Pub Date : 2025-01-15DOI: 10.1109/LRA.2025.3528675
Shi Zhang;Rongxin Cui;Weisheng Yan;Yinglin Li
Recent advances in sampling-based algorithms have enhanced the ability of mobile robots to navigate safely in environments with localization uncertainty. However, navigating narrow passages remains a significant challenge due to the heightened risks posed by uncertainty. In this letter, we present a novel algorithm, Risk-Averse RRT* with Local Vine Expansion Behavior (RA-RRTV*), to systematically address these challenges. The algorithm combines RRT* with chance constraints and incorporates an objective function to balance path length and risk, enabling the discovery of risk-averse paths. Narrow passages in the belief space are identified using sample-based information, while sequential Bayesian sampling is employed to guide the expansion of local belief vines, ensuring connectivity in high-risk regions. We provide proof of the asymptotic optimality of RA-RRTV*. The effectiveness of RA-RRTV* is demonstrated through extensive simulations and real-world experiments.
基于采样算法的最新进展增强了移动机器人在具有定位不确定性的环境中安全导航的能力。然而，由于不确定性带来的风险增加，在狭窄的航道上航行仍然是一项重大挑战。在这篇文章中，我们提出了一种新的算法，Risk-Averse RRT* with Local Vine Expansion Behavior (RA-RRTV*)，来系统地解决这些挑战。该算法将RRT*与机会约束相结合，并引入目标函数来平衡路径长度和风险，从而发现规避风险的路径。使用基于样本的信息识别信念空间中的狭窄通道，同时使用顺序贝叶斯抽样来指导局部信念藤的扩展，确保高风险区域的连通性。给出了RA-RRTV*的渐近最优性证明。RA-RRTV*的有效性通过广泛的模拟和现实世界的实验证明。
{"title":"RA-RRTV*: Risk-Averse RRT* With Local Vine Expansion for Path Planning in Narrow Passages Under Localization Uncertainty","authors":"Shi Zhang;Rongxin Cui;Weisheng Yan;Yinglin Li","doi":"10.1109/LRA.2025.3528675","DOIUrl":"https://doi.org/10.1109/LRA.2025.3528675","url":null,"abstract":"Recent advances in sampling-based algorithms have enhanced the ability of mobile robots to navigate safely in environments with localization uncertainty. However, navigating narrow passages remains a significant challenge due to the heightened risks posed by uncertainty. In this letter, we present a novel algorithm, Risk-Averse RRT* with Local Vine Expansion Behavior (RA-RRTV*), to systematically address these challenges. The algorithm combines RRT* with chance constraints and incorporates an objective function to balance path length and risk, enabling the discovery of risk-averse paths. Narrow passages in the belief space are identified using sample-based information, while sequential Bayesian sampling is employed to guide the expansion of local belief vines, ensuring connectivity in high-risk regions. We provide proof of the asymptotic optimality of RA-RRTV*. The effectiveness of RA-RRTV* is demonstrated through extensive simulations and real-world experiments.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 2","pages":"2072-2079"},"PeriodicalIF":4.6,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-14DOI: 10.1109/LRA.2025.3528661
Junyi Shen;Tetsuro Miyazaki;Kenji Kawashima
Hysteresis brings challenges to both the control and state perception of soft robots. This work proposes a real-time gain-scheduled feedforward proportional controller design and a Physical Reservoir Computing (PRC) model to address hysteresis effects in the motion control and unobstructed state estimation of a dual pneumatic artificial muscle (PAM) soft bending actuator. The dual-PAM soft actuator comprises an active PAM used for actuation and a pressurized-and-sealed passive PAM serving as a physical reservoir and used for computation. The physical reservoir's state is reflected by the passive PAM's inner pressure and used for bending state estimation. Experiments exhibit the physical reservoir state's nonlinear responses to the active PAM's actuation inputs. The proposed feedforward controller improves the soft actuator's responsiveness in hysteresis dead zones by dynamically adjusting the feedforward proportional gain. The proposed controller outperforms a linear approximation-based feedforward controller in motion control, and the PRC-based bending state estimation model achieves higher accuracy than a comparative Echo State Network (ESN) with 1,000 neurons. The presented strategies are expected to benefit the precise motion control and unobstructed state estimation of soft actuators.
