Pub Date : 2022-05-23DOI: 10.1109/icra46639.2022.9812167
Zhiqiang Jian, Songyi Zhang, Jiahui Zhang, Shi-tao Chen, N. Zheng
Collision risk and smoothness are the most important factors in global path planning. Currently, planning methods that reduce global path collision risk and improve its smoothness through numerical optimization have achieved good results. However, these methods cannot always optimize the path. The reason is all points on the path are considered as decision variables, which leads to the high dimensionality of the defined optimization problem. Therefore, we propose a novel global path optimization method. The method characterizes the path as a parametric curve and then optimizes the curve's parameters with a defined objective function, which successfully reduces the dimension of optimization problem. The proposed method is compared with baseline and state-of-the-art methods. Experimental results show the path optimized by our method is not only optimal in collision risk, but also in efficiency and smoothness. Furthermore, the proposed method is also implemented and tested in both simulation and real robots.
{"title":"Parametric Path Optimization for Wheeled Robots Navigation","authors":"Zhiqiang Jian, Songyi Zhang, Jiahui Zhang, Shi-tao Chen, N. Zheng","doi":"10.1109/icra46639.2022.9812167","DOIUrl":"https://doi.org/10.1109/icra46639.2022.9812167","url":null,"abstract":"Collision risk and smoothness are the most important factors in global path planning. Currently, planning methods that reduce global path collision risk and improve its smoothness through numerical optimization have achieved good results. However, these methods cannot always optimize the path. The reason is all points on the path are considered as decision variables, which leads to the high dimensionality of the defined optimization problem. Therefore, we propose a novel global path optimization method. The method characterizes the path as a parametric curve and then optimizes the curve's parameters with a defined objective function, which successfully reduces the dimension of optimization problem. The proposed method is compared with baseline and state-of-the-art methods. Experimental results show the path optimized by our method is not only optimal in collision risk, but also in efficiency and smoothness. Furthermore, the proposed method is also implemented and tested in both simulation and real robots.","PeriodicalId":341244,"journal":{"name":"2022 International Conference on Robotics and Automation (ICRA)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128159851","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 : 2022-05-23DOI: 10.1109/icra46639.2022.9811902
Cheng Fang, Di Wang, Dezhen Song, Jun Zou
To continuously improve robotic grasping, we are interested in developing a contactless fingertip-mounted sensor for near-distance ranging and material sensing. Previously, we demonstrated a dual-modal and dual sensing mechanisms (DMDSM) pretouch sensor prototype based on pulse-echo ultrasound and optoacoustics. However, the complex system, the bulky and expensive pulser-receiver, and the omni-directionally sensitive microphone block the sensor from practical applications in real robotic fingers. To address these issues, we report the second generation (G2) DMDSM sensor without the pulser-receiver and microphone, which is made possible by redesigning the ultrasound transmitter and receiver to gain much wider acoustic bandwidth. To verify our design, a prototype of the G2 DMDSM sensor has been fabricated and tested. The testing results show that the G2 DMDSM sensor can achieve better ranging and similar material/structure sensing performance, but with much-simplified configuration and operation. The primary results indicate that the G2 DMDSM sensor could provide a promising solution for fingertip pretouch sensing in robotic grasping.
