Pub Date : 2020-05-01DOI: 10.1109/ICRA40945.2020.9196648
M. Barenboim, A. Degani
This paper investigates a burrowing robot that can maneuver and steer while being submerged in a granular medium. The robot locomotes using an internal vibro-impact mechanism and steers using a rotating bevel-tip head. We formulate and investigate a non-holonomic model for the steering mechanism and a hybrid dynamics model for the thrusting mechanism. We perform a numerical analysis of the dynamics of the robot's thrusting mechanism using a simplified, orientation and depth dependent model for the drag forces acting on the robot. We first show, in simulation, that by carefully tuning various control input parameters, the thrusting mechanism can drive the robot both forward and backward. We present several experiments designed to evaluate and verify the simulative results using a proof-of-concept robot. We show that different input amplitudes indeed affect the direction of motion, as suggested by the simulation. We further demonstrate the ability of the robot to perform a simple S-shaped trajectory. These experiments demonstrate the feasibility of the robot's design and fidelity of the model.
{"title":"Steerable Burrowing Robot: Design, Modeling and Experiments","authors":"M. Barenboim, A. Degani","doi":"10.1109/ICRA40945.2020.9196648","DOIUrl":"https://doi.org/10.1109/ICRA40945.2020.9196648","url":null,"abstract":"This paper investigates a burrowing robot that can maneuver and steer while being submerged in a granular medium. The robot locomotes using an internal vibro-impact mechanism and steers using a rotating bevel-tip head. We formulate and investigate a non-holonomic model for the steering mechanism and a hybrid dynamics model for the thrusting mechanism. We perform a numerical analysis of the dynamics of the robot's thrusting mechanism using a simplified, orientation and depth dependent model for the drag forces acting on the robot. We first show, in simulation, that by carefully tuning various control input parameters, the thrusting mechanism can drive the robot both forward and backward. We present several experiments designed to evaluate and verify the simulative results using a proof-of-concept robot. We show that different input amplitudes indeed affect the direction of motion, as suggested by the simulation. We further demonstrate the ability of the robot to perform a simple S-shaped trajectory. These experiments demonstrate the feasibility of the robot's design and fidelity of the model.","PeriodicalId":6859,"journal":{"name":"2020 IEEE International Conference on Robotics and Automation (ICRA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76349398","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 : 2020-05-01DOI: 10.1109/ICRA40945.2020.9197337
C. Bouzgarrou, A. Koessler, N. Bouton
The 3-CRS manipulator is an original parallel mechanism having 6 degrees of freedom (DOFs) with only 3 limbs. This mechanism uses a motorized cylindrical joint per limb. This new paradigm of actuation opens research fields on new families of robots that should particularly interest the parallel robotics community. According to its dimensional synthesis, this mechanism can have remarkable kinematic properties such as a large orientation workspace or reconfiguration capabilities. In this paper, we introduce this mechanism and we study its singularities by using a geometric approach. This approach simplifies considerably singularity analysis problem by considering the relative geometric configurations of three planes defined by the distal links of the limbs. Thanks to that, a reconfiguration mode of the 3-CRS, that doubles its reachable workspace, is highlighted. This property is illustrated on a physical prototype of the robot.
{"title":"Singularity analysis and reconfiguration mode of the 3-CRS parallel manipulator","authors":"C. Bouzgarrou, A. Koessler, N. Bouton","doi":"10.1109/ICRA40945.2020.9197337","DOIUrl":"https://doi.org/10.1109/ICRA40945.2020.9197337","url":null,"abstract":"The 3-CRS manipulator is an original parallel mechanism having 6 degrees of freedom (DOFs) with only 3 limbs. This mechanism uses a motorized cylindrical joint per limb. This new paradigm of actuation opens research fields on new families of robots that should particularly interest the parallel robotics community. According to its dimensional synthesis, this mechanism can have remarkable kinematic properties such as a large orientation workspace or reconfiguration capabilities. In this paper, we introduce this mechanism and we study its singularities by using a geometric approach. This approach simplifies considerably singularity analysis problem by considering the relative geometric configurations of three planes defined by the distal links of the limbs. Thanks to that, a reconfiguration mode of the 3-CRS, that doubles its reachable workspace, is highlighted. This property is illustrated on a physical prototype of the robot.","PeriodicalId":6859,"journal":{"name":"2020 IEEE International Conference on Robotics and Automation (ICRA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87487399","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 : 2020-05-01DOI: 10.1109/ICRA40945.2020.9197072
Yufeng Yue, Chule Yang, Jun Zhang, Mingxing Wen, Zhenyu Wu, Haoyuan Zhang, Danwei W. Wang
Enabling long-term operation during day and night for collaborative robots requires a comprehensive understanding of the unstructured environment. Besides, in the dynamic environment, robots must be able to recognize dynamic objects and collaboratively build a global map. This paper proposes a novel approach for dynamic collaborative mapping based on multimodal environmental perception. For each mission, robots first apply heterogeneous sensor fusion model to detect humans and separate them to acquire static observations. Then, the collaborative mapping is performed to estimate the relative position between robots and local 3D maps are integrated into a globally consistent 3D map. The experiment is conducted in the day and night rainforest with moving people. The results show the accuracy, robustness, and versatility in 3D map fusion missions.