{"title":"Controlling Pneumatic Bending Actuator With Gain-Scheduled Feedforward and Physical Reservoir Computing State Estimation","authors":"Junyi Shen;Tetsuro Miyazaki;Kenji Kawashima","doi":"10.1109/LRA.2025.3528661","DOIUrl":"https://doi.org/10.1109/LRA.2025.3528661","url":null,"abstract":"Hysteresis brings challenges to both the control and state perception of soft robots. This work proposes a real-time gain-scheduled feedforward proportional controller design and a Physical Reservoir Computing (PRC) model to address hysteresis effects in the motion control and unobstructed state estimation of a dual pneumatic artificial muscle (PAM) soft bending actuator. The dual-PAM soft actuator comprises an active PAM used for actuation and a pressurized-and-sealed passive PAM serving as a physical reservoir and used for computation. The physical reservoir's state is reflected by the passive PAM's inner pressure and used for bending state estimation. Experiments exhibit the physical reservoir state's nonlinear responses to the active PAM's actuation inputs. The proposed feedforward controller improves the soft actuator's responsiveness in hysteresis dead zones by dynamically adjusting the feedforward proportional gain. The proposed controller outperforms a linear approximation-based feedforward controller in motion control, and the PRC-based bending state estimation model achieves higher accuracy than a comparative Echo State Network (ESN) with 1,000 neurons. The presented strategies are expected to benefit the precise motion control and unobstructed state estimation of soft actuators.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 3","pages":"2120-2127"},"PeriodicalIF":4.6,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Haptic feedback systems play a critical role in enriching the user experience in human-robot interaction. However, existing devices designed for evoking haptic sensations often face limitations owing to their low degree of freedom of deformation. In this study, we introduce the Funabot-Sleeve, a haptic device based on McKibben artificial muscles, and investigate its potential to evoke a range of haptic sensations using both steady-state and transient air pressure patterns. Our investigation examines the influence of these patterns on evoking distinct haptic sensations and identifies four specific sensations that can be evoked: Embraced, Pinched, a combination of Embraced and Pressed, and Twisted sensations. Across all participants, the evoked sensations showed positive correlations, with most correlations exceeding a value of 0.4, indicating a high degree of agreement in the sensations felt by the subjects. Our research lays the groundwork for the design of fabric actuators, capable of replicating specific stimuli and skin surface effects, thereby enabling a more sophisticated and personalized haptic feedback experience.
{"title":"Funabot-Sleeve: A Wearable Device Employing McKibben Artificial Muscles for Haptic Sensation in the Forearm","authors":"Yanhong Peng;Yusuke Sakai;Yuki Funabora;Kenta Yokoe;Tadayoshi Aoyama;Shinji Doki","doi":"10.1109/LRA.2025.3528229","DOIUrl":"https://doi.org/10.1109/LRA.2025.3528229","url":null,"abstract":"Haptic feedback systems play a critical role in enriching the user experience in human-robot interaction. However, existing devices designed for evoking haptic sensations often face limitations owing to their low degree of freedom of deformation. In this study, we introduce the Funabot-Sleeve, a haptic device based on McKibben artificial muscles, and investigate its potential to evoke a range of haptic sensations using both steady-state and transient air pressure patterns. Our investigation examines the influence of these patterns on evoking distinct haptic sensations and identifies four specific sensations that can be evoked: Embraced, Pinched, a combination of Embraced and Pressed, and Twisted sensations. Across all participants, the evoked sensations showed positive correlations, with most correlations exceeding a value of 0.4, indicating a high degree of agreement in the sensations felt by the subjects. Our research lays the groundwork for the design of fabric actuators, capable of replicating specific stimuli and skin surface effects, thereby enabling a more sophisticated and personalized haptic feedback experience.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 2","pages":"1944-1951"},"PeriodicalIF":4.6,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142992977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-13DOI: 10.1109/LRA.2025.3528225
Praveen Kumar Ranjan;Abhinav Sinha;Yongcan Cao
In this paper, we address the problem of enclosing an arbitrarily moving target in three dimensions by a single pursuer while ensuring the pursuer's safety by preventing collisions with the target. The proposed guidance strategy steers the pursuer to a safe region of space surrounding and excluding the target, allowing it to maintain a certain distance from the latter while offering greater flexibility in positioning and converging to any orbit within this safe zone. We leverage the concept of the Lyapunov Barrier Function as a powerful tool to constrain the distance between the pursuer and the target within asymmetric bounds, thereby ensuring the pursuer's safety within the predefined region. Further, we demonstrate the effectiveness of the proposed guidance law in managing arbitrarily maneuvering targets and other uncertainties (such as vehicle/autopilot dynamics and external disturbances) by enabling the pursuer to consistently achieve stable global enclosing behaviors by switching between stable enclosing trajectories within the safe region whenever necessary, even in response to aggressive target maneuvers. To attest to the merits of our work, we conduct experimental tests with various plant models, including a high-fidelity quadrotor model within Software-in-the-loop (SITL) simulations, encompassing various challenging target maneuver scenarios and requiring only relative information for successful execution.