{"title":"The Second Generation (G2) Fingertip Sensor for Near-Distance Ranging and Material Sensing in Robotic Grasping*","authors":"Cheng Fang, Di Wang, Dezhen Song, Jun Zou","doi":"10.1109/icra46639.2022.9811902","DOIUrl":"https://doi.org/10.1109/icra46639.2022.9811902","url":null,"abstract":"To continuously improve robotic grasping, we are interested in developing a contactless fingertip-mounted sensor for near-distance ranging and material sensing. Previously, we demonstrated a dual-modal and dual sensing mechanisms (DMDSM) pretouch sensor prototype based on pulse-echo ultrasound and optoacoustics. However, the complex system, the bulky and expensive pulser-receiver, and the omni-directionally sensitive microphone block the sensor from practical applications in real robotic fingers. To address these issues, we report the second generation (G2) DMDSM sensor without the pulser-receiver and microphone, which is made possible by redesigning the ultrasound transmitter and receiver to gain much wider acoustic bandwidth. To verify our design, a prototype of the G2 DMDSM sensor has been fabricated and tested. The testing results show that the G2 DMDSM sensor can achieve better ranging and similar material/structure sensing performance, but with much-simplified configuration and operation. The primary results indicate that the G2 DMDSM sensor could provide a promising solution for fingertip pretouch sensing in robotic grasping.","PeriodicalId":341244,"journal":{"name":"2022 International Conference on Robotics and Automation (ICRA)","volume":"215 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128614598","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 : 2022-05-23DOI: 10.48550/arXiv.2206.12145
David B. Adrian, A. Kupcsik, Markus Spies, H. Neumann
We propose a framework for robust and efficient training of Dense Object Nets (DON) [1] with a focus on industrial multi-object robot manipulation scenarios. DON is a popular approach to obtain dense, view-invariant object descriptors, which can be used for a multitude of downstream tasks in robot manipulation, such as, pose estimation, state representation for control, etc. However, the original work [1] focused training on singulated objects, with limited results on instance-specific, multi-object applications. Additionally, a complex data collection pipeline, including 3D reconstruction and mask annotation of each object, is required for training. In this paper, we further improve the efficacy of DON with a simplified data collection and training regime, that consistently yields higher precision and enables robust tracking of keypoints with less data requirements. In particular, we focus on training with multi-object data instead of singulated objects, combined with a well-chosen augmentation scheme. We additionally propose an alternative loss formulation to the original pixel wise formulation that offers better results and is less sensitive to hyperparameters. Finally, we demonstrate the robustness and accuracy of our proposed framework on a real-world robotic grasping task.
{"title":"Efficient and Robust Training of Dense Object Nets for Multi-Object Robot Manipulation","authors":"David B. Adrian, A. Kupcsik, Markus Spies, H. Neumann","doi":"10.48550/arXiv.2206.12145","DOIUrl":"https://doi.org/10.48550/arXiv.2206.12145","url":null,"abstract":"We propose a framework for robust and efficient training of Dense Object Nets (DON) [1] with a focus on industrial multi-object robot manipulation scenarios. DON is a popular approach to obtain dense, view-invariant object descriptors, which can be used for a multitude of downstream tasks in robot manipulation, such as, pose estimation, state representation for control, etc. However, the original work [1] focused training on singulated objects, with limited results on instance-specific, multi-object applications. Additionally, a complex data collection pipeline, including 3D reconstruction and mask annotation of each object, is required for training. In this paper, we further improve the efficacy of DON with a simplified data collection and training regime, that consistently yields higher precision and enables robust tracking of keypoints with less data requirements. In particular, we focus on training with multi-object data instead of singulated objects, combined with a well-chosen augmentation scheme. We additionally propose an alternative loss formulation to the original pixel wise formulation that offers better results and is less sensitive to hyperparameters. Finally, we demonstrate the robustness and accuracy of our proposed framework on a real-world robotic grasping task.","PeriodicalId":341244,"journal":{"name":"2022 International Conference on Robotics and Automation (ICRA)","volume":"230 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128619406","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 : 2022-05-23DOI: 10.1109/icra46639.2022.9811359
Jesus Mago, F. Louveau, M. Vitrani, G. Morel
One of the major functions brought by robots in Minimally Invasive Surgery is endoscope holding. This consists, for the user, in placing the camera at a desired location which the robot will maintain still once he/she releases it. This behavior is usually achieved with rigid position servoing, leading to possibly high forces generated and safety issues. Model-based weight compensation is an alternative solution. However, endoscopic cameras' weight is difficult to model as their gravity parameters can change during the same surgery. In this paper, an algorithm is presented as an option to cope with this variability in the gravity model without using rigid position servoing. The surgeon first positions the camera in a comanipulation mode (gravity compensation). When he/she releases the camera, if the gravity model is not accurate, the endoscope presents a drift. In this case, a controller brings the endoscope back to its release position by combining low gain position control and model adaptation. Once stabilized, the system is switched back to a zero-stiffness mode. Two in-vitro experiments were performed in which a user manipulates an endoscope whose configuration of mass is changed. In one case, the mass in the gravity model was set to half of the actual one. In the second case, a variable weight was attached to the endoscope. The algorithm successfully updated the model for each experiment reducing position errors by 95% and 57%, respectively.
