Pub Date : 2023-07-10DOI: 10.15607/RSS.2023.XIX.050
Flavio De Vincenti, Stelian Coros
—In this work, we extend the model predictive control methods developed in the legged robotics literature to collaborative loco-manipulation settings. The systems we study entail a payload collectively carried by multiple quadruped robots equipped with a mechanical arm. We use a direct multiple shooting method to solve the resulting high-dimensional, optimal control problems for trajectories of ground reaction forces, manipulation wrenches, and stepping locations. To capture the dominant dynamics of the system, we model each agent and the shared payload as single rigid bodies. We demonstrate the versatility of our framework in a series of simulation experiments involving collaborative manipulation over challenging terrains.
{"title":"Centralized Model Predictive Control for Collaborative Loco-Manipulation","authors":"Flavio De Vincenti, Stelian Coros","doi":"10.15607/RSS.2023.XIX.050","DOIUrl":"https://doi.org/10.15607/RSS.2023.XIX.050","url":null,"abstract":"—In this work, we extend the model predictive control methods developed in the legged robotics literature to collaborative loco-manipulation settings. The systems we study entail a payload collectively carried by multiple quadruped robots equipped with a mechanical arm. We use a direct multiple shooting method to solve the resulting high-dimensional, optimal control problems for trajectories of ground reaction forces, manipulation wrenches, and stepping locations. To capture the dominant dynamics of the system, we model each agent and the shared payload as single rigid bodies. We demonstrate the versatility of our framework in a series of simulation experiments involving collaborative manipulation over challenging terrains.","PeriodicalId":248720,"journal":{"name":"Robotics: Science and Systems XIX","volume":"8 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":"126701030","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}
Pub Date : 2023-07-10DOI: 10.15607/RSS.2023.XIX.096
Jieqiang Sun, Meibao Yao, Xueming Xiao, Zhibing Xie, Bo Zheng
—Modular robots hold the promise of changing their shape and even dimension to adapt to various tasks and environments. To realize this superiority, it is essential to find the appropriate morphology and its corresponding behavior simultaneously to ensure optimality of the reconfigura- tion. However, achieving co-optimization is challenging because robotic configuration and motion are interactive and coupled with each other, as well as their optimization processes. To this end, we proposed a co-optimization framework based on hierarchical Deep Reinforcement Learning (DRL), consisting of a configuration model and a motion model based on the Twin Delayed Deep Deterministic policy gradient algorithm (TD3). The two network models update asynchronously with a shared reward to ensure co-optimality. We conduct simulations and experiments with the Webots platform to validate the proposed framework, and the preliminary results show that it yields high quality optimization schemes and thus allows modular robots to be more adaptive to dynamic and multi-task scenarios.
{"title":"Co-optimization of Morphology and Behavior of Modular Robots via Hierarchical Deep Reinforcement Learning","authors":"Jieqiang Sun, Meibao Yao, Xueming Xiao, Zhibing Xie, Bo Zheng","doi":"10.15607/RSS.2023.XIX.096","DOIUrl":"https://doi.org/10.15607/RSS.2023.XIX.096","url":null,"abstract":"—Modular robots hold the promise of changing their shape and even dimension to adapt to various tasks and environments. To realize this superiority, it is essential to find the appropriate morphology and its corresponding behavior simultaneously to ensure optimality of the reconfigura- tion. However, achieving co-optimization is challenging because robotic configuration and motion are interactive and coupled with each other, as well as their optimization processes. To this end, we proposed a co-optimization framework based on hierarchical Deep Reinforcement Learning (DRL), consisting of a configuration model and a motion model based on the Twin Delayed Deep Deterministic policy gradient algorithm (TD3). The two network models update asynchronously with a shared reward to ensure co-optimality. We conduct simulations and experiments with the Webots platform to validate the proposed framework, and the preliminary results show that it yields high quality optimization schemes and thus allows modular robots to be more adaptive to dynamic and multi-task scenarios.","PeriodicalId":248720,"journal":{"name":"Robotics: Science and Systems XIX","volume":"33 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":"123663985","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}
Pub Date : 2023-07-10DOI: 10.15607/RSS.2023.XIX.092
Ning Tan, YU Peng, Kai Huang
As research into continuum robots flourishes, there are more and more types of continuum robots, which require researchers to tirelessly design robot-specific motion control algorithms. Besides, the convergence time of control systems for continuum robots has received very little attention. In this paper, we propose a novel predefined-time convergent zeroing dynamics (PTCZD) model, which ensures that the associated error-monitoring function converges to zero in predefined-time. Based on the PTCZD model, we design an inverse kinematics solver and a state estimator for continuum robots, thereby obtaining a generic predefined-time convergent control method for heterogeneous continuum robots for the first time. Simulations and experiments based on cable-driven continuum robots and concentric tube continuum robots are performed to verify the efficacy, robustness and adaptability of the proposed control method. In addition, comparative studies are carried out to demonstrate its advantages against existing control methods for continuum robots.
