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Distributed Coverage Control for Spatial Processes Estimation with Noisy Observations IEEE Robot. Automation Lett. (IF 5.2) Pub Date : 2024-03-26 Mattia Mantovani, Federico Pratissoli, Lorenzo Sabattini
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Modeling of a Six-Bar Tensegrity Robot Using the Port-Hamiltonian Framework and Experimental Validation IEEE Robot. Automation Lett. (IF 5.2) Pub Date : 2024-03-26 Songyuan Liu, Qingkai Yang, Jingshuo Lv, Hao Fang
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From Propeller Damage Estimation and Adaptation to Fault Tolerant Control: Enhancing Quadrotor Resilience IEEE Robot. Automation Lett. (IF 5.2) Pub Date : 2024-03-25 Jeffrey Mao, Jennifer Yeom, Suraj Nair, Giuseppe Loianno
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Towards stiffness tunable programmable matter IEEE Robot. Automation Lett. (IF 5.2) Pub Date : 2024-03-25 Lupo Manes, Sebastiano Fichera, Paolo Paoletti
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IEEE Robotics and Automation Society Information IEEE Robot. Automation Lett. (IF 5.2) Pub Date : 2024-03-25
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Whole-Body Control for Autonomous Landing of Unmanned Helicopter Equipped with Antagonistic Cable-Driven Legged Landing Gear IEEE Robot. Automation Lett. (IF 5.2) Pub Date : 2024-03-25 Baolin Tian, Haitao Yu, Zhen Yan, Hao Wang, Haibo Gao, Hongying Yu
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DSNet:Double Strand Robotic Grasp Detection Network Based on Cross Attention IEEE Robot. Automation Lett. (IF 5.2) Pub Date : 2024-03-25 Yonghong Zhang, Xiayang Qin, Tiantian Dong, Yuchao Li, Hongcheng Song, Yunping Liu, Ziqi Li, Qi Liu
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IEEE Robotics and Automation Letters Information for Authors IEEE Robot. Automation Lett. (IF 5.2) Pub Date : 2024-03-25
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Leadership Inference for Multi-Agent Interactions IEEE Robot. Automation Lett. (IF 5.2) Pub Date : 2024-03-25 Hamzah I. Khan, David Fridovich-Keil
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Understanding URDF: A Dataset and Analysis IEEE Robot. Automation Lett. (IF 5.2) Pub Date : 2024-03-25 Daniella Tola, Peter Corke
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IEEE Robotics and Automation Society Information IEEE Robot. Automation Lett. (IF 5.2) Pub Date : 2024-03-25
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GMPC: Geometric Model Predictive Control for Wheeled Mobile Robot Trajectory Tracking IEEE Robot. Automation Lett. (IF 5.2) Pub Date : 2024-03-25 Jiawei Tang, Shuang Wu, Bo Lan, Yahui Dong, Yuqiang Jin, Guangjian Tian, Wen-An Zhang, Ling Shi
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Intersection-Free Robot Manipulation With Soft-Rigid Coupled Incremental Potential Contact IEEE Robot. Automation Lett. (IF 5.2) Pub Date : 2024-03-22 Wenxin Du, Siqiong Yao, Xinlei Wang, Yuhang Xu, Wenqiang Xu, Cewu Lu
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Dedicated Dynamic Parameter Identification for Delta-Like Robots IEEE Robot. Automation Lett. (IF 5.2) Pub Date : 2024-03-22 D. Gnad, H. Gattringer, A. Müller, W. Höbarth, R. Riepl, L. Messner
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A Hybrid Approach for Cross-modality Pose Estimation Between Image and Point Cloud IEEE Robot. Automation Lett. (IF 5.2) Pub Date : 2024-03-22 Ze Huang, Li Sun, Qibin He, Zhongyang Xiao, Xinhui Bai, Hongyuan Yuan, Songzhi Su, Li Zhang
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FGCT6D: Frequency-Guided CNN-Transformer Fusion Network for Metal Parts' Robust 6D Pose Estimation IEEE Robot. Automation Lett. (IF 5.2) Pub Date : 2024-03-22 Han Sun, Zhenning Zhou, Yizhao Wang, Zhuangzhuang Zhang, Qixin Cao
