IEEE Transactions on Robotics最新文献_第10页

Traversability-Aware Legged Navigation by Learning From Real-World Visual Data 从真实世界的视觉数据中学习的可穿越性感知腿导航

IF 10.5 1区计算机科学

IEEE Transactions on Robotics Pub Date : 2025-12-18 DOI: 10.1109/TRO.2025.3645885

Hongbo Zhang;Zhongyu Li;Xuanqi Zeng;Laura Smith;Kyle Stachowicz;Dhruv Shah;Linzhu Yue;Zhitao Song;Weipeng Xia;Sergey Levine;Koushil Sreenath;Yun-hui Liu

{"title":"Traversability-Aware Legged Navigation by Learning From Real-World Visual Data","authors":"Hongbo Zhang;Zhongyu Li;Xuanqi Zeng;Laura Smith;Kyle Stachowicz;Dhruv Shah;Linzhu Yue;Zhitao Song;Weipeng Xia;Sergey Levine;Koushil Sreenath;Yun-hui Liu","doi":"10.1109/TRO.2025.3645885","DOIUrl":"10.1109/TRO.2025.3645885","url":null,"abstract":"The enhanced mobility brought by legged locomotion empowers quadrupedal robots to navigate through complex and unstructured environments. However, optimizing agile locomotion while accounting for the varying energy costs of traversing different terrains remains an open challenge. Most previous work focuses on planning trajectories with traversability cost estimation based on human-labeled environmental features. This human-centric approach is insufficient because it does not account for the varying capabilities of the robot locomotion controllers over challenging terrains. To address this, we introduce a novel real-world learning pipeline that unifies offline demonstrations, online reinforcement learning, and multimodal perception to achieve robust legged navigation. The framework employs multiple training stages to develop a planner that guides the robot in avoiding obstacles and hard-to-traverse terrains while reaching its goals. We first develop a novel traversability estimator in a robot-centric manner. The training of the navigation planner is directly performed in the real world using a sample efficient reinforcement learning method. With the proposed method, a quadrupedal robot learns to perform traversability-aware navigation through real-world interactions in diverse offroad and unstructured environments. Moreover, the robot demonstrates the ability to generalize the learned navigation skills to unseen scenarios.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"42 ","pages":"400-417"},"PeriodicalIF":10.5,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145777819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Rethink Repeatable Measures of Robot Performance With Statistical Query 用统计查询重新思考机器人性能的可重复度量

IF 10.5 1区计算机科学

IEEE Transactions on Robotics Pub Date : 2025-12-18 DOI: 10.1109/TRO.2025.3645934

Bowen Weng;Linda Capito;Guillermo A. Castillo;Dylan Khor

引用次数: 0

Probabilistic Modeling and Control for Multi-UAV Search Over Uneven Terrain 多无人机非均匀地形搜索的概率建模与控制

IF 10.5 1区计算机科学

IEEE Transactions on Robotics Pub Date : 2025-12-18 DOI: 10.1109/TRO.2025.3645884

Luka Lanča;Karlo Jakac;Stefan Ivić

{"title":"Probabilistic Modeling and Control for Multi-UAV Search Over Uneven Terrain","authors":"Luka Lanča;Karlo Jakac;Stefan Ivić","doi":"10.1109/TRO.2025.3645884","DOIUrl":"10.1109/TRO.2025.3645884","url":null,"abstract":"This article addresses survey missions involving multiple uncrewed aerial vehicles (UAVs) over complex, varying terrain. The methodology integrates a probabilistic model of target’s position uncertainty with UAV flight dynamics, camera properties, and a machine learning-based detection system. It estimates undetected target probability and overall search performance, feeding into a feedback loop that combines 2-D ergodic search with model predictive control (MPC) of UAV altitude and velocity. Trial trajectory optimization accounts for sensing characteristics and operational constraints, producing terrain-aware, collision-free trajectories that balance area coverage with target detection. Simulations demonstrate the integration of MPC and ergodic search, enabling dynamic altitude adjustments to enhance the search performance. The control algorithm operates in real time and performs reliably under uncertainty. Field experiments provided training data, validated the method, and confirmed compliance with motion constraints. Detection rates closely match model predictions, demonstrating stable performance even under significant deviations from ideal conditions. The framework, thus, offers a reliable solution for autonomous multi-UAV search operations in real-world environments.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"42 ","pages":"418-438"},"PeriodicalIF":10.5,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11304178","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145777820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ADR-PNAS: A Novel Sim-to-Real Transfer Approach for Robotic Manipulation Tasks ADR-PNAS：一种机器人操作任务模拟到真实的新方法

IF 10.5 1区计算机科学

IEEE Transactions on Robotics Pub Date : 2025-12-17 DOI: 10.1109/TRO.2025.3644950