{"title":"3D Guidance Law for Flexible Target Enclosing With Inherent Safety","authors":"Praveen Kumar Ranjan;Abhinav Sinha;Yongcan Cao","doi":"10.1109/LRA.2025.3528225","DOIUrl":"https://doi.org/10.1109/LRA.2025.3528225","url":null,"abstract":"In this paper, we address the problem of enclosing an arbitrarily moving target in three dimensions by a single pursuer while ensuring the pursuer's safety by preventing collisions with the target. The proposed guidance strategy steers the pursuer to a safe region of space surrounding and excluding the target, allowing it to maintain a certain distance from the latter while offering greater flexibility in positioning and converging to any orbit within this safe zone. We leverage the concept of the Lyapunov Barrier Function as a powerful tool to constrain the distance between the pursuer and the target within asymmetric bounds, thereby ensuring the pursuer's safety within the predefined region. Further, we demonstrate the effectiveness of the proposed guidance law in managing arbitrarily maneuvering targets and other uncertainties (such as vehicle/autopilot dynamics and external disturbances) by enabling the pursuer to consistently achieve stable global enclosing behaviors by switching between stable enclosing trajectories within the safe region whenever necessary, even in response to aggressive target maneuvers. To attest to the merits of our work, we conduct experimental tests with various plant models, including a high-fidelity quadrotor model within Software-in-the-loop (SITL) simulations, encompassing various challenging target maneuver scenarios and requiring only relative information for successful execution.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 2","pages":"2088-2095"},"PeriodicalIF":4.6,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142992991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-09DOI: 10.1109/LRA.2025.3527762
Jiaqiang Yang;Danyang Qin;Huapeng Tang;Sili Tao;Haoze Bie;Lin Ma
Visual self-localization technology is essential for unmanned aerial vehicles (UAVs) to achieve autonomous navigation and mission execution in environments where global navigation satellite system (GNSS) signals are unavailable. This technology estimates the UAV's geographic location by performing cross-view matching between UAV and satellite images. However, significant viewpoint differences between UAV and satellite images result in poor accuracy for existing cross-view matching methods. To address this, we integrate the DINOv2 model with UAV visual localization tasks and propose a DINOv2-based UAV visual self-localization method. Considering the inherent differences between pre-trained models and cross-view matching tasks, we propose a global-local feature adaptive enhancement method (GLFA). This method leverages Transformer and multi-scale convolutions to capture long-range dependencies and local spatial information in visual images, improving the model's ability to recognize key discriminative landmarks. In addition, we propose a cross-enhancement method based on a spatial pyramid (CESP), which constructs a multi-scale spatial pyramid to cross-enhance features, effectively improving the ability of the features to perceive multi-scale spatial information. Experimental results demonstrate that the proposed method achieves impressive scores of 86.27% in R@1 and 88.87% in SDM@1 on the DenseUAV public benchmark dataset, providing a novel solution for UAV visual self-localization.
