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2019 IEEE International Conference on robotics and Biomimetics, ROBIO 2019

作者： Zhang, Yaowen Liu, Yechao Xie, Zongwu Moallem, Mehrdad Harbin Institute of Technology State Key Laboratory of Robotics and System Harbin Heilongjiang Province150006 China School of Mechatronic Systems Engineering Simon Fraser University VancouverBCV3T 0A3 Canada

ISBN: (纸本)9781728163215

The null space of the Jacobian for the redundant manipulators can be utilized to reach dual control goals and optimal effects. For the flexible base redundant manipulator system, the reaction force from the motion of the manipulator should be carefully handled while the end effector is tracking a desired trajectory. Different optimal goals are discussed, including the minimization of base deflection force/torque and base energy dissipation path. Considering the base reaction dynamic, the reaction force/torque is treated as the input of the base and the deflection energy is treated as the output, an integrated optimal goal is built and solved based on the LQR control to get an optimal feedback for the closed-loop dynamics. We discuss the different optimal goals and implement trough the redefined acceleration. We test the different control laws on the three- link manipulators which mounted on a three-DoF flexible base. Simulation results demonstrate that optimal goal in LQR form can be advantageous in terms of flexible base manipulator. © 2019 IEEE.

关键词： Redundant manipulators

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Programming by Visual Demonstration for Pick-and-Place Tasks using Robot Skills

Programming by Visual Demonstration for Pick-and-Place Tasks...

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IEEE International Conference on robotics and Biomimetics

作者： Peng Hao Tao Lu Yinghao Cai Shuo Wang The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of Sciences Beijing China

In this paper, we present a vision-based robot programming system for pick-and-place tasks that can generate programs from human demonstrations. The system consists of a detection network and a program generation module. The detection network leverages convolutional pose machines to detect the key-points of the objects. The network is trained in a simulation environment in which the train set is collected and auto-labeled. To bridge the gap between reality and simulation, we propose a design method of transform function for mapping a real image to synthesized style. Compared with the unmapped results, the Mean Absolute Error (MAE) of the model completely trained with synthesized images is reduced by 23% and the False Negative Rate FNR (FNR) of the model fine-tuned by the real images is reduced by 42.5% after mapping. The program generation module provides a human-readable program based on the detection results to reproduce a real-world demonstration, in which a longshort memory (LSM) is designed to integrate current and historical information. The system is tested in the real world with a UR5 robot on the task of stacking colored cubes in different orders.

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Self-intersection Attention Pooling Based Classification for Rock Recognition

Self-intersection Attention Pooling Based Classification for...

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International Conference on control, Automation, robotics and Vision (ICARCV)

作者： Liang Chen Qian Zheng Zhitao Liu Weijie Mao Fulong Lin State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China Fudan University Shanghai China General Institute of Design and Research China Railway Engineering Equipment Group Co. LTD Zhengzhou China

ISBN: (数字)9781728177090

ISBN: (纸本)9781728177106

Rock recognition is a critical step for intelligent control system in tunnel boring machine (TBM). We build the rock recognition dataset by the visual monitoring system in TBM. After that, we present an attention-fusion network (AFN) for rock recognition classification. Our model is trained with the recognition object (rock) image, and gets the rock attention map through the self-intersection operation. To complete the classification task, we propose the attention block in the attention-fusion classifier, which is effective to fuse and encode the feature and attention in an end-to-end manner. We confirm the effectiveness of the self-intersection operation and attention block by visualized experiment and contrast experiment. At the same time, experimental results of the rock recognition dataset indicate that AFN outperforms state-of-the-art methods, including LSTM (mask-guided), ABN (attention-based), ResNetSO (global-feature), both in accuracy and realtime on the rock recognition tasks.

关键词： Rocks Task analysis Feature extraction Visualization Monitoring Convolution Image recognition

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Learning Category-level Implicit 3D Rotation Representations for 6D Pose Estimation from RGB Images

Learning Category-level Implicit 3D Rotation Representations...

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IEEE International Conference on robotics and Biomimetics

作者： Xiaocan Li Yinghao Cai Shuo Wang Tao Lu State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China

We exploit the embedding ability of a de-noising autoencoder for an implicit 3D rotation representation learning at the category level. Contrast to the exact 3D reconstruction model of each instance-level physical object, we leverage the inexact CAD/Reconstruction models of an object as the representative model for some category. Under our assumptions that objects within the same category share resemble geometry, we train a de-noising autoencoder on synthetic 3D views of category-level objects to extract the homogenous features at the bottleneck layer. The latent representation is agnostic not only to heterogeneous textures, colors, and illuminations, but also ambiguous pose caused by object symmetry. To extend the instance-level 3D translation estimation to the category level, we considered the 3D diagonal length ratio between the source and target object. We achieved a frame rate of 17Hz.