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Planning With Purpose: Task-Specific Trajectory Optimization IEEE Robot. Automation Lett. (IF 5.2) Pub Date : 2024-03-22 Yinan Pei, Yuri Ivanov
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Commonsense-Aware Object Value Graph for Object Goal Navigation IEEE Robot. Automation Lett. (IF 5.2) Pub Date : 2024-03-22 Hwiyeon Yoo, Yunho Choi, Jeongho Park, Songhwai Oh
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Cooperative Object Transport by Two Robots Connected With a Ball-String-Ball Structure IEEE Robot. Automation Lett. (IF 5.2) Pub Date : 2024-03-21 Yunke Huang, Shuai Zhang
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PNAS-MOT: Multi-Modal Object Tracking With Pareto Neural Architecture Search IEEE Robot. Automation Lett. (IF 5.2) Pub Date : 2024-03-21 Chensheng Peng, Zhaoyu Zeng, Jinling Gao, Jundong Zhou, Masayoshi Tomizuka, Xinbing Wang, Chenghu Zhou, Nanyang Ye
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An Inverse Kinematics Algorithm With Smooth Task Switching for Redundant Robots IEEE Robot. Automation Lett. (IF 5.2) Pub Date : 2024-03-21 Hannes Gamper, Laura Rodrigo Pérez, Andreas Mueller, Alejandro Díaz Rosales, Mario Di Castro
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Asymptotically Optimal A* for Kinodynamic Planning IEEE Robot. Automation Lett. (IF 5.2) Pub Date : 2024-03-20 Maciej Przybylski
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Autonomous Landing on a Moving Platform Using Vision-Based Deep Reinforcement Learning IEEE Robot. Automation Lett. (IF 5.2) Pub Date : 2024-03-20 Pawel Ladosz, Meraj Mammadov, Heejung Shin, Woojae Shin, Hyondong Oh
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MACIM: Multi-Agent Collaborative Implicit Mapping IEEE Robot. Automation Lett. (IF 5.2) Pub Date : 2024-03-20 Yinan Deng, Yujie Tang, Yi Yang, Danwei Wang, Yufeng Yue
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Adaptive Time-Delay Attitude Control of Jumping Robots Based on Voltage Control Model IEEE Robot. Automation Lett. (IF 5.2) Pub Date : 2024-03-20 Ming Pi
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Design of a Human-inspired Sensorized and Adaptive Foot that Enhances Stability through Tensegrity (Hi-SAFEST) IEEE Robot. Automation Lett. (IF 5.2) Pub Date : 2024-03-20 Hoyeon Yeom, Gunoo Park, Joonbum Bae
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Visual-Force-Tactile Fusion for Gentle Intricate Insertion Tasks IEEE Robot. Automation Lett. (IF 5.2) Pub Date : 2024-03-20 Piaopiao Jin, Bidan Huang, Wang Wei Lee, Tiefeng Li, Wei Yang
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Star-Searcher: A Complete and Efficient Aerial System for Autonomous Target Search in Complex Unknown Environments IEEE Robot. Automation Lett. (IF 5.2) Pub Date : 2024-03-20 Yiming Luo, Zixuan Zhuang, Neng Pan, Chen Feng, Shaojie Shen, Fei Gao, Hui Cheng, Boyu Zhou
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Safety-Aware Causal Representation for Trustworthy Offline Reinforcement Learning in Autonomous Driving IEEE Robot. Automation Lett. (IF 5.2) Pub Date : 2024-03-20 Haohong Lin, Wenhao Ding, Zuxin Liu, Yaru Niu, Jiacheng Zhu, Yuming Niu, Ding Zhao
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Transfer Learning for Efficient Intent Prediction in Lower-Limb Prosthetics: A Strategy for Limited Datasets IEEE Robot. Automation Lett. (IF 5.2) Pub Date : 2024-03-20 Duong Le, Shihao Cheng, Robert D. Gregg, Maani Ghaffari
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Deep Reinforcement Learning-based Large-scale Robot Exploration IEEE Robot. Automation Lett. (IF 5.2) Pub Date : 2024-03-20 Yuhong Cao, Rui Zhao, Yizhuo Wang, Bairan Xiang, Guillaume Sartoretti
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High Precision 6-DoF Grasp Detection in Cluttered Scenes Based on Network Optimization and Pose Propagation IEEE Robot. Automation Lett. (IF 5.2) Pub Date : 2024-03-19 Wenjun Tang, Kai Tang, Bin Zi, Sen Qian, Dan Zhang