{"title":"DINOv2-Based UAV Visual Self-Localization in Low-Altitude Urban Environments","authors":"Jiaqiang Yang;Danyang Qin;Huapeng Tang;Sili Tao;Haoze Bie;Lin Ma","doi":"10.1109/LRA.2025.3527762","DOIUrl":"https://doi.org/10.1109/LRA.2025.3527762","url":null,"abstract":"Visual self-localization technology is essential for unmanned aerial vehicles (UAVs) to achieve autonomous navigation and mission execution in environments where global navigation satellite system (GNSS) signals are unavailable. This technology estimates the UAV's geographic location by performing cross-view matching between UAV and satellite images. However, significant viewpoint differences between UAV and satellite images result in poor accuracy for existing cross-view matching methods. To address this, we integrate the DINOv2 model with UAV visual localization tasks and propose a DINOv2-based UAV visual self-localization method. Considering the inherent differences between pre-trained models and cross-view matching tasks, we propose a global-local feature adaptive enhancement method (GLFA). This method leverages Transformer and multi-scale convolutions to capture long-range dependencies and local spatial information in visual images, improving the model's ability to recognize key discriminative landmarks. In addition, we propose a cross-enhancement method based on a spatial pyramid (CESP), which constructs a multi-scale spatial pyramid to cross-enhance features, effectively improving the ability of the features to perceive multi-scale spatial information. Experimental results demonstrate that the proposed method achieves impressive scores of 86.27% in R@1 and 88.87% in SDM@1 on the DenseUAV public benchmark dataset, providing a novel solution for UAV visual self-localization.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 2","pages":"2080-2087"},"PeriodicalIF":4.6,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142992990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-09DOI: 10.1109/LRA.2025.3527757
Xiaozhu Lin;Xiaopei Liu;Yang Wang
The pursuit of agile and efficient underwater robots, especially bio-mimetic robotic fish, has been impeded by challenges in creating motion controllers that are able to fully exploit their hydrodynamic capabilities. This letter addresses these challenges by introducing a novel, model-free, end-to-end control framework that leverages Deep Reinforcement Learning (DRL) to enable agile and energy-efficient swimming of robotic fish. Unlike existing methods that rely on predefined trigonometric swimming patterns like Central Pattern Generators (CPG), our approach directly outputs low-level actuator commands without strong constraints, enabling the robotic fish to learn agile swimming behaviors. In addition, by integrating a high-performance Computational Fluid Dynamics (CFD) simulator with innovative sim-to-real strategies, such as normalized density calibration and servo response calibration, the proposed framework significantly mitigates the sim-to-real gap, facilitating direct transfer of control policies to real-world environments without fine-tuning. Comparative experiments demonstrate that our method achieves faster swimming speeds, smaller turn-around radii, and reduced energy consumption compared to the state-of-the-art swimming controllers. Furthermore, the proposed framework shows promise in addressing complex tasks, paving the way for more effective deployment of robotic fish in real aquatic environments.
{"title":"Learning Agile Swimming: An End-to-End Approach Without CPGs","authors":"Xiaozhu Lin;Xiaopei Liu;Yang Wang","doi":"10.1109/LRA.2025.3527757","DOIUrl":"https://doi.org/10.1109/LRA.2025.3527757","url":null,"abstract":"The pursuit of agile and efficient underwater robots, especially bio-mimetic robotic fish, has been impeded by challenges in creating motion controllers that are able to fully exploit their hydrodynamic capabilities. This letter addresses these challenges by introducing a novel, model-free, end-to-end control framework that leverages Deep Reinforcement Learning (DRL) to enable agile and energy-efficient swimming of robotic fish. Unlike existing methods that rely on predefined trigonometric swimming patterns like Central Pattern Generators (CPG), our approach directly outputs low-level actuator commands without strong constraints, enabling the robotic fish to learn agile swimming behaviors. In addition, by integrating a high-performance Computational Fluid Dynamics (CFD) simulator with innovative sim-to-real strategies, such as normalized density calibration and servo response calibration, the proposed framework significantly mitigates the sim-to-real gap, facilitating direct transfer of control policies to real-world environments without fine-tuning. Comparative experiments demonstrate that our method achieves faster swimming speeds, smaller turn-around radii, and reduced energy consumption compared to the state-of-the-art swimming controllers. Furthermore, the proposed framework shows promise in addressing complex tasks, paving the way for more effective deployment of robotic fish in real aquatic environments.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 2","pages":"1992-1999"},"PeriodicalIF":4.6,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142992983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Accurate tire modeling is crucial for optimizing autonomous racing vehicles, as State-of-the-Art (SotA) model-based techniques rely on precise knowledge of the vehicle's parameters, yet system identification in dynamic racing conditions is challenging due to varying track and tire conditions. Traditional methods require extensive operational ranges, often impractical in racing scenarios. Machine Learning (ML)-based methods, while improving performance, struggle with generalization and depend on accurate initialization. This paper introduces a novel on-track system identification algorithm, incorporating a Neural Network (NN) for error correction, which is then employed for traditional system identification with virtually generated data. Crucially, the process is iteratively reapplied, with tire parameters updated at each cycle, leading to notable improvements in accuracy in tests on a scaled vehicle. Experiments show that it is possible to learn a tire model without prior knowledge with only 30 seconds of driving data, and 3 seconds of training time. This method demonstrates greater one-step prediction accuracy than the baseline Nonlinear Least Squares (NLS) method under noisy conditions, achieving a 3.3x lower Root Mean Square Error (RMSE), and yields tire models with comparable accuracy to traditional steady-state system identification. Furthermore, unlike steady-state methods requiring large spaces and specific experimental setups, the proposed approach identifies tire parameters directly on a race track in dynamic racing environments.