Yu Nong

{"title":"ADR-PNAS: A Novel Sim-to-Real Transfer Approach for Robotic Manipulation Tasks","authors":"Yu Nong","doi":"10.1109/TRO.2025.3644950","DOIUrl":"10.1109/TRO.2025.3644950","url":null,"abstract":"Sim-to-real transfer in robotic manipulation tasks has emerged as a crucial area of research, addressing the challenge of bridging the gap between simulated environments and real-world applications. This article presents a brief review of current methodologies and introduces novel approaches to enhance the efficacy of sim-to-real transfer. We propose a new framework, adaptive domain randomization (DR) with progressive neural architecture search, which combines adaptive DR techniques with neural architecture search to optimize both the simulation parameters and the neural network architecture for improved transfer. We evaluate our approach against multiple state-of-the-art baselines, including standard DR, adaptive DR, model-agnostic meta-learning, randomized-to-canonical adaptation networks, and ensemble policy learning. Our experiments on a diverse set of manipulation tasks demonstrate significant improvements in the transfer performance, with up to 35% reduction in reality gap compared to state-of-the-art methods. Furthermore, we introduce a novel metric, the transfer efficiency index, to quantify the effectiveness of sim-to-real transfer across different tasks and methodologies.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"42 ","pages":"770-781"},"PeriodicalIF":10.5,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145770771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Data-Efficient and Predefined-Time Stable Control for Continuum Robots 连续体机器人的数据高效和预定义时间稳定控制

IF 10.5 1区计算机科学

IEEE Transactions on Robotics Pub Date : 2025-12-16 DOI: 10.1109/TRO.2025.3644946

Peng Yu;Zhenhan Liang;Ning Tan

{"title":"Data-Efficient and Predefined-Time Stable Control for Continuum Robots","authors":"Peng Yu;Zhenhan Liang;Ning Tan","doi":"10.1109/TRO.2025.3644946","DOIUrl":"10.1109/TRO.2025.3644946","url":null,"abstract":"Inspired by soft creatures and structures in nature, continuum robots exhibit remarkable flexibility, safe interaction, and ease of miniaturization, showcasing vast application potential. However, their flexible structure renders analytical methods inadequate for precise modeling and control, while existing data-driven approaches suffer from low data efficiency and unproven theoretical control performance. This article aims to achieve data-efficient modeling and reliable control of continuum robots through innovative algorithms, exploring the performance of the new method from both theoretical and experimental perspectives. Specifically, we utilize neural ordinary differential equations (NODE) to achieve data-efficient modeling of continuum robots and investigate the performance of the modeling method. Then, we propose a novel predefined-time-synchronized stable zeroing neurodynamics (PTSS-ZND) model. By combining the NODE method and the PTSS-ZND method, we propose a reliable data-driven control system. Through rigorous theoretical analysis, we prove the stability and predefined-time convergence of the data-driven control system. Finally, through simulations and physical experiments, we validate the feasibility and convergence of the novel method and its advantages over existing data-driven methods. Experiments on one-segment and three-segment continuum robots indicate that the proposed method achieves a root mean square position error (e.g., 2.5 mm for the three-segment robot) of less than 1% of the robot length using fewer than 100 data samples. Our method also demonstrates robust performance under various external and internal disturbances. In addition, it can potentially be extended for end-effector pose control.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"42 ","pages":"382-399"},"PeriodicalIF":10.5,"publicationDate":"2025-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145770774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

One Filter to Deploy Them All: Robust Safety for Quadrupedal Navigation in Unknown Environments 一个过滤器部署它们：未知环境中四足导航的鲁棒安全性

IF 10.5 1区计算机科学

IEEE Transactions on Robotics Pub Date : 2025-12-16 DOI: 10.1109/TRO.2025.3644957

Albert Lin;Shuang Peng;Somil Bansal

{"title":"One Filter to Deploy Them All: Robust Safety for Quadrupedal Navigation in Unknown Environments","authors":"Albert Lin;Shuang Peng;Somil Bansal","doi":"10.1109/TRO.2025.3644957","DOIUrl":"10.1109/TRO.2025.3644957","url":null,"abstract":"As learning-based methods for legged robots rapidly grow in popularity, it is important that we can provide safety assurances efficiently across different controllers and environments. Existing works either rely on <italic>a priori</i> knowledge of the environment and safety constraints to ensure system safety or provide assurances for a specific locomotion policy. To address these limitations, we propose an observation-conditioned reachability-based (OCR) safety-filter framework. Our key idea is to use an OCR value network (OCR-VN) that predicts the optimal control-theoretic safety value function for new failure regions and dynamic uncertainty during deployment time. Specifically, the OCR-VN facilitates rapid safety adaptation through two key components: a LiDAR-based input that allows the dynamic construction of safe regions in light of new obstacles and a disturbance estimation module that accounts for dynamics uncertainty in the wild. The predicted safety value function is used to construct an adaptive safety filter that overrides the nominal quadruped controller when necessary to maintain safety. Through simulation studies and hardware experiments on a Unitree Go1 quadruped in agile planar navigation tasks, we demonstrate that the proposed framework can automatically safeguard a wide range of hierarchical quadruped controllers, adapts to novel environments, and is robust to unmodeled dynamics <italic>without a priori access to the controllers or environments</i>—hence, “One Filter to Deploy Them All.”","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"42 ","pages":"545-560"},"PeriodicalIF":10.5,"publicationDate":"2025-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145770770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Actor–Critic Model Predictive Control: Differentiable Optimization Meets Reinforcement Learning for Agile Flight 参与者-评论家模型预测控制：可微优化与敏捷飞行的强化学习