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DIVIDE: Learning a Domain-Invariant Geometric Space for Depth Estimation IEEE Robot. Automation Lett. (IF 5.2) Pub Date : 2024-03-18 Dongseok Shim, H. Jin Kim
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Trimodal Navigable Region Segmentation Model: Grounding Navigation Instructions in Urban Areas IEEE Robot. Automation Lett. (IF 5.2) Pub Date : 2024-03-18 Naoki Hosomi, Shumpei Hatanaka, Yui Iioka, Wei Yang, Katsuyuki Kuyo, Teruhisa Misu, Kentaro Yamada, Komei Sugiura
In this study, we develop a model that enables mobilities to have more friendly interactions with users. Specifically, we focus on the referring navigable regions task in which a model grounds navigable regions of the road using the mobility's camera image and natural language navigation instructions. This task is challenging because of the requirement of vision-and-language comprehension in situations
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Embodied Intelligence: Bionic Robot Controller Integrating Environment Perception, Autonomous Planning, and Motion Control IEEE Robot. Automation Lett. (IF 5.2) Pub Date : 2024-03-18 Yahui Gan, Bo Zhang, Jiawei Shao, Zao Han, Ang Li, Xianzhong Dai
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Prior Information-Assisted Neural Network for Point Cloud Segmentation in Human-Robot Interaction Scenarios IEEE Robot. Automation Lett. (IF 5.2) Pub Date : 2024-03-18 Jingxin Lin, Kaifan Zhong, Tao Gong, Xianmin Zhang, Nianfeng Wang
This letter proposes a prior information-assisted (PIA) point cloud segmentation network that can be effectively applied to point cloud segmentation applications in human-robot interaction scenario. The joint angles of the robots are used as prior information, which is fed into the network as an additional input in the form of a vector along with the actual point cloud of the target scene. The PIA
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Enhancing VIO Robustness under Sudden Lighting Variation: A Learning-Based IMU Dead-Reckoning for UAV Localization IEEE Robot. Automation Lett. (IF 5.2) Pub Date : 2024-03-18 Daolong Yang, Haoyuan Liu, Xueying Jin, Jiawei Chen, Chengcai Wang, Xilun Ding, Kun Xu
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Inchworm-inspired soft robot with controllable locomotion based on self-sensing of deformation IEEE Robot. Automation Lett. (IF 5.2) Pub Date : 2024-03-18 Yue Di, Yuyan Zhang, Yintang Wen, Haiying Yao, Zixiang Zhou, Zhixin Ren, Hongmiao Tian, Jinyou Shao
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RMSC-VIO: Robust Multi-Stereoscopic Visual-Inertial Odometry for Local Visually Challenging Scenarios IEEE Robot. Automation Lett. (IF 5.2) Pub Date : 2024-03-18 Tong Zhang, Jianyu Xu, Hao Shen, Rui Yang, Tao Yang
We present a Multi-Stereoscopic Visual-Inertial Odometry (VIO) system capable of integrating an arbitrary number of stereo cameras, exhibiting excellent robustness in the face of visually challenging scenarios. During system initialization, we introduce multi-view keyframes for simultaneous processing of multiple image inputs and propose an adaptive feature selection method to alleviate the computational
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Self-Supervised Multi-Modal Learning for Collaborative Robotic Grasp-Throw IEEE Robot. Automation Lett. (IF 5.2) Pub Date : 2024-03-18 Yanxu Hou, Zihan Fang, Jun Li
Accurate throwing skills can expand the pick-and-place ability of a manipulator, which is significant but challenging in the field of robotics. Most existing robotic throwing methods neglect the mass of an object and air drag, not to mention the effect of a grasp on the subsequent throw, resulting in inaccurate throws. In this regard, we propose collaborative grasping and throwing learning (CGTL).