{"title":"Safe endoscope holding in minimally invasive surgery: zero stiffness and adaptive weight compensation","authors":"Jesus Mago, F. Louveau, M. Vitrani, G. Morel","doi":"10.1109/icra46639.2022.9811359","DOIUrl":"https://doi.org/10.1109/icra46639.2022.9811359","url":null,"abstract":"One of the major functions brought by robots in Minimally Invasive Surgery is endoscope holding. This consists, for the user, in placing the camera at a desired location which the robot will maintain still once he/she releases it. This behavior is usually achieved with rigid position servoing, leading to possibly high forces generated and safety issues. Model-based weight compensation is an alternative solution. However, endoscopic cameras' weight is difficult to model as their gravity parameters can change during the same surgery. In this paper, an algorithm is presented as an option to cope with this variability in the gravity model without using rigid position servoing. The surgeon first positions the camera in a comanipulation mode (gravity compensation). When he/she releases the camera, if the gravity model is not accurate, the endoscope presents a drift. In this case, a controller brings the endoscope back to its release position by combining low gain position control and model adaptation. Once stabilized, the system is switched back to a zero-stiffness mode. Two in-vitro experiments were performed in which a user manipulates an endoscope whose configuration of mass is changed. In one case, the mass in the gravity model was set to half of the actual one. In the second case, a variable weight was attached to the endoscope. The algorithm successfully updated the model for each experiment reducing position errors by 95% and 57%, respectively.","PeriodicalId":341244,"journal":{"name":"2022 International Conference on Robotics and Automation (ICRA)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129605406","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 : 2022-05-23DOI: 10.1109/icra46639.2022.9812432
Yongming Qin, M. Kumon, T. Furukawa
This paper presents a data-driven multiple model framework for estimating the intention of a target from observations. Multiple model (MM) state estimation methods have been extensively used for intention estimation by mapping one intention to one dynamic model assuming one-to-one relations. However, intentions are subjective to humans and it is difficult to establish the one-to-one relations explicitly. The proposed framework infers the multiple-to-multiple relations between intentions and models directly from observations that are labeled with intentions. For intention estimation, both the relations and model probabilities of an Interacting Multiple Model (IMM) state estimation approach are integrated into a recursive Bayesian framework. Taking advantage of the inferred multiple-to-multiple relations, the framework incorpo-rates more accurate relations and avoids following the strict one-to-one relations. Numerical and real experiments were performed to investigate the framework through the intention estimation of a maneuvered quadrotor. Results show higher estimation accuracy and superior flexibility in designing mod-els over the conventional approach that assumes one-to-one relations.
{"title":"A Data-Driven Multiple Model Framework for Intention Estimation","authors":"Yongming Qin, M. Kumon, T. Furukawa","doi":"10.1109/icra46639.2022.9812432","DOIUrl":"https://doi.org/10.1109/icra46639.2022.9812432","url":null,"abstract":"This paper presents a data-driven multiple model framework for estimating the intention of a target from observations. Multiple model (MM) state estimation methods have been extensively used for intention estimation by mapping one intention to one dynamic model assuming one-to-one relations. However, intentions are subjective to humans and it is difficult to establish the one-to-one relations explicitly. The proposed framework infers the multiple-to-multiple relations between intentions and models directly from observations that are labeled with intentions. For intention estimation, both the relations and model probabilities of an Interacting Multiple Model (IMM) state estimation approach are integrated into a recursive Bayesian framework. Taking advantage of the inferred multiple-to-multiple relations, the framework incorpo-rates more accurate relations and avoids following the strict one-to-one relations. Numerical and real experiments were performed to investigate the framework through the intention estimation of a maneuvered quadrotor. Results show higher estimation accuracy and superior flexibility in designing mod-els over the conventional approach that assumes one-to-one relations.","PeriodicalId":341244,"journal":{"name":"2022 International Conference on Robotics and Automation (ICRA)","volume":"473 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127550925","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 : 2022-05-23DOI: 10.1109/icra46639.2022.9811959
Kangcheng Liu, Yuzhi Zhao, Z. Gao, Ben M. Chen
Existing state-of-the-art 3D point clouds understanding methods only perform well in a fully supervised manner. To the best of our knowledge, there exists no unified framework which simultaneously solves the downstream high-level understanding tasks, especially when labels are extremely limited. This work presents a general and simple framework to tackle point clouds understanding when labels are limited. We propose a novel unsupervised region expansion based clustering method for generating clusters. More importantly, we innovatively propose to learn to merge the over-divided clusters based on the local low-level geometric property similarities and the learned high-level feature similarities supervised by weak labels. Hence, the true weak labels guide pseudo labels merging taking both geometric and semantic feature correlations into consideration. Finally, the self-supervised data augmentation optimization module is proposed to guide the propagation of labels among semantically similar points within a scene. Experimental Results demonstrate that our framework has the best performance among the three most important weakly supervised point clouds understanding tasks including semantic segmentation, instance segmentation, and object detection even when limited points are labeled.
