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Multi-Object Grasping - Types and Taxonomy 39

Multi-Object Grasping - Types and Taxonomy

引用

IEEE international conference on robotics and automation (ICRA)

作者： Sun, Yu Amatova, Eliza Chen, Tianze Univ S Florida Robot Percept & Act Lab RPAL Comp Sci & Engn Dept Tampa FL 33620 USA

ISBN: (纸本)9781728196817

This paper proposes 12 multi-object grasps (MOGs) types from a human and robot grasping data set. The grasp types are then analyzed and organized into a MOG taxonomy. This paper first presents three MOG data collection setups: a human finger tracking setup for multi-object grasping demonstrations, a real system with Barretthand, UR5e arm, and a MOG algorithm, a simulation system with the same settings as the real system. Then the paper describes a novel stochastic grasping routine designed based on a biased random walk to explore the robotic hand's configuration space for feasible MOGs. Based on observations in both the human demonstrations and robotic MOG solutions, this paper proposes 12 MOG types in two groups: shape-based types and function-based types. The new MOG types are compared using six characteristics and then compiled into a taxonomy. This paper then introduces the observed MOG type combinations and shows examples of 16 different combinations.

关键词： Legged locomotion automation Taxonomy Grasping Data collection Data models Planning

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Improving Haptic Exploration of Object Shape by Discovering Symmetries 39

Improving Haptic Exploration of Object Shape by Discovering ...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Bonzini, Aramis Augusto Seminara, Lucia Jamone, Lorenzo Queen Mary Univ London Sch Elect Engn & Comp Sci ARQ Adv Robot Queen Mary London E1 4NS England Univ Genoa Dept Naval Elect Elect & Telecommun Engn DITEN Via Opera Pia 11A I-16145 Genoa Italy

ISBN: (纸本)9781728196817

The shapes of most real-world objects are symmetric with respect to at least one plane of symmetry. This information is unconsciously used by humans when they attempt to estimate the shape of an object in presence of uncertainty or missing evidence, for example if the object is partially occluded or if they are exploring the object by touch (i.e. haptic exploration). In robotics, this concept has been used for the visual estimation of object shape. However, no attempt has been made so far to incorporate this idea into haptic-based estimation. This work presents a method for the haptic exploration of object shape that includes the assumption that a symmetry could exist. The approach combines a tailored version of Gaussian Processes and a novel exploratory procedure that is able to detect the position and orientation of any plane of symmetry. Our results show that, if one or more symmetries exist, the object shape can be estimated faster and more accurately. Interestingly, in the case that no symmetry is present, the exploration process is only slightly slower, and the final accuracy of the shape estimation is not compromised.

关键词： Adaptation models Visualization Shape Computational modeling Estimation Gaussian processes Mathematical models

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Learn to Grasp with Less Supervision: A Data-Efficient Maximum Likelihood Grasp 39

Learn to Grasp with Less Supervision: A Data-Efficient Maxim...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Zhu, Xinghao Zhou, Yefan Fan, Yongxiang Sun, Lingfeng Chen, Jianyu Tomizuka, Masayoshi Univ Calif Berkeley Mech Syst Control Lab Uc Berkeley CA 94720 USA FANUC Amer Corp FANUC Adv Res Lab Berkeley CA USA

ISBN: (纸本)9781728196817

Robotic grasping for a diverse set of objects is essential in many robot manipulation tasks. One promising approach is to learn deep grasping models from large training datasets of object images and grasp labels. However, empirical grasping datasets are typically sparsely labeled (i.e., a small number of successful grasp labels. in each image). The data sparsity issue can lead to insufficient supervision and false-negative labels, and thus results in poor learning results. This paper proposes a Maximum Likelihood Grasp Sampling Loss (MLGSL) to tackle the data sparsity issue. The proposed method supposes that successful grasps are stochastically sampled from the predicted grasp distribution and maximizes the observing likelihood. MLGSL is utilized for training a fully convolutional network that generates thousands of grasps simultaneously. Training results suggest that models based on MLGSL can learn to grasp with datasets composing of 2 labels per image. Compared to previous works, which require training datasets of 16 labels per image, MLGSL is 8. more data-efficient. Meanwhile, physical robot experiments demonstrate an equivalent performance at a 90.7% grasp success rate on household objects. Codes and videos are available at [1].

