Robotics: Science and Systems XIX最新文献

Centralized Model Predictive Control for Collaborative Loco-Manipulation 协同局部操作的集中式模型预测控制

Robotics: Science and Systems XIX Pub Date : 2023-07-10 DOI: 10.15607/RSS.2023.XIX.050

Flavio De Vincenti, Stelian Coros

引用次数: 1

Co-optimization of Morphology and Behavior of Modular Robots via Hierarchical Deep Reinforcement Learning 基于层次深度强化学习的模块化机器人形态与行为协同优化

Robotics: Science and Systems XIX Pub Date : 2023-07-10 DOI: 10.15607/RSS.2023.XIX.096

Jieqiang Sun, Meibao Yao, Xueming Xiao, Zhibing Xie, Bo Zheng

引用次数: 1

Predefined-Time Convergent Motion Control for Heterogeneous Continuum Robots 异构连续体机器人的预定义时间收敛运动控制

Robotics: Science and Systems XIX Pub Date : 2023-07-10 DOI: 10.15607/RSS.2023.XIX.092

Ning Tan, YU Peng, Kai Huang

引用次数: 0

ERASOR2: Instance-Aware Robust 3D Mapping of the Static World in Dynamic Scenes ERASOR2:动态场景中静态世界的实例感知鲁棒3D映射

Robotics: Science and Systems XIX Pub Date : 2023-07-10 DOI: 10.15607/RSS.2023.XIX.067

Hyungtae Lim, Lucas Nunes, Benedikt Mersch, Xieyunali Chen, J. Behley, H. Myung, C. Stachniss

引用次数: 2

Demonstrating A Walk in the Park: Learning to Walk in 20 Minutes With Model-Free Reinforcement Learning 示范在公园散步:用无模型强化学习在20分钟内学会走路

Robotics: Science and Systems XIX Pub Date : 2023-07-10 DOI: 10.15607/RSS.2023.XIX.056

Laura M. Smith, Ilya Kostrikov, S. Levine

引用次数: 26

Self-Supervised Lidar Place Recognition in Overhead Imagery Using Unpaired Data 基于非配对数据的自监督激光雷达位置识别

Robotics: Science and Systems XIX Pub Date : 2023-07-10 DOI: 10.15607/RSS.2023.XIX.098

T. Y. Tang, D. Martini

引用次数: 0

A Sampling-Based Approach for Heterogeneous Coalition Scheduling with Temporal Uncertainty 一种基于抽样的时间不确定性异构联盟调度方法

Robotics: Science and Systems XIX Pub Date : 2023-07-10 DOI: 10.15607/RSS.2023.XIX.107

Andrew Messing, Jacopo Banfi, M. Stadler, Ethan Stump, H. Ravichandar, N. Roy, S. Hutchinson

{"title":"A Sampling-Based Approach for Heterogeneous Coalition Scheduling with Temporal Uncertainty","authors":"Andrew Messing, Jacopo Banfi, M. Stadler, Ethan Stump, H. Ravichandar, N. Roy, S. Hutchinson","doi":"10.15607/RSS.2023.XIX.107","DOIUrl":"https://doi.org/10.15607/RSS.2023.XIX.107","url":null,"abstract":"—Scheduling algorithms for real-world heterogeneous multi-robot teams must be able to reason about temporal uncertainty in the world model in order to create plans that are tolerant to the risk of unexpected delays. To this end, we present a novel sampling-based risk-aware approach for solving Heterogeneous Coalition Scheduling with Temporal Uncertainty (HCSTU) problems, which does not require any assumptions regarding the specific underlying cause of the temporal uncertainty or the specific duration distributions. Our approach computes a schedule which obeys the temporal constraints of a small number of heuristically-selected sample scenarios by solving a Mixed-Integer Linear Program, along with an upper bound on the schedule execution time. Then, it uses a hypothesis testing method, the Sequential Probability Ratio Test, to provide a probabilistic guarantee that the upper bound on the execu- tion time will be respected for a user-specified risk tolerance. With extensive experiments, we demonstrate that our approach empirically respects the risk tolerance, and generates solutions of comparable or better quality than state-of-the-art approaches while being an order of magnitude faster to compute on average. Finally, we demonstrate how robust schedules generated by our approach can be incorporated as solutions to subproblems within the broader Simultaneous Task Allocation and Planning with Spatiotemporal Constraints problem to both guide and expedite the search for solutions of higher quality and lower risk.","PeriodicalId":248720,"journal":{"name":"Robotics: Science and Systems XIX","volume":"3 s4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113957527","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

