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检索条件"任意字段=International Symposium on Computational Intelligence in Robotics and Automation"

共 3101 条记录，以下是341-350 订阅

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Learning Grasps for Unknown Objects in Cluttered Scenes

Learning Grasps for Unknown Objects in Cluttered Scenes

引用

IEEE international Conference on robotics and automation (ICRA)

作者： Fischinger, David Vincze, Markus Jiang, Yun Vienna Univ Technol Automat Control Inst A-1040 Vienna Austria Cornell Univ Dept Comp Sci Ithaca NY 14850 USA

ISBN: (纸本)9781467356411;9781467356435

In this paper, we propose a method for grasping unknown objects from piles or cluttered scenes, given a point cloud from a single depth camera. We introduce a shape-based method Symmetry Height Accumulated Features (SHAF) - that reduces the scene description complexity such that the use of machine learning techniques becomes feasible. We describe the basic Height Accumulated Features and the Symmetry Features and investigate their quality using an F-score metric. We discuss the gain from Symmetry Features for grasp classification and demonstrate the expressive power of Height Accumulated Features by comparing it to a simple height based learning method. In robotic experiments of grasping single objects, we test 10 novel objects in 150 trials and show significant improvement of 34% over a state-of-the-art method, achieving a success rate of 92%. An improvement of 29% over the competitive method was achieved for a task of clearing a table with 5 to 10 objects and overall 90 trials. Furthermore we show that our approach is easily adaptable for different manipulators by running our experiments on a second platform.

关键词： computational complexity learning (artificial intelligence) manipulators pattern classification

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Continuous Vocal Imitation with Self-organized Vowel Spaces in Recurrent Neural Network

Continuous Vocal Imitation with Self-organized Vowel Spaces ...

引用

IEEE international Conference on robotics and automation

作者： Kanda, Hisashi Ogata, Tetsuya Takahashi, Toru Komatani, Kazunori Okuno, Hiroshi G. Kyoto Univ Grad Sch Informat Dept Intelligence Sci & Technol Kyoto Japan

ISBN: (纸本)9781424427888

A continuous vocal imitation system was developed using a computational model that explains the process of phoneme acquisition by infants. Human infants perceive speech sounds not as discrete phoneme sequences but as continuous acoustic signals. One of critical problems in phoneme acquisition is the design for segmenting these continuous speech sounds. The key idea to solve this problem is that articulatory mechanisms such as the vocal tract help human beings to perceive speech sound units corresponding to phonemes. To segment acoustic signal with articulatory movement, we apply the segmenting method to our system by Recurrent Neural Network with Parametric Bias (RNNPB). This method determines the multiple segmentation boundaries in a temporal sequence using the prediction error of the RNNPB model, and the PB values obtained by the method can be encoded as kind of phonemes. Our system was implemented by using a physical vocal tract model, called the Maeda model. Experimental results demonstrated that our system can self-organize the same phonemes in different continuous sounds, and can imitate vocal sound involving arbitrary numbers of vowels using the vowel space in the RNNPB. This suggests that our model reflects the process of phoneme acquisition.

关键词： Recurrent neural networks

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Semi-Infinite Programming with Complementarity Constraints for Pose Optimization with Pervasive Contact

Semi-Infinite Programming with Complementarity Constraints f...

引用

IEEE international Conference on robotics and automation (ICRA)

作者： Zhang, Mengchao Hauser, Kris Univ Illinois Dept Mech Sci & Engn Urbana IL 61801 USA Univ Illinois Dept Comp Sci Urbana IL USA

ISBN: (纸本)9781728190778

This paper presents a novel computational model to address the problem that contact is an infinite phenomena involving continuous regions of interaction. The problem is cast as a semi-infinite program with complementarity constraints (SIPCC). Rather than pre-discretize contacting surfaces into a finite number of contact points, we use semi-infinite programming (SIP) techniques that operate on the underlying continuous geometry, but dynamically determine a finite number of constraints that are most relevant to solving the problem. Then we solve the series of problems whose solutions converge toward one that contains a true optimum of the original SIPCC. We apply the model to a grasping pose optimization problem for a gripper and a humanoid robot, and our model enables the robots to find a feasible pose to hold (non-)convex objects while ensuring force and torque balance.

关键词： Geometry Torque Conferences computational modeling Force Humanoid robots Grasping

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An Improved Chaotic Particle Swarm Optimization and Its Application in Investment

An Improved Chaotic Particle Swarm Optimization and Its Appl...