{"title":"Day and Night Collaborative Dynamic Mapping in Unstructured Environment Based on Multimodal Sensors","authors":"Yufeng Yue, Chule Yang, Jun Zhang, Mingxing Wen, Zhenyu Wu, Haoyuan Zhang, Danwei W. Wang","doi":"10.1109/ICRA40945.2020.9197072","DOIUrl":"https://doi.org/10.1109/ICRA40945.2020.9197072","url":null,"abstract":"Enabling long-term operation during day and night for collaborative robots requires a comprehensive understanding of the unstructured environment. Besides, in the dynamic environment, robots must be able to recognize dynamic objects and collaboratively build a global map. This paper proposes a novel approach for dynamic collaborative mapping based on multimodal environmental perception. For each mission, robots first apply heterogeneous sensor fusion model to detect humans and separate them to acquire static observations. Then, the collaborative mapping is performed to estimate the relative position between robots and local 3D maps are integrated into a globally consistent 3D map. The experiment is conducted in the day and night rainforest with moving people. The results show the accuracy, robustness, and versatility in 3D map fusion missions.","PeriodicalId":6859,"journal":{"name":"2020 IEEE International Conference on Robotics and Automation (ICRA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87621643","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 : 2020-05-01DOI: 10.1109/ICRA40945.2020.9196595
Ling Gao, Junyan Su, Jiadi Cui, Xiangchen Zeng, Xin-Zhong Peng, L. Kneip
The motion of planar ground vehicles is often non-holonomic, and as a result may be modelled by the 2 DoF Ackermann steering model. We analyse the feasibility of estimating such motion with a downward facing camera that exerts fronto-parallel motion with respect to the ground plane. This turns the motion estimation into a simple image registration problem in which we only have to identify a 2-parameter planar homography. However, one difficulty that arises from this setup is that ground-plane features are indistinctive and thus hard to match between successive views. We encountered this difficulty by introducing the first globally-optimal, correspondence-less solution to plane-based Ackermann motion estimation. The solution relies on the branch-and-bound optimisation technique. Through the low-dimensional parametrisation, a derivation of tight bounds, and an efficient implementation, we demonstrate how this technique is eventually amenable to accurate real-time motion estimation. We prove its property of global optimality and analyse the impact of assuming a locally constant centre of rotation. Our results on real data finally demonstrate a significant advantage over the more traditional, correspondence-based hypothesise-and-test schemes.
{"title":"Efficient Globally-Optimal Correspondence-Less Visual Odometry for Planar Ground Vehicles","authors":"Ling Gao, Junyan Su, Jiadi Cui, Xiangchen Zeng, Xin-Zhong Peng, L. Kneip","doi":"10.1109/ICRA40945.2020.9196595","DOIUrl":"https://doi.org/10.1109/ICRA40945.2020.9196595","url":null,"abstract":"The motion of planar ground vehicles is often non-holonomic, and as a result may be modelled by the 2 DoF Ackermann steering model. We analyse the feasibility of estimating such motion with a downward facing camera that exerts fronto-parallel motion with respect to the ground plane. This turns the motion estimation into a simple image registration problem in which we only have to identify a 2-parameter planar homography. However, one difficulty that arises from this setup is that ground-plane features are indistinctive and thus hard to match between successive views. We encountered this difficulty by introducing the first globally-optimal, correspondence-less solution to plane-based Ackermann motion estimation. The solution relies on the branch-and-bound optimisation technique. Through the low-dimensional parametrisation, a derivation of tight bounds, and an efficient implementation, we demonstrate how this technique is eventually amenable to accurate real-time motion estimation. We prove its property of global optimality and analyse the impact of assuming a locally constant centre of rotation. Our results on real data finally demonstrate a significant advantage over the more traditional, correspondence-based hypothesise-and-test schemes.","PeriodicalId":6859,"journal":{"name":"2020 IEEE International Conference on Robotics and Automation (ICRA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87997262","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 : 2020-05-01DOI: 10.1109/ICRA40945.2020.9196601
Isabell Hofstetter, Michael Sprunk, Florian Ries, M. Haueis
Reliable data association represents a main challenge of feature-based vehicle localization and is the key to integrity of localization. Independent of the type of features used, incorrect associations between detected and mapped features will provide erroneous position estimates. Only if the uniqueness of a local environment is represented by the features that are stored in the map, the reliability of localization is enhanced.In this work, a new approach based on Geometric Hashing is introduced to the field of data association for feature-based vehicle localization. Without any information on a prior position, the proposed method allows to efficiently search large map regions for plausible feature associations. Therefore, odometry and GNSS-based inputs can be neglected, which reduces the risk of error propagation and enables safe localization.The approach is demonstrated on approximately 10min of data recorded in an urban scenario. Cylindrical objects without distinctive descriptors, which were extracted from LiDAR data, serve as localization features. Experimental results both demonstrate the feasibility as well as limitations of the approach.