{"title":"Learning-Based On-Track System Identification for Scaled Autonomous Racing in Under a Minute","authors":"Onur Dikici;Edoardo Ghignone;Cheng Hu;Nicolas Baumann;Lei Xie;Andrea Carron;Michele Magno;Matteo Corno","doi":"10.1109/LRA.2025.3527336","DOIUrl":"https://doi.org/10.1109/LRA.2025.3527336","url":null,"abstract":"Accurate tire modeling is crucial for optimizing autonomous racing vehicles, as State-of-the-Art (SotA) model-based techniques rely on precise knowledge of the vehicle's parameters, yet system identification in dynamic racing conditions is challenging due to varying track and tire conditions. Traditional methods require extensive operational ranges, often impractical in racing scenarios. Machine Learning (ML)-based methods, while improving performance, struggle with generalization and depend on accurate initialization. This paper introduces a novel on-track system identification algorithm, incorporating a Neural Network (NN) for error correction, which is then employed for traditional system identification with virtually generated data. Crucially, the process is iteratively reapplied, with tire parameters updated at each cycle, leading to notable improvements in accuracy in tests on a scaled vehicle. Experiments show that it is possible to learn a tire model without prior knowledge with only 30 seconds of driving data, and 3 seconds of training time. This method demonstrates greater one-step prediction accuracy than the baseline Nonlinear Least Squares (NLS) method under noisy conditions, achieving a 3.3x lower Root Mean Square Error (RMSE), and yields tire models with comparable accuracy to traditional steady-state system identification. Furthermore, unlike steady-state methods requiring large spaces and specific experimental setups, the proposed approach identifies tire parameters directly on a race track in dynamic racing environments.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 2","pages":"1984-1991"},"PeriodicalIF":4.6,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142992982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-08DOI: 10.1109/LRA.2025.3527338
Alper Ahmetoglu;Erhan Oztop;Emre Ugur
Discovering the symbols and rules that can be used in long-horizon planning from a robot's unsupervised exploration of its environment and continuous sensorimotor experience is a challenging task. The previous studies proposed learning symbols from single or paired object interactions and planning with these symbols. In this work, we propose a system that learns rules with discovered object and relational symbols that encode an arbitrary number of objects and the relations between them, converts those rules to Planning Domain Description Language (PDDL), and generates plans that involve affordances of the arbitrary number of objects to achieve tasks. We validated our system with box-shaped objects in different sizes and showed that the system can develop a symbolic knowledge of pick-up, carry, and place operations, taking into account object compounds in different configurations, such as boxes would be carried together with a larger box that they are placed on. We also compared our method with other symbol learning methods and showed that planning with the operators defined over relational symbols gives better planning performance compared to the baselines.