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1st international symposium on computational intelligence and Design

作者： Zhang Hao Shen Ji-hong Zhang Tie-nan Li Yang Harbin Engn Univ Coll Automat Harbin 150001 Peoples R China Harbin Engn Univ Coll Sci Harbin 150001 Peoples R China Harbin Engn Univ Sch Econ Management Harbin 150001 Peoples R China

ISBN: (纸本)9780769533117

Particle swarm optimization is a population-based swarm intelligence algorithm driven by the simulation of a social psychological metaphor instead of the survival of the fittest individual. But it's easy to be trapped into local optimum. Based on the chaos theory, the random function is introduced to Tent map, and the improved Tent map is introduced to PSO. Update the velocity and position of the particle by the improved Tent map instead of the random parameters. Eliminate the particle whose position is the farthest to the optimal solution after iterating certain steps, and reestablish the position of new particle according to average value of the positions of all the particles to search again. The algorithm has faster convergence and better global optimization capability. The improved Tent PSO is applied to the investment optimization, and the result of simulation shows better optimization function.

关键词： Chaos Particle swarm optimization Investments Logistics Probability density function computational intelligence Design automation Design engineering Educational institutions Engineering management

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Agile Actions with a Centaur-Type Humanoid: A Decoupled Approach

Agile Actions with a Centaur-Type Humanoid: A Decoupled Appr...

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IEEE international Conference on robotics and automation (ICRA)

作者： Polverini, Matteo Parigi Hoffman, Enrico Mingo Laurenzi, Arturo Tsagarakis, Nikos G. Ist Italiano Tecnol IIT Humanoids & Human Ctr Mechatron Res Line HHCM Genoa Italy

ISBN: (纸本)9781728190778

The kinematic features of a centaur-type humanoid platform, combined with a powerful actuation, enable the experimentation of a variety of agile and dynamic motions. However, the higher number of degrees-of-freedom and the increased weight of the system, compared to the bipedal and quadrupedal counterparts. pose significant research challenges in terms of computational load and real implementation. To this end, this work presents a control architecture to perform agile actions, conceived for torque-controlled platforms, which decouples for computational purposes offline optimal control planning of lower-body primitives, based on a template kinematic model, and online control of the upper-body motion to maintain balance. Three stabilizing strategies are presented, whose performance is compared in two types of simulated jumps, while experimental validation is performed on a half-squat jump using the CENTAURO robot.

关键词： automation Conferences Dynamics Humanoid robots Optimal control Kinematics Computer architecture

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Efficient Real-Time Inference in Temporal Convolution Networks

Efficient Real-Time Inference in Temporal Convolution Networ...

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IEEE international Conference on robotics and automation (ICRA)

作者： Khandelwal, Piyush MacGlashan, James Wurman, Peter Stone, Peter Sony Corp Amer Sony AI 25 Madison Ave New York NY 10010 USA Univ Texas Austin Dept Comp Sci 2317 Speedway Austin TX 78712 USA

ISBN: (纸本)9781728190778

It has been recently demonstrated that Temporal Convolution Networks (TCNs) provide state-of-the-art results in many problem domains where the input data is a time-series. TCNs typically incorporate information from a long history of inputs (the receptive field) into a single output using many convolution layers. Real-time inference using a trained TCN can be challenging on devices with limited compute and memory, especially if the receptive field is large. This paper introduces the RT-TCN algorithm that reuses the output of prior convolution operations to minimize the computational requirements and persistent memory footprint of a TCN during real-time inference. We also show that when a TCN is trained using time slices of the input time-series, it can be executed in real-time continually using RT-TCN. In addition, we provide TCN architecture guidelines that ensure that real-time inference can be performed within memory and computational constraints.

关键词： automation Convolution Conferences Memory management Network architecture Real-time systems Inference algorithms

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Robot Trust and Self-Confidence Based Role Arbitration Method for Physical Human-Robot Collaboration

Robot Trust and Self-Confidence Based Role Arbitration Metho...