关键词： Training Convolutional codes automation Grasping Task analysis Robots Videos

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Trajectory Prediction with Linguistic Representations 39

Trajectory Prediction with Linguistic Representations

引用

IEEE international conference on robotics and automation (ICRA)

作者： Kuo, Yen-Ling Huang, Xin Barbu, Andrei McGill, Stephen G. Katz, Boris Leonard, John J. Rosman, Guy Toyota Res Inst Cambridge MA 02139 USA MIT CSAIL Cambridge MA 02139 USA MIT CBMM Cambridge MA 02139 USA

ISBN: (纸本)9781728196817

Language allows humans to build mental models that interpret what is happening around them resulting in more accurate long-term predictions. We present a novel trajectory prediction model that uses linguistic intermediate representations to forecast trajectories, and is trained using trajectory samples with partially-annotated captions. The model learns the meaning of each of the words without direct per-word supervision. At inference time, it generates a linguistic description of trajectories which captures maneuvers and interactions over an extended time interval. This generated description is used to refine predictions of the trajectories of multiple agents. We train and validate our model on the Argoverse dataset, and demonstrate improved accuracy results in trajectory prediction. In addition, our model is more interpretable: it presents part of its reasoning in plain language as captions, which can aid model development and can aid in building confidence in the model before deploying it.

关键词： automation Buildings Linguistics Predictive models Cognition Trajectory Cognitive science

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Towards Scale Consistent Monocular Visual Odometry by Learning from the Virtual World 39

Towards Scale Consistent Monocular Visual Odometry by Learni...

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IEEE international conference on robotics and automation (ICRA)

作者： Zhang, Sen Zhang, Jing Tao, Dacheng Univ Sydney Sch Comp Sci Sydney NSW Australia

ISBN: (纸本)9781728196817

Monocular visual odometry (VO) has attracted extensive research attention by providing real-time vehicle motion from cost-effective camera images. However, state-of-the-art optimization-based monocular VO methods suffer from the scale inconsistency problem for long-term predictions. Deep learning has recently been introduced to address this issue by leveraging stereo sequences or ground-truth motions in the training dataset. However, it comes at an additional cost for data collection, and such training data may not be available in all datasets. In this work, we propose VRVO, a novel framework for retrieving the absolute scale from virtual data that can be easily obtained from modern simulation environments, whereas in the real domain no stereo or ground-truth data are required in either the training or inference phases. Specifically, we first train a scale-aware disparity network using both monocular real images and stereo virtual data. The virtual-to-real domain gap is bridged by using an adversarial training strategy to map images from both domains into a shared feature space. The resulting scale-consistent disparities are then integrated with a direct VO system by constructing a virtual stereo objective that ensures the scale consistency over long trajectories. Additionally, to address the suboptimality issue caused by the separate optimization backend and the learning process, we further propose a mutual reinforcement pipeline that allows bidirectional information flow between learning and optimization, which boosts the robustness and accuracy of each other. We demonstrate the effectiveness of our framework on the KITTI and vKITTI2 datasets.

关键词： Training Deep learning Pipelines Training data Robustness Real-time systems Data models

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Polarization Image Demosaicking with Directional Cubic Residual Interpolation 13

Polarization Image Demosaicking with Directional Cubic Resid...

引用

13th IEEE international conference on CYBER Technology in automation, Control, and Intelligent Systems, CYBER 2023

作者： Su, Yiming Lu, Yang Liu, Jiawei Zhang, Zhen Tian, Jiandong State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9798350315196

This paper proposes a rapid and efficient demosaicking algorithm specifically for the polarization filter array to effectively address the issue of polarization image demosaicking. Our method utilizes directional cubic filters and edge-aware weights to obtain a well-refined guide image that helps us turn the mosaicked image into full-resolution polarization images through residual interpolation. Experimental findings illustrate that our proposed method performs so well against existing monochrome and chromatic polarization filter array demosaicking methods. © 2023 IEEE.

关键词： Interpolation

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A Hybrid Dynamic Modeling Method for External Torque Estimation of Robot Manipulators 5

A Hybrid Dynamic Modeling Method for External Torque Estimat...

引用

5th international conference on robotics, Intelligent Control and Artificial Intelligence, RICAI 2023

作者： Yuan, Hanqing Ren, Shanrong Yao, Jun Mao, Jianliang College of Automation Engineering Shanghai University of Electric Power Shanghai China School of Automation Southeast University Nanjing China

ISBN: (纸本)9798350357950

This paper introduces a hybrid modeling approach designed to estimate external forces in robot manipulators, with a focus on achieving high-precision force estimation while minimizing reliance on force/torque sensors. Traditional methods used for robot dynamics modeling and identification often grapple with unmodeled dynamics, system measurement noise, filtering delays, and equipment vibrations, all of which contribute to significant errors in the resulting dynamic models. To address these challenges, this paper leverages neural networks to mitigate the discrepancies in the physical model. The process commences with the application of the Newton-Euler method to perform dynamic modeling of the robot manipulators. Subsequent parameter identification is conducted to establish the physical model governing the robot manipulators' dynamics. Building on this physical model, a backpropagation (BP) neural network is employed to create a data-driven model of the model errors, thereby further enhancing model accuracy. To validate the effectiveness of this approach, external torque estimation experiments were carried out on a six-axis robot manipulators platform utilizing a Beckhoff controller. The experimental results affirm the method's capability to significantly improve the accuracy of external torque estimation in scenarios where force/torque sensors are unavailable. © 2023 IEEE.