{"title":"Symbolic Manipulation Planning With Discovered Object and Relational Predicates","authors":"Alper Ahmetoglu;Erhan Oztop;Emre Ugur","doi":"10.1109/LRA.2025.3527338","DOIUrl":"https://doi.org/10.1109/LRA.2025.3527338","url":null,"abstract":"Discovering the symbols and rules that can be used in long-horizon planning from a robot's unsupervised exploration of its environment and continuous sensorimotor experience is a challenging task. The previous studies proposed learning symbols from single or paired object interactions and planning with these symbols. In this work, we propose a system that learns rules with discovered object and relational symbols that encode an arbitrary number of objects and the relations between them, converts those rules to Planning Domain Description Language (PDDL), and generates plans that involve affordances of the arbitrary number of objects to achieve tasks. We validated our system with box-shaped objects in different sizes and showed that the system can develop a symbolic knowledge of pick-up, carry, and place operations, taking into account object compounds in different configurations, such as boxes would be carried together with a larger box that they are placed on. We also compared our method with other symbol learning methods and showed that planning with the operators defined over relational symbols gives better planning performance compared to the baselines.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 2","pages":"1968-1975"},"PeriodicalIF":4.6,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142992980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-08DOI: 10.1109/LRA.2025.3527282
Seunghoon Yoo;Hyunjun Park;Youngsu Cha
This letter presents a novel cable-driven hybrid origami-inspired actuator with load-bearing capability. In contrast to conventional origami, the hybrid origami layer of the actuator is characterized by resilient hinges and rigid facets. The layers are bonded and assembled with the motors that apply tension via wires to generate a motion. The actuator exhibits high blocking force performance while preserving the large deformability of the conventional origami. To analyze the structure, a mathematical model is built using origami kinematics and elastic analysis. A hybrid origami tower with multiple layers is also suggested to show feasibility as a robot manipulator.
{"title":"Design and Analysis of a Hybrid Actuator With Resilient Origami-Inspired Hinges","authors":"Seunghoon Yoo;Hyunjun Park;Youngsu Cha","doi":"10.1109/LRA.2025.3527282","DOIUrl":"https://doi.org/10.1109/LRA.2025.3527282","url":null,"abstract":"This letter presents a novel cable-driven hybrid origami-inspired actuator with load-bearing capability. In contrast to conventional origami, the hybrid origami layer of the actuator is characterized by resilient hinges and rigid facets. The layers are bonded and assembled with the motors that apply tension via wires to generate a motion. The actuator exhibits high blocking force performance while preserving the large deformability of the conventional origami. To analyze the structure, a mathematical model is built using origami kinematics and elastic analysis. A hybrid origami tower with multiple layers is also suggested to show feasibility as a robot manipulator.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 3","pages":"2128-2135"},"PeriodicalIF":4.6,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-08DOI: 10.1109/LRA.2025.3527344
Yang Xu;Qiucan Huang;Shaojie Shen;Huan Yin
Radar SLAM is robust in challenging conditions, such as fog, dust, and smoke, but suffers from the sparsity and noisiness of radar sensing, including speckle noise and multipath effects. This study provides a performance-enhanced radar SLAM system by incorporating point uncertainty. The basic system is a radar-inertial odometry system that leverages velocity-aided radar points and high-frequency inertial measurements. We first propose to model the uncertainty of radar points in polar coordinates by considering the nature of radar sensing. Then, the proposed uncertainty model is integrated into the data association module and incorporated for back-end state estimation. Real-world experiments on both public and self-collected datasets validate the effectiveness of the proposed models and approaches. The findings highlight the potential of incorporating point uncertainty to improve the radar SLAM system.
{"title":"Incorporating Point Uncertainty in Radar SLAM","authors":"Yang Xu;Qiucan Huang;Shaojie Shen;Huan Yin","doi":"10.1109/LRA.2025.3527344","DOIUrl":"https://doi.org/10.1109/LRA.2025.3527344","url":null,"abstract":"Radar SLAM is robust in challenging conditions, such as fog, dust, and smoke, but suffers from the sparsity and noisiness of radar sensing, including speckle noise and multipath effects. This study provides a performance-enhanced radar SLAM system by incorporating point uncertainty. The basic system is a radar-inertial odometry system that leverages velocity-aided radar points and high-frequency inertial measurements. We first propose to model the uncertainty of radar points in polar coordinates by considering the nature of radar sensing. Then, the proposed uncertainty model is integrated into the data association module and incorporated for back-end state estimation. Real-world experiments on both public and self-collected datasets validate the effectiveness of the proposed models and approaches. The findings highlight the potential of incorporating point uncertainty to improve the radar SLAM system.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 3","pages":"2168-2175"},"PeriodicalIF":4.6,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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