2022 IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids)最新文献_第10页

Multi-Modal Manipulation Planning for an Upper-Torso Humanoid System 上躯干类人系统的多模态操纵规划

2022 IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids) Pub Date : 2022-11-28 DOI: 10.1109/Humanoids53995.2022.10000144

S. Natarajan, F. Kirchner

{"title":"Multi-Modal Manipulation Planning for an Upper-Torso Humanoid System","authors":"S. Natarajan, F. Kirchner","doi":"10.1109/Humanoids53995.2022.10000144","DOIUrl":"https://doi.org/10.1109/Humanoids53995.2022.10000144","url":null,"abstract":"Motion planning problems for manipulators are often considered and planned on a single submanifold. To solve a more challenging manipulation task, the manipulator needs to use both prehensile and non-prehensile manipulation actions to solve the task. Non-Prehensile manipulation actions operate on a lower dimension compared to prehensile manipulation actions. In this work, we propose a multi-modal motion planner for tree kinematic structure robotic systems, which plans through different submanifolds to provide a continuous trajectory to fulfill the given task. As both manipulators share the same torso joints, a movement involving the torso and one of the manipulators often causes an undesired behavior in the second manipulator, such as being in a collision state or an object that cannot be received from the second manipulator. A multi-task propagation function is developed to find optimal joint angles for the tree kinematic structure while propagating on the submanifolds of the planning space. This planner is evaluated and tested on an upper-torso humanoid system in two different scenarios. The results show that our planner achieved a higher success rate and needs less planning time compared to a planner which uses dual-arm manipulators mounted on a rigid body.","PeriodicalId":180816,"journal":{"name":"2022 IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids)","volume":"30 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116377961","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: 0

Research and Design of a Humanoid Cushioning Foot for Robot Jumping 机器人跳跃类人缓冲足的研究与设计

2022 IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids) Pub Date : 2022-11-28 DOI: 10.1109/Humanoids53995.2022.10000193

Chuanku Yi, Xuechao Chen, Zhangguo Yu, Haoxiang Qi, Qiang Huang

引用次数: 1

Utilizing the Natural Dynamics of Elastic Legged Robots for Periodic Jumping Motions 利用弹性腿机器人的自然动力学进行周期性跳跃运动

2022 IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids) Pub Date : 2022-11-28 DOI: 10.1109/Humanoids53995.2022.10000146

F. Beck, Maximilian Rehermann, J. Reger, C. Ott

引用次数: 0

A Feasibility Study of a Data-Driven Human-Robot Conversational Interface for Reminiscence Therapy 数据驱动的人机对话界面用于回忆治疗的可行性研究

2022 IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids) Pub Date : 2022-11-28 DOI: 10.1109/Humanoids53995.2022.10000119

Nathalia Céspedes, Anne Hsu, Janelle M. Jones, I. Farkhatdinov

引用次数: 0

Optimization of Humanoid Robot Designs for Human-Robot Ergonomic Payload Lifting 面向人机工效载荷提升的仿人机器人优化设计

2022 IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids) Pub Date : 2022-11-24 DOI: 10.1109/Humanoids53995.2022.10000222

Carlotta Sartore, Lorenzo Rapetti, D. Pucci

{"title":"Optimization of Humanoid Robot Designs for Human-Robot Ergonomic Payload Lifting","authors":"Carlotta Sartore, Lorenzo Rapetti, D. Pucci","doi":"10.1109/Humanoids53995.2022.10000222","DOIUrl":"https://doi.org/10.1109/Humanoids53995.2022.10000222","url":null,"abstract":"When a human and a humanoid robot collaborate physically, ergonomics is a key factor to consider. Assuming a given humanoid robot, several control architectures exist nowadays to address ergonomic physical human-robot collaboration. This paper takes one step further by considering robot hardware parameters as optimization variables in the problem of collaborative payload lifting. The variables that parametrize robot's kinematics and dynamics ensure their physical consistency, and the human model is considered in the optimization problem. By leveraging the proposed modelling framework, the ergonomy of the interaction is maximized, here given by the agents' energy expenditure. Robot kinematic, dynamics, hardware constraints and human geometries are considered when solving the associated optimization problem. The proposed methodology is used to identify optimum hardware parameters for the design of the ergoCub robot, a humanoid possessing a degree of embodied intelligence for ergonomic interaction with humans. For the optimization problem, the starting point is the iCub humanoid robot. The obtained robot design reaches loads at heights in the range of 0.8 - 1.5 m with respect to the iCub robot whose range is limited to 0.8-1.2 m. The robot energy expenditure is decreased by about 33%, meanwhile, the human ergonomy is preserved, leading overall to an improved interaction.","PeriodicalId":180816,"journal":{"name":"2022 IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids)","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128105469","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: 2

Whole-Body Trajectory Optimization for Robot Multimodal Locomotion 机器人多模态运动的全身轨迹优化