{"title":"Predefined-Time Convergent Motion Control for Heterogeneous Continuum Robots","authors":"Ning Tan, YU Peng, Kai Huang","doi":"10.15607/RSS.2023.XIX.092","DOIUrl":"https://doi.org/10.15607/RSS.2023.XIX.092","url":null,"abstract":"As research into continuum robots flourishes, there are more and more types of continuum robots, which require researchers to tirelessly design robot-specific motion control algorithms. Besides, the convergence time of control systems for continuum robots has received very little attention. In this paper, we propose a novel predefined-time convergent zeroing dynamics (PTCZD) model, which ensures that the associated error-monitoring function converges to zero in predefined-time. Based on the PTCZD model, we design an inverse kinematics solver and a state estimator for continuum robots, thereby obtaining a generic predefined-time convergent control method for heterogeneous continuum robots for the first time. Simulations and experiments based on cable-driven continuum robots and concentric tube continuum robots are performed to verify the efficacy, robustness and adaptability of the proposed control method. In addition, comparative studies are carried out to demonstrate its advantages against existing control methods for continuum robots.","PeriodicalId":248720,"journal":{"name":"Robotics: Science and Systems XIX","volume":"2 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":"121785894","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}
Pub Date : 2023-07-10DOI: 10.15607/RSS.2023.XIX.067
Hyungtae Lim, Lucas Nunes, Benedikt Mersch, Xieyunali Chen, J. Behley, H. Myung, C. Stachniss
—A map of the environment is an essential component for robotic navigation. In the majority of cases, a map of the static part of the world is the basis for localization, planning, and navigation. However, dynamic objects that are presented in the scenes during mapping leave undesirable traces in the map, which can impede mobile robots from achieving successful robotic navigation. To remove the artifacts caused by dynamic objects in the map, we propose a novel instance-aware map building method. Our approach rejects dynamic points at an instance-level while preserving most static points by exploiting instance segmentation estimates. Furthermore, we propose effective ways to consider the erroneous estimates of instance segmentation, enabling our proposed method to be robust even under imprecise instance segmentation. As demonstrated in our experimental evaluation, our approach shows substantial performance increases in terms of both, the preservation of static points and rejection of dynamic points. Our code is available at https://github.com/url-kaist/ERASOR2 .
{"title":"ERASOR2: Instance-Aware Robust 3D Mapping of the Static World in Dynamic Scenes","authors":"Hyungtae Lim, Lucas Nunes, Benedikt Mersch, Xieyunali Chen, J. Behley, H. Myung, C. Stachniss","doi":"10.15607/RSS.2023.XIX.067","DOIUrl":"https://doi.org/10.15607/RSS.2023.XIX.067","url":null,"abstract":"—A map of the environment is an essential component for robotic navigation. In the majority of cases, a map of the static part of the world is the basis for localization, planning, and navigation. However, dynamic objects that are presented in the scenes during mapping leave undesirable traces in the map, which can impede mobile robots from achieving successful robotic navigation. To remove the artifacts caused by dynamic objects in the map, we propose a novel instance-aware map building method. Our approach rejects dynamic points at an instance-level while preserving most static points by exploiting instance segmentation estimates. Furthermore, we propose effective ways to consider the erroneous estimates of instance segmentation, enabling our proposed method to be robust even under imprecise instance segmentation. As demonstrated in our experimental evaluation, our approach shows substantial performance increases in terms of both, the preservation of static points and rejection of dynamic points. Our code is available at https://github.com/url-kaist/ERASOR2 .","PeriodicalId":248720,"journal":{"name":"Robotics: Science and Systems XIX","volume":"19 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":"117227949","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}
Pub Date : 2023-07-10DOI: 10.15607/RSS.2023.XIX.056
Laura M. Smith, Ilya Kostrikov, S. Levine
—Deep reinforcement learning is a promising ap- proach to learning policies in unstructured environments. Due to its sample inefficiency, though, deep RL applications have primarily focused on simulated environments. In this work, we demonstrate that the recent advancements in machine learning algorithms and libraries combined with careful MDP formulation lead to learning quadruped locomotion in only 20 minutes in the real world. We evaluate our approach on several indoor and outdoor terrains that are known to be challenging for classical, model-based controllers and observe that the robot consistently learns a walking gait on all of these terrains. Finally, we evaluate our design decisions in a simulated environment. We provide videos of all real-world training and code to reproduce our results on our website: https://sites.google.com/berkeley.