{"title":"Reliable Data Association for Feature-Based Vehicle Localization using Geometric Hashing Methods","authors":"Isabell Hofstetter, Michael Sprunk, Florian Ries, M. Haueis","doi":"10.1109/ICRA40945.2020.9196601","DOIUrl":"https://doi.org/10.1109/ICRA40945.2020.9196601","url":null,"abstract":"Reliable data association represents a main challenge of feature-based vehicle localization and is the key to integrity of localization. Independent of the type of features used, incorrect associations between detected and mapped features will provide erroneous position estimates. Only if the uniqueness of a local environment is represented by the features that are stored in the map, the reliability of localization is enhanced.In this work, a new approach based on Geometric Hashing is introduced to the field of data association for feature-based vehicle localization. Without any information on a prior position, the proposed method allows to efficiently search large map regions for plausible feature associations. Therefore, odometry and GNSS-based inputs can be neglected, which reduces the risk of error propagation and enables safe localization.The approach is demonstrated on approximately 10min of data recorded in an urban scenario. Cylindrical objects without distinctive descriptors, which were extracted from LiDAR data, serve as localization features. Experimental results both demonstrate the feasibility as well as limitations of the approach.","PeriodicalId":6859,"journal":{"name":"2020 IEEE International Conference on Robotics and Automation (ICRA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88028224","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 : 2020-05-01DOI: 10.1109/ICRA40945.2020.9197161
Nassim Benhabib, V. Padois, D. Daney
In this work, a robotic assistance strategy is developed to improve the safety in an artisanal task that involves a strong interaction between a machine-tool and an operator. Wood milling is chosen as a pilot task due to its importance in carpentry and its accidentogenic aspect. A physical model of the tooling process including a human is proposed and a simulator is thereafter developed to better understand situations that are dangerous for the craftsman. This simulator is validated with experiments on three subjects using an harmless mock-up. This validation shows the pertinence of the proposed control approach for the collaborative robot used to increase the safety of the task.
{"title":"Securing Industrial Operators with Collaborative Robots: Simulation and Experimental Validation for a Carpentry task","authors":"Nassim Benhabib, V. Padois, D. Daney","doi":"10.1109/ICRA40945.2020.9197161","DOIUrl":"https://doi.org/10.1109/ICRA40945.2020.9197161","url":null,"abstract":"In this work, a robotic assistance strategy is developed to improve the safety in an artisanal task that involves a strong interaction between a machine-tool and an operator. Wood milling is chosen as a pilot task due to its importance in carpentry and its accidentogenic aspect. A physical model of the tooling process including a human is proposed and a simulator is thereafter developed to better understand situations that are dangerous for the craftsman. This simulator is validated with experiments on three subjects using an harmless mock-up. This validation shows the pertinence of the proposed control approach for the collaborative robot used to increase the safety of the task.","PeriodicalId":6859,"journal":{"name":"2020 IEEE International Conference on Robotics and Automation (ICRA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88394805","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 : 2020-05-01DOI: 10.1109/ICRA40945.2020.9197366
Younggun Cho, Giseop Kim, Ayoung Kim
Learning-based ego-motion estimation approaches have recently drawn strong interest from researchers, mostly focusing on visual perception. A few learning-based approaches using Light Detection and Ranging (LiDAR) have been re-ported; however, they heavily rely on a supervised learning manner. Despite the meaningful performance of these approaches, supervised training requires ground-truth pose labels, which is the bottleneck for real-world applications. Differing from these approaches, we focus on unsupervised learning for LiDAR odometry (LO) without trainable labels. Achieving trainable LO in an unsupervised manner, we introduce the uncertainty-aware loss with geometric confidence, thereby al-lowing the reliability of the proposed pipeline. Evaluation on the KITTI, Complex Urban, and Oxford RobotCar datasets demonstrate the prominent performance of the proposed method compared to conventional model-based methods. The proposed method shows a comparable result against SuMa (in KITTI), LeGO-LOAM (in Complex Urban), and Stereo-VO (in Oxford RobotCar). The video and extra-information of the paper are described in https://sites.google.com/view/deeplo.