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IEEE international Conference on robotics and automation (ICRA)

作者： Wang, Qiao Liu, Dikai Carmichael, Marc G. Lin, Chin-Teng Univ Technol Sydney Robot Inst Fac Engn & Informat Technol 81 Broadway Ultimo NSW 2010 Australia Univ Technol Sydney Australian Artificial Intelligence Inst Sch Comp Sci Fac Engn & Informat Technol Ultimo NSW 2010 Australia

ISBN: (纸本)9798350323658

Role arbitration in human-robot collaboration (HRC) is a dynamically changing process that is affected by many factors such as physical workload, environmental changes and trust. In order to address this dynamic process, a trust-based role arbitration method is studied in this research. A computational model of robot trust and self-confidence (TSC) in physical human-robot collaboration (pHRC) is proposed. The TSC model is defined as a function of objective robot and human co-worker performance. A role arbitration method is then proposed based on the TSC model presented. The human-in-the-loop experiments with a collaborative robot are conducted to verify the TSC-based role arbitration method. The results show that the proposed method could achieve superior human-robot combined performance, reduce human co-workers' workload, and improve subjective preference.

关键词： Collaborative robots

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IMU-Aided Event-based Stereo Visual Odometry

IMU-Aided Event-based Stereo Visual Odometry

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IEEE international Conference on robotics and automation (ICRA)

作者： Niu, Junkai Zhong, Sheng Zhou, Yi Hunan Univ Neuromorph Automat & Intelligence Lab NAIL Sch Robot Changsha Peoples R China

ISBN: (纸本)9798350384581;9798350384574

Direct methods for event-based visual odometry solve the mapping and camera pose tracking sub-problems by establishing implicit data association in a way that the generative model of events is exploited. The main bottlenecks faced by state-of-the-art work in this field include the high computational complexity of mapping and the limited accuracy of tracking. In this paper, we improve our previous direct pipeline Event-based Stereo Visual Odometry in terms of accuracy and efficiency. To speed up the mapping operation, we propose an efficient strategy of edge-pixel sampling according to the local dynamics of events. The mapping performance in terms of completeness and local smoothness is also improved by combining the temporal stereo results and the static stereo results. To circumvent the degeneracy issue of camera pose tracking in recovering the yaw component of general 6-DoF motion, we introduce as a prior the gyroscope measurements via pre-integration. Experiments on publicly available datasets justify our improvement. We release our pipeline as an open-source software for future research in this field.

关键词： Mapping

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A Geometric Folding Pattern for Robot Coverage Path Planning

A Geometric Folding Pattern for Robot Coverage Path Planning

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IEEE international Conference on robotics and automation (ICRA)

作者： Zhu, Lifeng Yao, Shuai Li, Boyang Song, Aiguo Jia, Yiyang Mitani, Jun Southeast Univ State Key Lab Bioelect Jiangsu Key Lab Remote Measurement & Control Sch Instrument Sci & Engn Dhaka Bangladesh Univ Tsukuba Tsukuba Ibaraki Japan China Jiliang Univ Hangzhou Peoples R China Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9781728190778

Conventional coverage path planning algorithms are mainly based on the zigzag and spiral patterns or their combinations. The traversal order is limited by the linear or inside-outside manner. We propose a new set of coverage patterns induced from geometric folding operations, called the geometric folding pattern, to make coverage paths with more flexible traversal order. We study the modeling and parameterization of the geometric folding patterns. Then, a sampling operator is introduced. Based on the computational tools, we demonstrate the application of the proposed patterns in designing coverage paths. We show that the simple geometric folding patterns are flexible and controllable, which enables more choices for the coverage path planning problem.

关键词： Spirals automation Conferences computational modeling Tools Aerospace electronics Data structures

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Dynamically Switching Human Prediction Models for Efficient Planning

Dynamically Switching Human Prediction Models for Efficient ...

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IEEE international Conference on robotics and automation (ICRA)

作者： Sripathy, Arjun Bobu, Andreea Brown, Daniel S. Dragan, Anca D. Univ Calif Berkeley EECS Berkeley CA 94720 USA

ISBN: (纸本)9781728190778

As environments involving both robots and humans become increasingly common, so does the need to account for people during planning. To plan effectively, robots must be able to respond to and sometimes influence what humans do. This requires a human model which predicts future human actions. A simple model may assume the human will continue what they did previously;a more complex one might predict that the human will act optimally, disregarding the robot;whereas an even more complex one might capture the robot's ability to influence the human. These models make different trade-offs between computational time and performance of the resulting robot plan. Using only one model of the human either wastes computational resources or is unable to handle critical situations. In this work, we give the robot access to a suite of human models and enable it to assess the performance-computation trade-off online. By estimating how an alternate model could improve human prediction and how that may translate to performance gain, the robot can dynamically switch human models whenever the additional computation is justified. Our experiments in a driving simulator showcase how the robot can achieve performance comparable to always using the best human model, but with greatly reduced computation.

关键词： automation computational modeling Conferences Switches Predictive models Performance gain Planning

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