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Measurement-Induced Boolean Dynamics from Open Quantum Networks ⁎

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IFAC-PapersOnLine 2020年第2期53卷 275-280页

作者： Hongsheng Qi Biqiang Mu Ian R. Petersen Guodong Shi Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing 100049 China Research School of Electrical Energy and Materials Engineering The Australian National University Canberra 0200 Australia Australian Center for Field Robotics School of Aerospace Mechanical and Mechatronic Engineering The University of Sydney NSW 2008 Australia

In this paper, we study the induced probabilistic Boolean dynamics for dynamical quantum networks subject to sequential quantum measurements. In this part of the paper, we focus on closed networks of quits whose states evolutions are described by a continuous Lindblad master equation. When measurements are performed sequentially along such continuous dynamics, the quantum network states undergo random jumps and the corresponding measurement outcomes can be described by a probabilistic Boolean network. First of all, we show that the state transition of the induced Boolean networks can be explicitly represented through realification of the master equation. Next, when the open quantum dynamics is relaxing in the sense that it possesses a unique equilibrium as a global attractor, structural properties including absorbing states, reducibility, and periodicity for the induced Boolean network are direct consequences of the relaxing property. Finally, we show that for quantum consensus networks as a type of non-relaxing open quantum network dynamics, the communication classes of the measurement-induced Boolean networks are encoded in the quantum Laplacian of the underlying interaction graph.

关键词： quantum networks open quantum systems quantum measurements Boolean networks

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A novel robot system assisting non-invasive positive pressure ventilation

A novel robot system assisting non-invasive positive pressur...

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2018 IEEE International Conference on Information and Automation, ICIA 2018

作者： Ma, Xuesong Zhu, Jiahua Gao, Wenpeng Fu, Yili State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin Heilongjiang Province China

ISBN: (纸本)9781538680698

Non-invasive positive pressure ventilation has the advantages of fewer complications and is beneficial to the patients. However, the anaesthesiologists must hold the mask covering the patients' mouth and nose, which is labour intensity and lack of not ergonomics. In this paper, we propose a concept of the anti-maxillary opening position (that is mandibular anti jaw position and the opening position implement step by step). A novel robot system assisting noninvasive positive pressure ventilation is designed to help the anaesthesiologist hold the mask, and meanwhile keep the anti-maxillary in the opening position for an optimal breathing condition without damaging the other tissues. The robot adopts the active and passive control mode. We have completed the structure design of the robot mechanical system and the processing and assembly of the prototype and designed the electronic control system for the robot, configured the electronic control hardware of each part of the robot, so that the robot has the ability to complete the auxiliary breathing capacity and limit protection. Finally, the experimental platform of robot system was built and the preliminary clinical trial was completed. © 2018 IEEE.

关键词： Machine design

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Programmable cell aggregation by a synthetic biosilicification approach

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iScience 2025年第6期28卷

作者： Qing Wang Jing Sun Lei Zang Huaxiong Yao Lin Wang Runtao Zhu Jie Li Simin Zeng Hongting Tang Teng Wang Ji Liu Bo Wang Bo Li Zhiyuan Liu Zhuojun Dai State Key Laboratory of Quantitative Synthetic Biology Shenzhen Institute of Synthetic Biology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China School of Chemistry and Chemical Engineering Harbin Institute of Technology Harbin 150001 China Center of Neural Engineering CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China Department of Mechanical and Energy Engineering Southern University of Science and Technology Shenzhen 518055 China Department of Civil Engineering University of Nottingham Ningbo China 199 Taikang East Road Ningbo 315100 China

Programmable cell aggregation offers valuable insights into the natural development of synthetic multicellular systems and enables precise control over spatial organization and material structuring. Previous efforts have focused on modifying cells with designed organic-based adhesive modules and assembling cells into defined patterns. Here, we present a different approach to guide cell assembly by tuning the organic-inorganic interactions. Our method involves engineering cells to express a silicifying peptide on their surfaces, which promotes silica deposition on the cell walls. This peptide simultaneously binds to the silica synthesized on adjacent cells, triggering cell clustering. The engineered cells exhibit rapid aggregation, with approximately 95% of cells assembling within 15 min. We further show that this capability can facilitate materials assembly and chemical production. Our biosilicification-based approach offers novel insights into natural multicellularity mechanisms and holds potential for applications in biomanufacturing and materials engineering.

关键词： Bioengineering Synthetic biology Biomaterials

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Non-Periodic Lower-Limb Motion Recognition with Noncontact Capacitive Sensing

Non-Periodic Lower-Limb Motion Recognition with Noncontact C...