2022 IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids) Pub Date : 2022-11-23 DOI: 10.1109/Humanoids53995.2022.10000241

Giuseppe L’Erario, Gabriele Nava, Giulio Romualdi, Fabio Bergonti, V. Razza, Stefano Dafarra, D. Pucci

{"title":"Whole-Body Trajectory Optimization for Robot Multimodal Locomotion","authors":"Giuseppe L’Erario, Gabriele Nava, Giulio Romualdi, Fabio Bergonti, V. Razza, Stefano Dafarra, D. Pucci","doi":"10.1109/Humanoids53995.2022.10000241","DOIUrl":"https://doi.org/10.1109/Humanoids53995.2022.10000241","url":null,"abstract":"The general problem of planning feasible trajectories for multimodal robots is still an open challenge. This paper presents a whole-body trajectory optimisation approach that addresses this challenge by combining methods and tools developed for aerial and legged robots. First, robot models that enable the presented whole-body trajectory optimisation framework are presented. The key model is the so-called robot centroidal momentum, the dynamics of which is directly related to the models of the robot actuation for aerial and terrestrial locomotion. Then, the paper presents how these models can be employed in an optimal control problem to generate either terrestrial or aerial locomotion trajectories with a unified approach. The optimisation problem considers robot kinematics, momentum, thrust forces and their bounds. The overall approach is validated using the multimodal robot iRonCub, a flying humanoid robot that expresses a degree of terrestrial and aerial locomotion. To solve the associated optimal trajectory generation problem, we employ ADAM, a custom-made open-source library that implements a collection of algorithms for calculating rigid-body dynamics using CasADi.","PeriodicalId":180816,"journal":{"name":"2022 IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132790060","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: 2

Affordance detection with Dynamic-Tree Capsule Networks 动态树胶囊网络的可视性检测

2022 IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids) Pub Date : 2022-11-09 DOI: 10.1109/Humanoids53995.2022.10000190

A. Rodr'iguez-S'anchez, Simon Haller-Seeber, David Peer, Chris Engelhardt, Jakob Mittelberger, Matteo Saveriano

{"title":"Affordance detection with Dynamic-Tree Capsule Networks","authors":"A. Rodr'iguez-S'anchez, Simon Haller-Seeber, David Peer, Chris Engelhardt, Jakob Mittelberger, Matteo Saveriano","doi":"10.1109/Humanoids53995.2022.10000190","DOIUrl":"https://doi.org/10.1109/Humanoids53995.2022.10000190","url":null,"abstract":"Affordance detection from visual input is a fundamental step in autonomous robotic manipulation. Existing solutions to the problem of affordance detection rely on convolutional neural networks. However, these networks do not consider the spatial arrangement of the input data and miss parts-to-whole relationships. Therefore, they fall short when confronted with novel, previously unseen object instances or new viewpoints. One solution to overcome such limitations can be to resort to capsule networks. In this paper, we introduce the first affordance detection network based on dynamic treestructured capsules for sparse 3D point clouds. We show that our capsule-based network outperforms current state-of-the-art models on viewpoint invariance and parts-segmentation of new object instances through a novel dataset we only used for evaluation and it is publicly available from github.com/gipfelen/DTCG-Net. In the experimental evaluation we will show that our algorithm is superior to current affordance detection methods when faced with grasping previously unseen objects thanks to our Capsule Network enforcing a parts-to-whole representation.","PeriodicalId":180816,"journal":{"name":"2022 IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121535328","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: 0

Enhanced Visual Feedback with Decoupled Viewpoint Control in Immersive Humanoid Robot Teleoperation using SLAM 基于SLAM的沉浸式人形机器人遥操作增强视觉反馈解耦视点控制

2022 IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids) Pub Date : 2022-11-03 DOI: 10.1109/Humanoids53995.2022.9999740

Yang Chen, Leyuan Sun, M. Benallegue, Rafael Cisneros, R. P. Singh, K. Kaneko, Arnaud Tanguy, Guillaume Caron, Kenji Suzuki, A. Kheddar, F. Kanehiro