{"title":"Unsupervised Geometry-Aware Deep LiDAR Odometry","authors":"Younggun Cho, Giseop Kim, Ayoung Kim","doi":"10.1109/ICRA40945.2020.9197366","DOIUrl":"https://doi.org/10.1109/ICRA40945.2020.9197366","url":null,"abstract":"Learning-based ego-motion estimation approaches have recently drawn strong interest from researchers, mostly focusing on visual perception. A few learning-based approaches using Light Detection and Ranging (LiDAR) have been re-ported; however, they heavily rely on a supervised learning manner. Despite the meaningful performance of these approaches, supervised training requires ground-truth pose labels, which is the bottleneck for real-world applications. Differing from these approaches, we focus on unsupervised learning for LiDAR odometry (LO) without trainable labels. Achieving trainable LO in an unsupervised manner, we introduce the uncertainty-aware loss with geometric confidence, thereby al-lowing the reliability of the proposed pipeline. Evaluation on the KITTI, Complex Urban, and Oxford RobotCar datasets demonstrate the prominent performance of the proposed method compared to conventional model-based methods. The proposed method shows a comparable result against SuMa (in KITTI), LeGO-LOAM (in Complex Urban), and Stereo-VO (in Oxford RobotCar). The video and extra-information of the paper are described in https://sites.google.com/view/deeplo.","PeriodicalId":6859,"journal":{"name":"2020 IEEE International Conference on Robotics and Automation (ICRA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86482774","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 : 2020-05-01DOI: 10.1109/ICRA40945.2020.9197463
Aaron Miller, Kyungzun Rim, Parth Chopra, Paritosh Kelkar, M. Likhachev
Fully autonomous vehicles are expected to share the road with less advanced vehicles for a significant period of time. Furthermore, an increasing number of vehicles on the road are equipped with a variety of low-fidelity sensors which provide some perception and localization data, but not at a high enough quality for full autonomy. In this paper, we develop a perception and localization system that allows a vehicle with low-fidelity sensors to incorporate high-fidelity observations from a vehicle in front of it, allowing both vehicles to operate with full autonomy. The resulting system generates perception and localization information that is both low-noise in regions covered by high-fidelity sensors and avoids false negatives in areas only observed by low-fidelity sensors, while dealing with latency and dropout of the communication link between the two vehicles. At its core, the system uses a set of Extended Kalman filters which incorporate observations from both vehicles’ sensors and extrapolate them using information about the road geometry. The perception and localization algorithms are evaluated both in simulation and on real vehicles as part of a full cooperative driving system.
{"title":"Cooperative Perception and Localization for Cooperative Driving","authors":"Aaron Miller, Kyungzun Rim, Parth Chopra, Paritosh Kelkar, M. Likhachev","doi":"10.1109/ICRA40945.2020.9197463","DOIUrl":"https://doi.org/10.1109/ICRA40945.2020.9197463","url":null,"abstract":"Fully autonomous vehicles are expected to share the road with less advanced vehicles for a significant period of time. Furthermore, an increasing number of vehicles on the road are equipped with a variety of low-fidelity sensors which provide some perception and localization data, but not at a high enough quality for full autonomy. In this paper, we develop a perception and localization system that allows a vehicle with low-fidelity sensors to incorporate high-fidelity observations from a vehicle in front of it, allowing both vehicles to operate with full autonomy. The resulting system generates perception and localization information that is both low-noise in regions covered by high-fidelity sensors and avoids false negatives in areas only observed by low-fidelity sensors, while dealing with latency and dropout of the communication link between the two vehicles. At its core, the system uses a set of Extended Kalman filters which incorporate observations from both vehicles’ sensors and extrapolate them using information about the road geometry. The perception and localization algorithms are evaluated both in simulation and on real vehicles as part of a full cooperative driving system.","PeriodicalId":6859,"journal":{"name":"2020 IEEE International Conference on Robotics and Automation (ICRA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86105011","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 : 2020-05-01DOI: 10.1109/ICRA40945.2020.9196546
Jialun Liu, Hironari Sugiyama, T. Nakayama, S. Miyashita
A button battery accidentally ingested by a toddler or small child can cause severe damage to the stomach within a short period of time. Once a battery lands on the surface of the esophagus or stomach, it can run a current in the tissue and induce a chemical reaction resulting in injury. Following our previous work where we presented an ingestible magnetic robot for button battery retrieval, this study presents a remotely achieved novel localization method of a button battery with commonly available magnetic sensors (Hall-effect sensors). By applying a direct magnetic field to the button battery using an electromagnetic coil, the battery is magnetized, and hence it becomes able to be sensed by Hall-effect sensors. Using a trilateration method, we were able to detect the locations of an LR44 button battery and other ferromagnetic materials at variable distances. Additional four electromagnetic coils were used to autonomously navigate a magnet-containing capsule to dislocate the battery from the affected site.