IF 10.5 1区计算机科学

IEEE Transactions on Robotics Pub Date : 2025-12-16 DOI: 10.1109/TRO.2025.3644945

Angel Romero;Elie Aljalbout;Yunlong Song;Davide Scaramuzza

{"title":"Actor–Critic Model Predictive Control: Differentiable Optimization Meets Reinforcement Learning for Agile Flight","authors":"Angel Romero;Elie Aljalbout;Yunlong Song;Davide Scaramuzza","doi":"10.1109/TRO.2025.3644945","DOIUrl":"10.1109/TRO.2025.3644945","url":null,"abstract":"A key open challenge in agile quadrotor flight is how to combine the flexibility and task-level generality of model-free reinforcement learning (RL) with the structure and online replanning capabilities of model predictive control (MPC), aiming to leverage their complementary strengths in dynamic and uncertain environments. This article provides an answer by introducing a new framework called <italic>Actor–Critic MPC</i>. The key idea is to embed a differentiable MPC within an actor–critic RL framework. This integration allows for short-term predictive optimization of control actions through MPC, while leveraging RL for end-to-end learning and exploration over longer horizons. Through various ablation studies, conducted in the context of agile quadrotor racing, we expose the benefits of the proposed approach: it achieves better out-of-distribution behavior, better robustness to changes in the quadrotor's dynamics and improved sample efficiency. In addition, we conduct an empirical analysis using a quadrotor platform that reveals a relationship between the critic's learned value function and the cost function of the differentiable MPC, providing a deeper understanding of the interplay between the critic's value and the MPC cost functions. Finally, we validate our method in a drone racing task on different tracks, in both simulation and the real world. Our method achieves the same superhuman performance as state-of-the-art model-free RL, showcasing speeds of up to 21 m/s. We show that the proposed architecture can achieve real-time control performance, learn complex behaviors via trial and error, and retain the predictive properties of the MPC to better handle out-of-distribution behavior.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"42 ","pages":"673-692"},"PeriodicalIF":10.5,"publicationDate":"2025-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145770769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Power of Persuasion: How Social Robots Influence Our Decisions in Collaborative Activities 说服的力量：社交机器人如何影响我们在合作活动中的决策

IF 10.5 1区计算机科学

IEEE Transactions on Robotics Pub Date : 2025-12-15 DOI: 10.1109/TRO.2025.3644350

Marcos Maroto-Gómez;Sara Carrasco-Martínez;Sofía Álvarez-Arias;Enrique Fernández-Rodicio;María Malfaz

{"title":"The Power of Persuasion: How Social Robots Influence Our Decisions in Collaborative Activities","authors":"Marcos Maroto-Gómez;Sara Carrasco-Martínez;Sofía Álvarez-Arias;Enrique Fernández-Rodicio;María Malfaz","doi":"10.1109/TRO.2025.3644350","DOIUrl":"10.1109/TRO.2025.3644350","url":null,"abstract":"Social robots are increasingly used in healthcare and education, but technological gaps, fears of human replacement, and moral or social beliefs can limit their acceptance. In collaborative settings, the activities to complete may influence users’ willingness to participate, raising the question of how moral and social attitudes shape human–robot interaction. This article studies the effect of the social judgment theory on social robotics to analyze which factors affect the users’ willingness to complete the robot’s requests. The methodology classifies the activities requested by the robot into assimilation (activities people typically accept), noncommitment (activities people usually reject), and the contrast (activities some people accept) groups. We conducted a user study with 63 participants interacting with the Mini social robot in a collaborative session where it requested some actions from the user. We analyze whether the kind of activity requested by the robot, its expressiveness, and demographic, moral, social, and robot factors influence the user behavior. Results show that the social judgment theory can be extended to social robotics since the kind of activity affects the user’s willingness to complete it. Besides, the results indicate that an expressive robot convinced users more than a nonexpressive robot and that participants who lied about their completed activities were more easily persuaded. We also found that participants with moderate knowledge of robotics completed more activities than those with low knowledge, and individuals with previous experience interacting with Mini were more likely to comply with its requests. However, demographic factors such as age or gender do not seem to influence robot persuasion despite previous studies suggesting they are important in human–robot collaboration.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"42 ","pages":"495-508"},"PeriodicalIF":10.5,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11300827","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145759503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Curvature-Constrained Vector Field for Motion Planning of Nonholonomic Robots 非完整机器人运动规划的曲率约束向量场