{"title":"WeakLabel3D-Net: A Complete Framework for Real-Scene LiDAR Point Clouds Weakly Supervised Multi-Tasks Understanding","authors":"Kangcheng Liu, Yuzhi Zhao, Z. Gao, Ben M. Chen","doi":"10.1109/icra46639.2022.9811959","DOIUrl":"https://doi.org/10.1109/icra46639.2022.9811959","url":null,"abstract":"Existing state-of-the-art 3D point clouds understanding methods only perform well in a fully supervised manner. To the best of our knowledge, there exists no unified framework which simultaneously solves the downstream high-level understanding tasks, especially when labels are extremely limited. This work presents a general and simple framework to tackle point clouds understanding when labels are limited. We propose a novel unsupervised region expansion based clustering method for generating clusters. More importantly, we innovatively propose to learn to merge the over-divided clusters based on the local low-level geometric property similarities and the learned high-level feature similarities supervised by weak labels. Hence, the true weak labels guide pseudo labels merging taking both geometric and semantic feature correlations into consideration. Finally, the self-supervised data augmentation optimization module is proposed to guide the propagation of labels among semantically similar points within a scene. Experimental Results demonstrate that our framework has the best performance among the three most important weakly supervised point clouds understanding tasks including semantic segmentation, instance segmentation, and object detection even when limited points are labeled.","PeriodicalId":341244,"journal":{"name":"2022 International Conference on Robotics and Automation (ICRA)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129942159","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 : 2022-05-23DOI: 10.1109/icra46639.2022.9812067
R. Vijayan, M. Stefano, C. Ott
In this paper, we propose a detumbling strategy that stabilizes the motion of a tumbling client satellite using an orbital servicing manipulator, which is the goal of the post-grasp phase. One of the critical aspects in this phase is ensuring that excessive contact forces are not generated at the grasp interface. In addition, space mission requirements might demand a nominal manipulator configuration that is suitable for further manipulation/servicing activities. The proposed strategy allows the detumbling of the client motion while ensuring that the contact forces developed at the grasp interface do not violate a safety threshold. Further, it allows the reconfiguration of the manipulator arm by exploiting the full actuation capability of the manipulator-equipped servicing spacecraft. The controller guarantees joint task convergence in the nullspace of the manipulator's end-effector, and is also valid for kinematically singular configurations of the manipulator. It is further augmented using a quadratic programming based approach to optimally constrain the contact forces. Finally, simulation results for a post-grasp detumbling scenario are shown to validate the effectiveness of the proposed method.