{"title":"Magnetic Sensor Based Topographic Localization for Automatic Dislocation of Ingested Button Battery","authors":"Jialun Liu, Hironari Sugiyama, T. Nakayama, S. Miyashita","doi":"10.1109/ICRA40945.2020.9196546","DOIUrl":"https://doi.org/10.1109/ICRA40945.2020.9196546","url":null,"abstract":"A button battery accidentally ingested by a toddler or small child can cause severe damage to the stomach within a short period of time. Once a battery lands on the surface of the esophagus or stomach, it can run a current in the tissue and induce a chemical reaction resulting in injury. Following our previous work where we presented an ingestible magnetic robot for button battery retrieval, this study presents a remotely achieved novel localization method of a button battery with commonly available magnetic sensors (Hall-effect sensors). By applying a direct magnetic field to the button battery using an electromagnetic coil, the battery is magnetized, and hence it becomes able to be sensed by Hall-effect sensors. Using a trilateration method, we were able to detect the locations of an LR44 button battery and other ferromagnetic materials at variable distances. Additional four electromagnetic coils were used to autonomously navigate a magnet-containing capsule to dislocate the battery from the affected site.","PeriodicalId":6859,"journal":{"name":"2020 IEEE International Conference on Robotics and Automation (ICRA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83855022","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 : 2020-05-01DOI: 10.1109/ICRA40945.2020.9196987
Kyon-Mo Yang, Kap-Ho Seo, S. Kang, Yoonseob Lim
Verbal interaction between a human and a robot may play a key role in conveying suitable directions for a robot to achieve the goal of a user’s request. However, a robot may need to correct task plans or make new decisions with human help, which would make the interaction inconvenient and also increase the interaction time. In this paper, we propose a new verbal interaction-based method that can generate plan models and execute proper actions without human involvement in the middle of performing a task by a robot. To understand the verbal behaviors of humans when giving instructions to a robot, we first conducted a brief user study and found that a human user does not explicitly express the required task. To handle such unclear instructions by a human, we propose two different algorithms that can generate a component of new plan models based on intents and entities parsed from natural language and can resolve the unclear entities existed in human instructions. An experimental scenario with a robot, Cozmo, was tried in the lab environment to test whether or not the proposed method could generate an appropriate plan model. As a result, we found that the robot could successfully accomplish the task following human instructions and also found that the number of interactions and components in the plan model could be reduced as opposed to the general reactive plan model. In the future, we are going to improve the automated process of generating plan models and apply various scenarios under different service environments and robots.
{"title":"Robot Plan Model Generation and Execution with Natural Language Interface*","authors":"Kyon-Mo Yang, Kap-Ho Seo, S. Kang, Yoonseob Lim","doi":"10.1109/ICRA40945.2020.9196987","DOIUrl":"https://doi.org/10.1109/ICRA40945.2020.9196987","url":null,"abstract":"Verbal interaction between a human and a robot may play a key role in conveying suitable directions for a robot to achieve the goal of a user’s request. However, a robot may need to correct task plans or make new decisions with human help, which would make the interaction inconvenient and also increase the interaction time. In this paper, we propose a new verbal interaction-based method that can generate plan models and execute proper actions without human involvement in the middle of performing a task by a robot. To understand the verbal behaviors of humans when giving instructions to a robot, we first conducted a brief user study and found that a human user does not explicitly express the required task. To handle such unclear instructions by a human, we propose two different algorithms that can generate a component of new plan models based on intents and entities parsed from natural language and can resolve the unclear entities existed in human instructions. An experimental scenario with a robot, Cozmo, was tried in the lab environment to test whether or not the proposed method could generate an appropriate plan model. As a result, we found that the robot could successfully accomplish the task following human instructions and also found that the number of interactions and components in the plan model could be reduced as opposed to the general reactive plan model. In the future, we are going to improve the automated process of generating plan models and apply various scenarios under different service environments and robots.","PeriodicalId":6859,"journal":{"name":"2020 IEEE International Conference on Robotics and Automation (ICRA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82646002","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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