{"title":"Demonstrating A Walk in the Park: Learning to Walk in 20 Minutes With Model-Free Reinforcement Learning","authors":"Laura M. Smith, Ilya Kostrikov, S. Levine","doi":"10.15607/RSS.2023.XIX.056","DOIUrl":"https://doi.org/10.15607/RSS.2023.XIX.056","url":null,"abstract":"—Deep reinforcement learning is a promising ap- proach to learning policies in unstructured environments. Due to its sample inefficiency, though, deep RL applications have primarily focused on simulated environments. In this work, we demonstrate that the recent advancements in machine learning algorithms and libraries combined with careful MDP formulation lead to learning quadruped locomotion in only 20 minutes in the real world. We evaluate our approach on several indoor and outdoor terrains that are known to be challenging for classical, model-based controllers and observe that the robot consistently learns a walking gait on all of these terrains. Finally, we evaluate our design decisions in a simulated environment. We provide videos of all real-world training and code to reproduce our results on our website: https://sites.google.com/berkeley.","PeriodicalId":248720,"journal":{"name":"Robotics: Science and Systems XIX","volume":"48 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":"126519754","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}
Pub Date : 2023-07-10DOI: 10.15607/RSS.2023.XIX.098
T. Y. Tang, D. Martini
—As much as place recognition is crucial for navi- gation, mapping and collecting training ground truth, namely sensor data pairs across different locations, are costly and time- consuming. This paper tackles these by learning lidar place recognition on public overhead imagery and in a self-supervised fashion, with no need for paired lidar and overhead imagery data. We learn the cross-modal data comparison between lidar and overhead imagery with a multi-step framework. First, images are transformed into synthetic lidar data and a latent projection is learned. Next, we discover pseudo pairs of lidar and satellite data from unpaired and asynchronous sequences, and use them for training a final embedding space projection in a cross-modality place recognition framework. We train and test our approach on real data from various environments and show performances approaching a supervised method using paired data.
{"title":"Self-Supervised Lidar Place Recognition in Overhead Imagery Using Unpaired Data","authors":"T. Y. Tang, D. Martini","doi":"10.15607/RSS.2023.XIX.098","DOIUrl":"https://doi.org/10.15607/RSS.2023.XIX.098","url":null,"abstract":"—As much as place recognition is crucial for navi- gation, mapping and collecting training ground truth, namely sensor data pairs across different locations, are costly and time- consuming. This paper tackles these by learning lidar place recognition on public overhead imagery and in a self-supervised fashion, with no need for paired lidar and overhead imagery data. We learn the cross-modal data comparison between lidar and overhead imagery with a multi-step framework. First, images are transformed into synthetic lidar data and a latent projection is learned. Next, we discover pseudo pairs of lidar and satellite data from unpaired and asynchronous sequences, and use them for training a final embedding space projection in a cross-modality place recognition framework. We train and test our approach on real data from various environments and show performances approaching a supervised method using paired data.","PeriodicalId":248720,"journal":{"name":"Robotics: Science and Systems XIX","volume":"1 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":"130569004","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}
Pub Date : 2023-07-10DOI: 10.15607/RSS.2023.XIX.107
Andrew Messing, Jacopo Banfi, M. Stadler, Ethan Stump, H. Ravichandar, N. Roy, S. Hutchinson
—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.
{"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}
Pub Date : 2023-07-10DOI: 10.15607/RSS.2023.XIX.007
C. Berg, V. Caggiano, Vikash Kumar
—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
{"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}
Pub Date : 2023-07-10DOI: 10.15607/RSS.2023.XIX.093
Yunhua Zhao, L. Wen
—This paper proposes a new adaptive control method with viscoelastic hysteresis compensation for high-precision tracking control of dielectric elastomer actuators (DEAs). A direct inverse feedforward compensator is constructed by using a modified Prandtl-Ishlinskii model for compensating hysteresis nonlinearities. The dynamics effects of DEAs and disturbances are coped with the adaptive inverse controller using filtered-x normalized least mean square algorithm. A series of real-time tracking experiments are carried out on a DEA made of commercial acrylic elastomers. The proposed control method achieves accurate tracking of various trajectories with the relative root-mean-square tracking error ranging from 1.37% to a maximum of 4.37% over the whole operating frequency range, and outperforms previously proposed methods in terms of accuracy. The excellent tracking results demonstrate the effectiveness of the developed control method for dielectric elastomer artificial muscles based soft actuators.
{"title":"Adaptive Tracking Control of Dielectric Elastomer Soft Actuators with Viscoelastic Hysteresis Compensation","authors":"Yunhua Zhao, L. Wen","doi":"10.15607/RSS.2023.XIX.093","DOIUrl":"https://doi.org/10.15607/RSS.2023.XIX.093","url":null,"abstract":"—This paper proposes a new adaptive control method with viscoelastic hysteresis compensation for high-precision tracking control of dielectric elastomer actuators (DEAs). A direct inverse feedforward compensator is constructed by using a modified Prandtl-Ishlinskii model for compensating hysteresis nonlinearities. The dynamics effects of DEAs and disturbances are coped with the adaptive inverse controller using filtered-x normalized least mean square algorithm. A series of real-time tracking experiments are carried out on a DEA made of commercial acrylic elastomers. The proposed control method achieves accurate tracking of various trajectories with the relative root-mean-square tracking error ranging from 1.37% to a maximum of 4.37% over the whole operating frequency range, and outperforms previously proposed methods in terms of accuracy. The excellent tracking results demonstrate the effectiveness of the developed control method for dielectric elastomer artificial muscles based soft actuators.","PeriodicalId":248720,"journal":{"name":"Robotics: Science and Systems XIX","volume":"1 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":"129464726","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}
Pub Date : 2023-07-10DOI: 10.15607/RSS.2023.XIX.090
S. T. Bui, Shinya Kawano, V. A. Ho
—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
{"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}
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