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High-Speed Detector For Low-Powered Devices In Aerial Grasping IEEE Robot. Automation Lett. (IF 5.2) Pub Date : 2024-03-18 Ashish Kumar, Laxmidhar Behera
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Hyperboloidal Pneumatic Artificial Muscle With Braided Straight Fibers IEEE Robot. Automation Lett. (IF 5.2) Pub Date : 2024-03-14 Masahiro Watanabe, Kenjiro Tadakuma, Satoshi Tadokoro
This letter introduces the development and analysis of a hyperboloidal pneumatic artificial muscle (h-PAM) utilizing braided straight fibers, aimed at overcoming the limitations of traditional pneumatic artificial muscles (PAMs). The novel design features a hyperboloidal rubber tube coupled with a braided shell of straight fibers, enhancing both contraction performance and flexibility, a significant
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Seamless Virtual Reality With Integrated Synchronizer and Synthesizer for Autonomous Driving IEEE Robot. Automation Lett. (IF 5.2) Pub Date : 2024-03-14 He Li, Ruihua Han, Zirui Zhao, Wei Xu, Qi Hao, Shuai Wang, Chengzhong Xu
Virtual reality (VR) is a promising data engine for autonomous driving (AD). However, data fidelity in this paradigm is often degraded by VR inconsistency, for which the existing VR approaches become ineffective, as they ignore the inter-dependency between low-level VR synchronizer designs (i.e., data collector) and high-level VR synthesizer designs (i.e., data processor). This paper presents a seamless
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Stein Variational Belief Propagation for Multi-Robot Coordination IEEE Robot. Automation Lett. (IF 5.2) Pub Date : 2024-03-14 Jana Pavlasek, Joshua Jing Zhi Mah, Ruihan Xu, Odest Chadwicke Jenkins, Fabio Ramos
Decentralized coordination for multi-robot systems involves planning in challenging, high-dimensional spaces. The planning problem is particularly challenging in the presence of obstacles and different sources of uncertainty such as inaccurate dynamic models and sensor noise. In this letter, we introduce Stein Variational Belief Propagation (SVBP), a novel algorithm for performing inference over nonparametric
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mmPlace: Robust Place Recognition With Intermediate Frequency Signal of Low-Cost Single-Chip Millimeter Wave Radar IEEE Robot. Automation Lett. (IF 5.2) Pub Date : 2024-03-14 Chengzhen Meng, Yifan Duan, Chenming He, Dequan Wang, Xiaoran Fan, Yanyong Zhang
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Manipulability-Augmented Next-Best-Configuration Exploration Planner for High-DoF Manipulators IEEE Robot. Automation Lett. (IF 5.2) Pub Date : 2024-03-14 Xin Liu, Xuebo Zhang, Shiyong Zhang, Mingxing Yuan, Jingjin Yu
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Modular Multi-Level Replanning TAMP Framework for Dynamic Environment IEEE Robot. Automation Lett. (IF 5.2) Pub Date : 2024-03-14 Tao Lin, Chengfei Yue, Ziran Liu, Xibin Cao
Task and Motion Planning (TAMP) algorithms can generate plans that combine logic and motion aspects for robots. However, these plans are sensitive to interference and control errors. To make TAMP algorithms more applicable and robust in the real world, we propose the m odular m ulti-level r eplanning TAMP f ramework(MMRF), expanded existing TAMP algorithms by combining real-time replanning components
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A two-chamber soft actuator with an expansion limit line for force enhancement IEEE Robot. Automation Lett. (IF 5.2) Pub Date : 2024-03-13 Jingon Yoon, Junmo Yang, Dongwon Yun
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SIM-Sync: From Certifiably Optimal Synchronization Over the 3D Similarity Group to Scene Reconstruction With Learned Depth IEEE Robot. Automation Lett. (IF 5.2) Pub Date : 2024-03-13 Xihang Yu, Heng Yang
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Single-Motor Robotic Gripper With Multi-Surface Fingers for Variable Grasping Configurations IEEE Robot. Automation Lett. (IF 5.2) Pub Date : 2024-03-13 Toshihiro Nishimura, Yosuke Suzuki, Tokuo Tsuj, Tetsuyou Watanabe
This study proposes a novel robotic gripper with variable grasping configurations for grasping various objects. The fingers of the developed gripper incorporate multiple different surfaces. The gripper possesses the function of altering the finger surfaces facing a target object by rotating the fingers in its longitudinal direction. In the proposed design equipped with two fingers, the two fingers
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Robust Visual Place Recognition for Severe Appearance Changes IEEE Robot. Automation Lett. (IF 5.2) Pub Date : 2024-03-13 Haiyang Jiang, Songhao Piao, Huai Yu, Wei Li, Lei Yu
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Paper-based bistable origami gripper to make quadcopters multi-functional IEEE Robot. Automation Lett. (IF 5.2) Pub Date : 2024-03-13 Shuta Okamoto, Yuki Fukatsu, Chinthaka Premachandra, Hiroki Shigemune