{"title":"A Detumbling Strategy for an Orbital Manipulator in the Post-Grasp Phase","authors":"R. Vijayan, M. Stefano, C. Ott","doi":"10.1109/icra46639.2022.9812067","DOIUrl":"https://doi.org/10.1109/icra46639.2022.9812067","url":null,"abstract":"In this paper, we propose a detumbling strategy that stabilizes the motion of a tumbling client satellite using an orbital servicing manipulator, which is the goal of the post-grasp phase. One of the critical aspects in this phase is ensuring that excessive contact forces are not generated at the grasp interface. In addition, space mission requirements might demand a nominal manipulator configuration that is suitable for further manipulation/servicing activities. The proposed strategy allows the detumbling of the client motion while ensuring that the contact forces developed at the grasp interface do not violate a safety threshold. Further, it allows the reconfiguration of the manipulator arm by exploiting the full actuation capability of the manipulator-equipped servicing spacecraft. The controller guarantees joint task convergence in the nullspace of the manipulator's end-effector, and is also valid for kinematically singular configurations of the manipulator. It is further augmented using a quadratic programming based approach to optimally constrain the contact forces. Finally, simulation results for a post-grasp detumbling scenario are shown to validate the effectiveness of the proposed method.","PeriodicalId":341244,"journal":{"name":"2022 International Conference on Robotics and Automation (ICRA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128946543","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}
Labour shortage, difficulties in labour management, the digitalization of fruit production pipeline to reduce the fruit production costs have made robotic systems for selective harvesting of strawberries an important industry and academic research. One of the important components of such technologies yet to be developed is fruit picking perception. For picking strawberries, a robot needs to infer the location of picking points from the images of strawberries. Moreover, the size and weight of strawberries to be picked can help the robot to place the picked strawberries in proper punnets directly to be delivered to customers in supermarkets. This can save significant time and packing costs in packhouses. Geometry-based approaches are the most common approach to determine the picking point but they suffer from inaccuracies due to noise, occlusion, and varying shape and orientation of the berries. In contrast, we present two novel datasets of strawberries annotated with picking points, key-points (such as the shoulder points, the contact point between the calyx and flesh, and the point on the flesh farthest from the calyx), and the weight and size of the berries. We performed experiments with Detectron-2, which is an extended version of Mask-RCNN with key-points detection capability. The results show that the key-points detection approach works well for picking and grasping point localization. The second dataset also presents the dimensions and weight of strawberries. Our novel baseline model for weight estimation outperforms many state-of-the-art deep networks. The datasets and annotations are available at https://github.com/imanlab/strawberry-pp-w-r-dataset.
{"title":"Strawberry picking point localization ripeness and weight estimation","authors":"Alessandra Tafuro, Adeayo Adewumi, Soran Parsa, Ghalamzan E. Amir, Bappaditya Debnath","doi":"10.1109/icra46639.2022.9812303","DOIUrl":"https://doi.org/10.1109/icra46639.2022.9812303","url":null,"abstract":"Labour shortage, difficulties in labour management, the digitalization of fruit production pipeline to reduce the fruit production costs have made robotic systems for selective harvesting of strawberries an important industry and academic research. One of the important components of such technologies yet to be developed is fruit picking perception. For picking strawberries, a robot needs to infer the location of picking points from the images of strawberries. Moreover, the size and weight of strawberries to be picked can help the robot to place the picked strawberries in proper punnets directly to be delivered to customers in supermarkets. This can save significant time and packing costs in packhouses. Geometry-based approaches are the most common approach to determine the picking point but they suffer from inaccuracies due to noise, occlusion, and varying shape and orientation of the berries. In contrast, we present two novel datasets of strawberries annotated with picking points, key-points (such as the shoulder points, the contact point between the calyx and flesh, and the point on the flesh farthest from the calyx), and the weight and size of the berries. We performed experiments with Detectron-2, which is an extended version of Mask-RCNN with key-points detection capability. The results show that the key-points detection approach works well for picking and grasping point localization. The second dataset also presents the dimensions and weight of strawberries. Our novel baseline model for weight estimation outperforms many state-of-the-art deep networks. The datasets and annotations are available at https://github.com/imanlab/strawberry-pp-w-r-dataset.","PeriodicalId":341244,"journal":{"name":"2022 International Conference on Robotics and Automation (ICRA)","volume":"8 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125659253","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 : 2022-05-23DOI: 10.1109/icra46639.2022.9812410
Raghavv Goel, Fnu Abhimanyu, Kirtan Patel, J. Galeotti, H. Choset
Ultrasound scanning is an imaging technique that aids medical professionals in diagnostics and interventional procedures. However, a trained human-in-the-loop (HITL) with a radiologist is required to perform the scanning procedure. We seek to create a novel ultrasound system that can provide imaging in the absence of a trained radiologist, say for patients in the field who suffered injuries after a natural disaster. One challenge of automating ultrasound scanning involves finding the optimal area to scan and then performing the actual scan. This task requires simultaneously maintaining contact with the surface while moving along it to capture high quality images. In this work, we present an automated Robotic Ultrasound System (RUS) to tackle these challenges. Our approach introduces a Bayesian Optimization framework to guide the probe to multiple points on the unknown surface. Our proposed framework collects the ultrasound images as well as the pose information at every probed point to estimate regions with high vessel density (information map) and the surface contour. Based on the information map and the surface contour, an area of interest is selected for scanning. Furthermore, to scan the proposed region, a novel 6-axis hybrid force-position controller is presented to ensure acoustic coupling. Lastly, we provide experimental results on two different phantom models to corroborate our approach.