关键词： dynamics modeling neural network models parameter identification robot manipulator

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Research and Design of Machine Vision-based Workpiece Defect Sorting Robot

Research and Design of Machine Vision-based Workpiece Defect...

引用

2024 international conference on Intelligent Computing and robotics, ICICR 2024

作者： Fu, Qingyu Yan, Hao He, Zhe Li, Wenyao Zang, Wenjing Zhu, Cheng Department of Automation Tianjin University of Commerce Tianjin China Computer Science Department Tianjin University of Commerce Tianjin China Automation Laboratory Tianjin University of Commerce Tianjin China

ISBN: (纸本)9798350372274

With the continuous improvement of industrial automation level, machine vision technology has been widely applied in the field of workpiece defect detection and sorting. In response to this demand, this paper proposes and designs a machine vision-based workpiece defect sorting system. This system integrates advanced technologies such as camera calibration, image preprocessing, and deep learning, which can efficiently and accurately identify workpiece defects and achieve automatic sorting. To verify the performance of the system, an experimental platform close to actual industrial scenarios was built, and extensive tests were conducted on workpieces of different types and batches. The experimental results show that the system has good robustness and reliability in complex environments, with a defect detection rate of over 98% and a sorting efficiency more than three times that of manual methods. Compared with traditional manual inspection methods, the machine vision sorting system significantly improves production efficiency and product quality, providing a feasible technical solution for the intelligent development of industries such as discrete manufacturing, with broad application prospects. In the future, machine vision technology is expected to play an important role in more industrial scenarios, promoting intelligent and high-quality development in manufacturing. ©2024 IEEE.

关键词： Machine vision

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Body Partition Refinement Dynamic Graph Convolutional Network for Skeleton-based Action Recognition 4

Body Partition Refinement Dynamic Graph Convolutional Networ...

引用

4th international conference on Industrial automation, robotics and Control Engineering, IARCE 2024

作者： Liu, Wenkang Wang, Yifan Zhang, Xiaoheng Li, Yang Beihang University Department of Automation Science and Electrical Engineering Beijing China

ISBN: (纸本)9798350380323

Graph convolutional networks (GCNs) are the most commonly used methods recently and have greatly pro-moted research on skeleton-based action recognition. Aggregating features guided by a topology graph and generating adja-cency matrices with semantic edges in the topology graph are important for the task. However, building a simple and applica-ble graph is a challenging problem. In this work, we construct a novel body partition refinement dynamic graph convolutional network (BPR-DGCN) for skeleton-based action recognition, in which we propose a new body partition refinement dynamic graph convolutional module (BPR-DGCM) to efficiently extract representative features. Furthermore, by combining the traditional physical connection graph with our proposed body partition connection graph, we construct a body partition refinement dynamic graph convolutional unit (BPR-DGCU), which adopts a channel grouping strategy to learn a shared dynamic topology graph as a generic prior for all groups and refine it with chan-nel-specific body partition connection graph for different groups. Our BPR-DGCN outperforms state-of-the-art methods on the NTU RGB+D 60 dataset. © 2024 IEEE.

关键词： Musculoskeletal system

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HR-Planner: A Hierarchical Highway Tactical Planner based on Residual Reinforcement Learning 39

HR-Planner: A Hierarchical Highway Tactical Planner based on...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Wu, Haoran Li, Yueyuan Zhuang, Hanyang Wang, Chunxiang Yang, Ming Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China Minist Educ China Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China Shanghai Engn Res Ctr Intelligent Control & Manag Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Univ Michigan Shanghai Jiao Tong Univ Joint Inst Shanghai 200240 Peoples R China

ISBN: (纸本)9781728196817

Tactical planning is crucial for safe and efficient driving on the highway. However, the problem is complicated by the uncertain intention of surrounding vehicles, as well as observation noise caused by measurement noise and perception errors. Rule-based tactical planning methods are ineffective in handling dynamic scenarios with uncertainty, and susceptible to observation noise. To tackle this problem, we propose a hierarchical tactical planning framework based on residual reinforcement learning. Besides, a new reinforcement learning from demonstrations scheme that views rule-based methods as soft guidance is developed to combine prior knowledge with data-driven methods. Based on the framework and the training scheme, rule-based methods not only can be improved in highway scenarios with uncertainty and observation noise, but also will guide the training procedure for increased sampling efficiency. Additionally, to boost in-depth and consistent exploration in a vehicle system with inertia, we employ noisy networks to explore the optimal policy. The proposed method is validated in a stochastic and uncertain simulation environment, and the results reveal that our method outperforms both rulebased methods and pure data-driven methods in terms of safety and driving efficiency under noisy observations and uncertainty.

关键词： Road transportation Training Uncertainty automation Measurement uncertainty Reinforcement learning Planning

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