{"title":"Enhanced Visual Feedback with Decoupled Viewpoint Control in Immersive Humanoid Robot Teleoperation using SLAM","authors":"Yang Chen, Leyuan Sun, M. Benallegue, Rafael Cisneros, R. P. Singh, K. Kaneko, Arnaud Tanguy, Guillaume Caron, Kenji Suzuki, A. Kheddar, F. Kanehiro","doi":"10.1109/Humanoids53995.2022.9999740","DOIUrl":"https://doi.org/10.1109/Humanoids53995.2022.9999740","url":null,"abstract":"In immersive humanoid robot teleoperation, there are three main shortcomings that can alter the transparency of the visual feedback: (i) the lag between the motion of the operator's and robot's head due to network communication delays or slow robot joint motion. This latency could cause a noticeable delay in the visual feedback, which jeopardizes the embodiment quality, can cause dizziness, and affects the interactivity resulting in operator frequent motion pauses for the visual feedback to settle; (ii) the mismatch between the camera's and the headset's field-of-views (FOV), the former having generally a lower FOV; and (iii) a mismatch between human's and robot's range of motions of the neck, the latter being also generally lower. In order to leverage these draw-backs, we developed a decoupled viewpoint control solution for a humanoid platform which allows visual feedback with low-latency and artificially increases the camera's FOV range to match that of the operator's headset. Our novel solution uses SLAM technology to enhance the visual feedback from a reconstructed mesh, complementing the areas that are not covered by the visual feedback from the robot. The visual feedback is presented as a point cloud in real-time to the operator. As a result, the operator is fed with real-time vision from the robot's head orientation by observing the pose of the point cloud. Balancing this kind of awareness and immersion is important in virtual reality based teleoperation, considering the safety and robustness of the control system. An experiment shows that the effectiveness of our solution.","PeriodicalId":180816,"journal":{"name":"2022 IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116055364","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: 0

Torque Controlled Locomotion of a Biped Robot with Link Flexibility 具有柔性连杆的双足机器人的力矩控制运动

2022 IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids) Pub Date : 2022-10-27 DOI: 10.1109/Humanoids53995.2022.10000135

N. Villa, Pierre Fernbach, M. Naveau, Guilhem Saurel, Ewen Dantec, N. Mansard, O. Stasse

{"title":"Torque Controlled Locomotion of a Biped Robot with Link Flexibility","authors":"N. Villa, Pierre Fernbach, M. Naveau, Guilhem Saurel, Ewen Dantec, N. Mansard, O. Stasse","doi":"10.1109/Humanoids53995.2022.10000135","DOIUrl":"https://doi.org/10.1109/Humanoids53995.2022.10000135","url":null,"abstract":"When a big and heavy robot moves, it exerts large forces on the environment and on its own structure, its angular momentum can vary substantially, and even the robot's structure can deform if there is a mechanical weakness. Under these conditions, standard locomotion controllers can fail easily. In this article, we propose a complete control scheme to work with heavy robots in torque control. The full centroidal dynamics is used to generate walking gaits online, link deflections are taken into account to estimate the robot posture and all postural instructions are designed to avoid conflicting with each other, improving balance. These choices reduce model and control errors, allowing our centroidal stabilizer to compensate for the remaining residual errors. The stabilizer and motion generator are designed together to ensure feasibility under the assumption of bounded errors. We deploy this scheme to control the locomotion of the humanoid robot Talos, whose hip links flex when walking. It allows us to reach steps of 35 cm, for an average speed of 25 cm/sec, which is among the best performances so far for torque-controlled electric robots.","PeriodicalId":180816,"journal":{"name":"2022 IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids)","volume":"133 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116453709","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: 2

MILD: Multimodal Interactive Latent Dynamics for Learning Human-Robot Interaction 学习人机交互的多模态交互潜在动力学

2022 IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids) Pub Date : 2022-10-22 DOI: 10.1109/Humanoids53995.2022.10000239

V. Prasad, Dorothea Koert, R. Stock-Homburg, Jan Peters, G. Chalvatzaki

{"title":"MILD: Multimodal Interactive Latent Dynamics for Learning Human-Robot Interaction","authors":"V. Prasad, Dorothea Koert, R. Stock-Homburg, Jan Peters, G. Chalvatzaki","doi":"10.1109/Humanoids53995.2022.10000239","DOIUrl":"https://doi.org/10.1109/Humanoids53995.2022.10000239","url":null,"abstract":"Modeling interaction dynamics to generate robot trajectories that enable a robot to adapt and react to a human's actions and intentions is critical for efficient and effective collaborative Human-Robot Interactions (HRI). Learning from Demonstration (LfD) methods from Human-Human Interactions (HHI) have shown promising results, especially when coupled with representation learning techniques. However, such methods for learning HRI either do not scale well to high dimensional data or cannot accurately adapt to changing via-poses of the interacting partner. We propose Multimodal Interactive Latent Dynamics (MILD), a method that couples deep representation learning and probabilistic machine learning to address the problem of two-party physical HRIs. We learn the interaction dynamics from demonstrations, using Hidden Semi-Markov Models (HSMMs) to model the joint distribution of the interacting agents in the latent space of a Variational Autoencoder (VAE). Our experimental evaluations for learning HRI from HHI demonstrations show that MILD effectively captures the multimodality in the latent representations of HRI tasks, allowing us to decode the varying dynamics occurring in such tasks. Compared to related work, MILD generates more accurate trajectories for the controlled agent (robot) when conditioned on the observed agent's (human) trajectory. Notably, MILD can learn directly from camera-based pose estimations to generate trajectories, which we then map to a humanoid robot without the need for any additional training. Supplementary Material: https://bit.ly/MILD-HRI.","PeriodicalId":180816,"journal":{"name":"2022 IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115331613","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: 4