SAR: Generalization of Physiological Dexterity via Synergistic Action Representation SAR:通过协同动作表征的生理灵巧的泛化

Robotics: Science and Systems XIX Pub Date : 2023-07-10 DOI: 10.15607/RSS.2023.XIX.007

C. Berg, V. Caggiano, Vikash Kumar

{"title":"SAR: Generalization of Physiological Dexterity via Synergistic Action Representation","authors":"C. Berg, V. Caggiano, Vikash Kumar","doi":"10.15607/RSS.2023.XIX.007","DOIUrl":"https://doi.org/10.15607/RSS.2023.XIX.007","url":null,"abstract":"—Learning effective continuous control policies in high-dimensional systems, including musculoskeletal agents, remains a significant challenge. Over the course of biological evolution, organisms have developed robust mechanisms for over-coming this complexity to learn highly sophisticated strategies for motor control. What accounts for this robust behavioral flexibility? Modular control via muscle synergies, i.e. coordinated muscle co-contractions, is considered to be one putative mechanism that enables organisms to learn muscle control in a simplified and generalizable action space. Drawing inspiration from this evolved motor control strategy, we use a physiologically accurate hand model to investigate whether leveraging a Synergistic Action Representation ( SAR ) acquired from simpler manipulation tasks improves learning and generalization on more complex tasks. We find that SAR -exploiting policies trained on a complex, 100- object randomized reorientation task significantly outperformed ( > 70 % success) baseline approaches ( < 20 % success). Notably, SAR -exploiting policies were also found to zero-shot generalize to thousands of unseen objects with out-of-domain size variations, while policies that did not adopt SAR failed to generalize. SAR also enabled significantly improved transfer learning on real-world objects. Finally, using a robotic manipulation task set and a full-body humanoid locomotion task, we establish the generality of SAR on broader high-dimensional control problems, achieving SOTA performance with an order of magnitude improved sample efficiency. To the best of our knowledge, this investigation is the first of its kind to present an end-to-end pipeline for discovering synergies and using this representation to learn high-dimensional continuous control across a wide diversity of tasks","PeriodicalId":248720,"journal":{"name":"Robotics: Science and Systems XIX","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126155431","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

Adaptive Tracking Control of Dielectric Elastomer Soft Actuators with Viscoelastic Hysteresis Compensation 粘弹性滞后补偿的介电弹性体软执行器自适应跟踪控制

Robotics: Science and Systems XIX Pub Date : 2023-07-10 DOI: 10.15607/RSS.2023.XIX.093

Yunhua Zhao, L. Wen

引用次数: 0

ROSE: Rotation-based Squeezing Robotic Gripper toward Universal Handling of Objects ROSE:面向物体通用处理的旋转挤压机器人抓取器

Robotics: Science and Systems XIX Pub Date : 2023-07-10 DOI: 10.15607/RSS.2023.XIX.090

S. T. Bui, Shinya Kawano, V. A. Ho

{"title":"ROSE: Rotation-based Squeezing Robotic Gripper toward Universal Handling of Objects","authors":"S. T. Bui, Shinya Kawano, V. A. Ho","doi":"10.15607/RSS.2023.XIX.090","DOIUrl":"https://doi.org/10.15607/RSS.2023.XIX.090","url":null,"abstract":"—Robotics hand/grippers nowadays are not limited to manufacturing lines; instead, they are widely utilized in cluttered environments, such as restaurants, farms, and warehouses. In such scenarios, they need to deal with high uncertainty of the grasped objects’ shapes, postures, surfaces, and material properties, which requires complex integration of sensing and decision-making process. On the other hand, integrating soft materials into the gripper’s design may tolerate the above uncertainties and reduce complexity in control. In this paper, we introduce ROSE , a novel soft gripper that can embrace the object and squeeze it by buckling a funnel-liked thin-walled soft membrane around the object by simple rotation of the base. Thanks to this design, ROSE hand can adapt to a wide range of objects that can fit in the funnel and handle with gentle gripping force. Regardless of this, ROSE can generate a high lift force (up to 33kgf) while significantly reducing the normal pressure on the gripped objects. In our experiment, a 198g ROSE can be integrated into a robot arm with a single actuation and successfully lift various types of objects, even after 400,000 trials. The embracing mechanism helps reduce the dependence of friction between the object and the membrane, as ROSE could pick up a chicken egg submerged inside an olive oil tank. We also report a feasible design for equipping the ROSE hand with tactile sensing while appealing to the scalability of the design to fit a wide range of objects. Video: https://youtu.be/E1wAI09LaoY","PeriodicalId":248720,"journal":{"name":"Robotics: Science and Systems XIX","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116933733","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