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IEEE/ASME (AIM) International Conference on Advanced Intelligent Mechatronics

作者： Enhao Zheng Jinchen Zeng Dongfang Xu Qining Wang Hong Qiao State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences No. 95 of Zhongguancun East Road Beijing China School of Automation and Electrical Engineering University of Science and Technology Beijing 30 Xueyuan Road Beijing Robotics Research Group College of Engineering Peking University Beijing China

ISBN: (数字)9781728167947

ISBN: (纸本)9781728167954

Noncontact captive sensing is a new sensing strategy that we proposed previously to compensate for the limitations of existing surface electromyography studies for exoskeleton control. It has been validated on locomotion mode recognition and gait phase estimation. However, our previous studies addressed the tasks of periodic ambulation based on machine learning algorithms. The performances of the capacitive sensing on non-periodical lower-limb motion recognition have never been evaluated. In this preliminary study, we designed a motion recognition method by fusing the capacitive sensing and the inertial sensors. The recognition algorithm was designed based on the combined logics, which freed the system from burdensome training procedures in the recognition tasks. The method was validated on three healthy subjects in performing 6 lower-limb motions and the transitions between them (10 in total). The capacitance-inertial fusion method produced an average precision/recall of >0.92 in static motions and >0.86 with transitions. The most prominent improvement of using the capacitance signals is that it increases the time-response ability during the motion transitions. Compared with purely using inertial sensors, the sensor fusion method reduced more than 100-ms latency on average. The pilot study extends the scope of the new sensing method in human motion recognition. Future efforts will be paid in this direction to get more promising results.

关键词： Robot sensing systems Capacitance Electrodes Task analysis Legged locomotion Exoskeletons

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Design and Implementation of a Lightweight Lower Extremity Exoskeleton

Design and Implementation of a Lightweight Lower Extremity E...

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作者： Jiamin Mu Hongzhou Jiang Yuxiang Hua Jie Zhao Yanhe Zhu State Key Laboratory of Robotics and System Harbin Institute of Technology Department of Fluid Control and Automation Harbin Institute of Technology

This paper summarizes the biomechanics simulation and design of a lightweight lower extremity exoskeleton. The biomechanics simulation was carried out using LifeMOD to obtain joint torques of human body in the condition of its applications including walking on flat ground, ascending stairs, sitting down and standing up which provided the design criteria of actuating torques. The anthropomorphically based exoskeleton has 7 degrees of freedom per leg, two of which are powered by brushless DC motors, and the total mass is less than 12 kg due to the use of carbon fiber material and the compact structural design and optimization. controlling and sensing system based on CANopen communication protocol is proposed which can provide high communication efficiency and reliability for the control of the exoskeleton.

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Dexterous Manipulation through Imitation Learning: A Survey

arXiv

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arXiv 2025年

作者： An, Shan Meng, Ziyu Tang, Chao Zhou, Yuning Liu, Tengyu Ding, Fangqiang Zhang, Shufang Mu, Yao Song, Ran Zhang, Wei Hou, Zeng-Guang Zhang, Hong School of Electrical and Information Engineering Tianjin University Tianjin300072 China School of Control Science and Engineering Shandong University Jinan250061 China State Key Laboratory of General Artificial Intelligence Beijing100086 China Department of Mechanical and Process Engineering ETH Zurich Zurich8092 Switzerland Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen518055 China School of Informatics University of Edinburgh EH8 9AB United Kingdom Department of Computer Science University of Hong Kong 999077 Hong Kong State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China CASIA-MUST Joint Laboratory of Intelligence Science and Technology Institute of Systems Engineering Macau University of Science and Technology 999078 China

Dexterous manipulation, which refers to the ability of a robotic hand or multi-fingered end-effector to skillfully control, reorient, and manipulate objects through precise, coordinated finger movements and adaptive force modulation, enables complex interactions similar to human hand dexterity. With recent advances in robotics and machine learning, there is a growing demand for these systems to operate in complex and unstructured environments. Traditional model-based approaches struggle to generalize across tasks and object variations due to the high-dimensionality and complex contact dynamics of dexterous manipulation. Although model-free methods such as reinforcement learning (RL) show promise, they require extensive training, large-scale interaction data, and carefully designed rewards for stability and effectiveness. Imitation learning (IL) offers an alternative by allowing robots to acquire dexterous manipulation skills directly from expert demonstrations, capturing fine-grained coordination and contact dynamics while bypassing the need for explicit modeling and large-scale trial-and-error. This survey provides an overview of dexterous manipulation methods based on imitation learning (IL), details recent advances, and addresses key challenges in the field. Additionally, it explores potential research directions to enhance IL-driven dexterous manipulation. Our goal is to offer researchers and practitioners a comprehensive introduction to this rapidly evolving domain. Copyright © 2025, The Authors. All rights reserved.

关键词： Grippers

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