, and manipulation policies to address a version of the Mechanical Search problem [130], actor model with neural network that combines Gaussian mixture and normalizing flows [131], joint learning of instance and semantic segmentation for robotic pickand-place with heavy occlusions in clutter [132], and predicting the quality and the pose of grasp using U-Grasping fully convolutional neural network(UG-Net) based on pixel-wise using depth image [133].…”

Section: B Suction and Multifunctional Graspingmentioning

confidence: 99%

Review of Deep Reinforcement Learning-Based Object Grasping: Techniques, Open Challenges, and Recommendations

2020

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The motivation behind our work is to review and analyze the most relevant studies on deep reinforcement learning-based object manipulation. Various studies are examined through a survey of existing literature and investigation of various aspects, namely, the intended applications, techniques applied, challenges faced by researchers and recommendations for minimizing obstacles. This review refers to all relevant articles on deep reinforcement learning-based object manipulation and solutions. The object grasping issue is a major manipulation challenge. Object grasping requires detection systems, methods and tools to facilitate efficient and fast agent training. Several studies have proposed that object grasping and its subtypes are the main elements in dealing with the environment and agent. Unlike other review articles, this review article provides different observations on deep reinforcement learning-based manipulation. The results of this comprehensive review of deep reinforcement learning in the manipulation field may be valuable for researchers and practitioners because they can expedite the establishment of important guidelines.

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Active Affordance Exploration for Robot Grasping

Cited by 11 publications

References 17 publications

Towards Multi-Objective Object Push-Grasp Policy Based on Maximum Entropy Deep Reinforcement Learning under Sparse Rewards

Towards Multi-Objective Object Push-Grasp Policy Based on Maximum Entropy Deep Reinforcement Learning under Sparse Rewards

Review of Learning-Based Robotic Manipulation in Cluttered Environments

Review of Deep Reinforcement Learning-Based Object Grasping: Techniques, Open Challenges, and Recommendations

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