{"title":"Autonomous Ultrasound Scanning using Bayesian Optimization and Hybrid Force Control","authors":"Raghavv Goel, Fnu Abhimanyu, Kirtan Patel, J. Galeotti, H. Choset","doi":"10.1109/icra46639.2022.9812410","DOIUrl":"https://doi.org/10.1109/icra46639.2022.9812410","url":null,"abstract":"Ultrasound scanning is an imaging technique that aids medical professionals in diagnostics and interventional procedures. However, a trained human-in-the-loop (HITL) with a radiologist is required to perform the scanning procedure. We seek to create a novel ultrasound system that can provide imaging in the absence of a trained radiologist, say for patients in the field who suffered injuries after a natural disaster. One challenge of automating ultrasound scanning involves finding the optimal area to scan and then performing the actual scan. This task requires simultaneously maintaining contact with the surface while moving along it to capture high quality images. In this work, we present an automated Robotic Ultrasound System (RUS) to tackle these challenges. Our approach introduces a Bayesian Optimization framework to guide the probe to multiple points on the unknown surface. Our proposed framework collects the ultrasound images as well as the pose information at every probed point to estimate regions with high vessel density (information map) and the surface contour. Based on the information map and the surface contour, an area of interest is selected for scanning. Furthermore, to scan the proposed region, a novel 6-axis hybrid force-position controller is presented to ensure acoustic coupling. Lastly, we provide experimental results on two different phantom models to corroborate our approach.","PeriodicalId":341244,"journal":{"name":"2022 International Conference on Robotics and Automation (ICRA)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128907585","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 : 2022-05-23DOI: 10.1109/icra46639.2022.9811993
Amit Parag, Sébastien Kleff, Léo Saci, N. Mansard, O. Stasse
The recent successes in deep reinforcement learning largely rely on the capabilities of generating masses of data, which in turn implies the use of a simulator. In particular, current progress in multi body dynamic simulators are under-pinning the implementation of reinforcement learning for end-to-end control of robotic systems. Yet simulators are mostly considered as black boxes while we have the knowledge to make them produce a richer information. In this paper, we are proposing to use the derivatives of the simulator to help with the convergence of the learning. For that, we combine model-based trajectory optimization to produce informative trials using 1st- and 2nd-order simulation derivatives. These locally-optimal runs give fair estimates of the value function and its derivatives, that we use to accelerate the convergence of the critics using Sobolev learning. We empirically demonstrate that the algorithm leads to a faster and more accurate estimation of the value function. The resulting value estimate is used in model-predictive controller as a proxy for shortening the preview horizon. We believe that it is also a first step toward superlinear reinforcement learning algorithm using simulation derivatives, that we need for end-to-end legged locomotion.
{"title":"Value learning from trajectory optimization and Sobolev descent: A step toward reinforcement learning with superlinear convergence properties","authors":"Amit Parag, Sébastien Kleff, Léo Saci, N. Mansard, O. Stasse","doi":"10.1109/icra46639.2022.9811993","DOIUrl":"https://doi.org/10.1109/icra46639.2022.9811993","url":null,"abstract":"The recent successes in deep reinforcement learning largely rely on the capabilities of generating masses of data, which in turn implies the use of a simulator. In particular, current progress in multi body dynamic simulators are under-pinning the implementation of reinforcement learning for end-to-end control of robotic systems. Yet simulators are mostly considered as black boxes while we have the knowledge to make them produce a richer information. In this paper, we are proposing to use the derivatives of the simulator to help with the convergence of the learning. For that, we combine model-based trajectory optimization to produce informative trials using 1st- and 2nd-order simulation derivatives. These locally-optimal runs give fair estimates of the value function and its derivatives, that we use to accelerate the convergence of the critics using Sobolev learning. We empirically demonstrate that the algorithm leads to a faster and more accurate estimation of the value function. The resulting value estimate is used in model-predictive controller as a proxy for shortening the preview horizon. We believe that it is also a first step toward superlinear reinforcement learning algorithm using simulation derivatives, that we need for end-to-end legged locomotion.","PeriodicalId":341244,"journal":{"name":"2022 International Conference on Robotics and Automation (ICRA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130750432","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}