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Development of a Dual Function Joint Modular Soft Actuator and its Evaluation Using a Novel Dummy Finger Joint-Soft Actuator Complex Model IEEE Robot. Automation Lett. (IF 5.2) Pub Date : 2024-03-13 Pablo E. Tort贸s-Vinocour, Shota Kokubu, Fuko Matsunaga, Yuxi Lu, Zhongchao Zhou, Jose Gomez-Tames, Wenwei Yu
Soft actuators, made from soft materials, offer a safer alternative to rigid robots for use on hand rehabilitation devices. A current challenge is to ensure these actuators comply with human finger morphology. To gain better insights into actuator mechanics when worn on and interacting with human fingers, combining physical experiments with simulation approaches is necessary. However, no simulation
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Modeling and Experimental Verification of a Continuous Curvature-Based Soft Growing Manipulator IEEE Robot. Automation Lett. (IF 5.2) Pub Date : 2024-03-08 Justin Allen, Ryan Dorosh, Chris Ninatanta, Andrew Allen, Linlin Shui, Kyle Yoshida, Jiecai Luo, Ming Luo
Soft robots show significant potential for use in search and rescue, human-robot interaction, and other emerging fields due to their ability to easily conform, deform, and interact with their environment. However, precise control of these soft robots is still being explored. In this letter, we investigate a potential solution to address the limitations of precise control for soft robots. We experimentally
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Viewpoint-Aware Visibility Scoring for Point Cloud Registration in Loop Closure IEEE Robot. Automation Lett. (IF 5.2) Pub Date : 2024-03-12 Ilseung Yoon, Tariq Islam, Kwangrok Kim, Cheolhyeon Kwon
LiDAR-based Simultaneous Localization and Mapping (SLAM) encounters a substantial challenge in the form of accumulating errors, which can adversely impact its reliability. Loop closing techniques have been extensively employed to counteract this issue. Nonetheless, the loop closing conundrum remains difficult to resolve, as point clouds often exhibit partial overlap due to disparities in scanning pose
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Learning Self-Supervised Traversability With Navigation Experiences of Mobile Robots: A Risk-Aware Self-Training Approach IEEE Robot. Automation Lett. (IF 5.2) Pub Date : 2024-03-12 Ikhyeon Cho, Woojin Chung
Mobile robots operating in outdoor environments face the challenge of navigating various terrains with different degrees of difficulty. Therefore, traversability estimation is crucial for safe and efficient robot navigation. Current approaches utilize a robot's driving experience to learn traversability in a self-supervised fashion. However, providing sufficient and diverse experience to the robot
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MorAL: Learning Morphologically Adaptive Locomotion Controller for Quadrupedal Robots on Challenging Terrains IEEE Robot. Automation Lett. (IF 5.2) Pub Date : 2024-03-12 Zeren Luo, Yinzhao Dong, Xinqi Li, Rui Huang, Zhengjie Shu, Erdong Xiao, Peng Lu
Due to the rapid development of the quadruped robot industry in the past decade, various commercial quadruped robots have emerged with distinct physical attributes. Different from the previous work in which the designed controller is robot-specific, this article proposes a learning-based control framework – MorAL, which is adaptive to different morphologies of quadruped robots and challenging terrains
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A Safe Preference Learning Approach for Personalization With Applications to Autonomous Vehicles IEEE Robot. Automation Lett. (IF 5.2) Pub Date : 2024-03-11 Ruya Karagulle, Nikos Aréchiga, Andrew Best, Jonathan DeCastro, Necmiye Ozay
This letter introduces a preference learning method that ensures adherence to given specifications, with an application to autonomous vehicles. Our approach incorporates the priority ordering of Signal Temporal Logic (STL) formulas describing traffic rules into a learning framework. By leveraging Parametric Weighted Signal Temporal Logic (PWSTL), we formulate the problem of safety-guaranteed preference
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Continuous Object State Recognition for Cooking Robots Using Pre-Trained Vision-Language Models and Black-Box Optimization IEEE Robot. Automation Lett. (IF 5.2) Pub Date : 2024-03-11 Kento Kawaharazuka, Naoaki Kanazawa, Yoshiki Obinata, Kei Okada, Masayuki Inaba
The state recognition of the environment and objects by robots is generally based on the judgement of the current state as a classification problem. On the other hand, state changes of food in cooking happen continuously and need to be captured not only at a certain time point but also continuously over time. In addition, the state changes of food are complex and cannot be easily described by manual