2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids)最新文献_第4页

Design of a force sensing hand for the R1 humanoid robot R1类人机器人力感手的设计

2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids) Pub Date : 2017-11-01 DOI: 10.1109/HUMANOIDS.2017.8246949

A. V. Sureshbabu, M. Maggiali, G. Metta, A. Parmiggiani

引用次数: 5

Legged mechanism design with momentum gains 具有动量增益的腿式机构设计

2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids) Pub Date : 2017-11-01 DOI: 10.1109/HUMANOIDS.2017.8246932

Brandon J. DeHart, D. Kulić

引用次数: 2

Efficient online adaptation with stochastic recurrent neural networks 随机递归神经网络的有效在线自适应

2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids) Pub Date : 2017-11-01 DOI: 10.1109/HUMANOIDS.2017.8246875

Daniel Tanneberg, Jan Peters, E. Rückert

引用次数: 3

Database-driven approach for Biosignal-based robot control with collaborative filtering 基于生物信号的机器人协同滤波控制的数据库驱动方法

2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids) Pub Date : 2017-11-01 DOI: 10.1109/HUMANOIDS.2017.8246934

J. Furukawa, Asuka Takai, J. Morimoto

{"title":"Database-driven approach for Biosignal-based robot control with collaborative filtering","authors":"J. Furukawa, Asuka Takai, J. Morimoto","doi":"10.1109/HUMANOIDS.2017.8246934","DOIUrl":"https://doi.org/10.1109/HUMANOIDS.2017.8246934","url":null,"abstract":"In this study, we propose a databasedriven torque estimation approach for EMG-based robot control. For conventional EMG-based controllers, torque estimation models need to be carefully calibrated to control robots that have multiple degrees of freedom. However, such a calibration procedure requires significant effort and restricts the applications of EMG-based methods to practical situations. To cope with this issue, we use large-scale data acquired from other users to avoid the calibration process and propose collaborative filtering to estimate the joint torque of a new user by exploiting the previously derived relationships between the EMG signals and the joint torque of other users. To validate our proposed method, we compared the joint torque estimation performance with a standard linear conversion model. In our experiments, we controlled an upper-limb exoskeleton robot with the estimated joint torque where we used 16-ch electrodes to measure the EMG signals of subjects. In a comparison, our proposed method showed comparable control performance with the standard approach that requires a careful calibration process.","PeriodicalId":143992,"journal":{"name":"2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126063633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Understanding movements of hand-over between two persons to improve humanoid robot systems 了解两人之间的交接动作，改进仿人机器人系统

2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids) Pub Date : 2017-11-01 DOI: 10.1109/HUMANOIDS.2017.8246972

Robin Rasch, S. Wachsmuth, Matthias König

引用次数: 7

Approximate hybrid model predictive control for multi-contact push recovery in complex environments 复杂环境下多触点推力恢复的近似混合模型预测控制

2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids) Pub Date : 2017-11-01 DOI: 10.1109/HUMANOIDS.2017.8239534

Tobia Marcucci, Robin Deits, M. Gabiccini, A. Bicchi, Russ Tedrake

{"title":"Approximate hybrid model predictive control for multi-contact push recovery in complex environments","authors":"Tobia Marcucci, Robin Deits, M. Gabiccini, A. Bicchi, Russ Tedrake","doi":"10.1109/HUMANOIDS.2017.8239534","DOIUrl":"https://doi.org/10.1109/HUMANOIDS.2017.8239534","url":null,"abstract":"Feedback control of robotic systems interacting with the environment through contacts is a central topic in legged robotics. One of the main challenges posed by this problem is the choice of a model sufficiently complex to capture the discontinuous nature of the dynamics but simple enough to allow online computations. Linear models have proved to be the most effective and reliable choice for smooth systems; we believe that piecewise affine (PWA) models represent their natural extension when contact phenomena occur. Discrete-time PWA systems have been deeply analyzed in the field of hybrid Model Predictive Control (MPC), but the straightforward application of MPC techniques to complex systems, such as a humanoid robot, leads to mixed-integer optimization problems which are not solvable at real-time rates. Explicit MPC methods can construct the entire control policy offline, but the resulting policy becomes too complex to compute for systems at the scale of a humanoid robot. In this paper we propose a novel algorithm which splits the computational burden between an offline sampling phase and a limited number of online convex optimizations, enabling the application of hybrid predictive controllers to higher-dimensional systems. In doing so we are willing to partially sacrifice feedback optimality, but we set stability of the system as an inviolable requirement. Simulation results of a simple planar humanoid that balances by making contact with its environment are presented to validate the proposed controller.","PeriodicalId":143992,"journal":{"name":"2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130345051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 58

Unified humanoid manipulation of an object of unknown mass properties and friction based on online constraint estimation 基于在线约束估计的未知质量和摩擦物体的统一仿人操纵

2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids) Pub Date : 2017-11-01 DOI: 10.1109/HUMANOIDS.2017.8246882

Shunichi Nozawa, Masaki Murooka, Shintaro Noda, Kunio Kojima, Yuta Kojio, Youhei Kakiuchi, K. Okada, M. Inaba

引用次数: 1

Estimating hand and foot reaction forces based on a generalized zero moment point for rehabilitation assist system 基于广义零力矩点的康复辅助系统手足反力估计

2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids) Pub Date : 2017-11-01 DOI: 10.1109/HUMANOIDS.2017.8246887

Kunihiro Ogata, Hideyuki Tanaka, Y. Matsumoto

引用次数: 0

Robot control for dummies: Insights and examples using OpenSoT 假人机器人控制:使用OpenSoT的见解和示例

2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids) Pub Date : 2017-11-01 DOI: 10.1109/HUMANOIDS.2017.8246954

E. Hoffman, A. Rocchi, Arturo Laurenzi, N. Tsagarakis

引用次数: 47

First-order-principles-based constructive network topologies: An application to robot inverse dynamics 基于一阶原理的构造网络拓扑:在机器人逆动力学中的应用

2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids) Pub Date : 2017-11-01 DOI: 10.1109/HUMANOIDS.2017.8246910

F. Ledezma, S. Haddadin

{"title":"First-order-principles-based constructive network topologies: An application to robot inverse dynamics","authors":"F. Ledezma, S. Haddadin","doi":"10.1109/HUMANOIDS.2017.8246910","DOIUrl":"https://doi.org/10.1109/HUMANOIDS.2017.8246910","url":null,"abstract":"Modeling physical systems with neural networks (NN) requires expert architects to determine the best number of nodes, layers and activation functions. For complex systems, such as articulated robots, reported results are limited in accuracy and generalization capabilities. In this work, we introduce the concept FOPnet. It is based on first-order principles and system knowledge to determine topologies of parametrized operator networks that accurately model input-output mappings of physical systems. These topologies consist of meaningful building elements and connections as well as a reduced number of parameters that describe the variables' interdependencies. In this way, learning speed is boosted and the model's accuracy, precision and generalization power improved. We apply the methodology to a 7 degrees-of-freedom LWR4 manipulator and discuss the estimation and generalization capabilities of the network. Results are compared to conventional Feed Forward NN as well as a state-of-the-art Deep Recurrent NN. For the considered complex robot dynamics FOPnet was able to achieve a seven orders of magnitude smaller generalization RMSE.","PeriodicalId":143992,"journal":{"name":"2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123762977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 29