IF 10.5 1区计算机科学

IEEE Transactions on Robotics Pub Date : 2025-12-15 DOI: 10.1109/TRO.2025.3644358

Yike Qiao;Xiaodong He;An Zhuo;Zhiyong Sun;Weimin Bao;Zhongkui Li

{"title":"Curvature-Constrained Vector Field for Motion Planning of Nonholonomic Robots","authors":"Yike Qiao;Xiaodong He;An Zhuo;Zhiyong Sun;Weimin Bao;Zhongkui Li","doi":"10.1109/TRO.2025.3644358","DOIUrl":"10.1109/TRO.2025.3644358","url":null,"abstract":"Vector fields are advantageous in handling nonholonomic motion planning, as they provide the robot with reference orientation across the workspace. However, additionally incorporating curvature constraints presents challenges due to the interconnection between the design of the curvature-bounded vector field and the tracking controller under limited actuation. In this article, we present a novel framework to co-develop the vector field and the control law, guiding the nonholonomic robot to the target configuration with curvature-bounded trajectory. First, we formulate the problem by introducing the target positive limit set, which allows the robot to either converge to or pass through the target configuration, depending on its dynamics and the specific tasks. Next, we construct a curvature-constrained vector field (CVF) via blending and embedding elementary flows in the workspace. To track such a CVF, a saturated control law with dynamic gains is proposed, under which the tracking error’s magnitude decreases even when saturation occurs. Under the control law, the kinematically constrained nonholonomic robot is guaranteed to track the reference CVF and converge to the target positive limit set with bounded trajectory curvature. Numerical simulations show that the proposed CVF method outperforms other vector-field-based algorithms. Experiments on Ackermann uncrewed ground vehicles and semiphysical fixed-wing uncrewed aerial vehicles demonstrate that the method can be effectively implemented in real-world scenarios.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"42 ","pages":"455-474"},"PeriodicalIF":10.5,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145759514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Cable-Driven Soft Robotic Hand With an In-Hand RGB-D Camera for Dexterous Grasping and Manipulation 一种带手持RGB-D相机的缆索驱动软机械手，用于灵巧抓取和操作

IF 10.5 1区计算机科学

IEEE Transactions on Robotics Pub Date : 2025-12-09 DOI: 10.1109/TRO.2025.3641751

Zhanfeng Zhou;Runze Zuo;Matthew Du;Shaojia Wang;Sebastian Levy;Yu Sun;Xinyu Liu

{"title":"A Cable-Driven Soft Robotic Hand With an In-Hand RGB-D Camera for Dexterous Grasping and Manipulation","authors":"Zhanfeng Zhou;Runze Zuo;Matthew Du;Shaojia Wang;Sebastian Levy;Yu Sun;Xinyu Liu","doi":"10.1109/TRO.2025.3641751","DOIUrl":"10.1109/TRO.2025.3641751","url":null,"abstract":"The aspiration to replicate the capabilities of the human hand has driven innovations in the design of soft robotic hands. Despite these advancements, many existing designs of soft hands still lack effective in-hand vision and the ability for each finger to achieve active multidegree-of-freedom motion. This article proposes a cable-driven soft robotic hand that can achieve dexterous grasping and manipulation, vision-guided grasping, vision-based slip detection and compensation, as well as visually servoed in-hand manipulation. The hand has five soft fingers, each capable of independent flexion/extension motion and bidirectional ad/abduction motion. A red–green–blue-depth (RGB-D) camera is integrated into the palm of the soft hand to enable in-hand vision capability. Modeling of the soft hand is established to analyze its kinematics, statics, and manipulability. A series of experiments are conducted to demonstrate its dexterous grasping and manipulation capabilities on a variety of objects. Using 3-D point cloud data from the in-palm camera, an effective vision-guided grasping strategy is developed to grasp objects on a table. The in-hand vision also enables slip detection and compensation during grasping to maintain the grasp stability. Furthermore, a hierarchical, visually servoed controller is developed to perform closed-loop in-hand object manipulation. With its high dexterity and visual feedback capabilities, the soft hand will find important applications such as household object manipulation and food picking/sorting, and may also be used as a prosthetic hand or an auxiliary hand for humans.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"42 ","pages":"600-618"},"PeriodicalIF":10.5,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145717954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0