IEEE Transactions on Robotics最新文献

{"title":"Temporal Cascading of Planning and Control for Quadrotor MPC","authors":"Rudolf Reiter;Chao Qin;Leonard Bauersfeld;Davide Scaramuzza","doi":"10.1109/TRO.2026.3726670","DOIUrl":"10.1109/TRO.2026.3726670","url":null,"abstract":"Many aerial tasks involving quadrotors demand both instant reactivity and long-horizon planning for obstacle avoidance, energy efficiency, or trajectory tracking. High-fidelity models enable accurate control but are too slow for long horizons. Low-fidelity planners scale but cannot control the system directly, necessitating cascaded architectures. Prevailing hierarchical approaches plan with a simplified model and use a high-fidelity controller for tracking, yet this decomposition is inherently suboptimal. The controller is limited by the coarse plan, and conventional model predictive control (MPC) alternatives shorten the horizon to stay real-time feasible. We present <sc>Unique</small>, an MPC architecture that replaces this hierarchical stacking with temporal cascading. The planning problem is formulated as the second tail horizon of a single multiphase MPC, rather than solved separately. We align costs across horizons, derive feasibility constraints for the point-mass planning model, and introduce transition constraints that convert high-fidelity states into meaningful low-fidelity states. Parallel point-mass and mixed-integer solvers address nonconvexities while incorporating progressive 3-D obstacle smoothing over the planning horizon. In simulation and real flights, under equal computational budgets, <sc>Unique</small> improves closed-loop tracking by up to 75% compared with standard MPC and hierarchical baselines. Ablations and Pareto analyses confirm performance gains across variations in horizon, constraint approximations, and smoothing schedules.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"42 ","pages":"3626-3645"},"PeriodicalIF":11.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148821228","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}

{"title":"Look as You Leap: Planning Simultaneous Motion and Perception for High-DoF Robots","authors":"Qingxi Meng;Emiliano Flores;Carlos Quintero-Peña;Peizhu Qian;Zachary Kingston;Shannan K. Hamlin;Vaibhav Unhelkar;Lydia E. Kavraki","doi":"10.1109/TRO.2026.3731487","DOIUrl":"10.1109/TRO.2026.3731487","url":null,"abstract":"Most common tasks for robots in dynamic spaces require that the environment is regularly and actively perceived. The perception task considered in this work can represent a broad range of robot perception objectives, including object detection, human activity recognition, and human face detection. For example, a service robot may need to continuously detect and localize a target object during manipulation, while an assistive robot in a healthcare setting may need to maintain reliable perception of a human face or activity for interaction and safety monitoring. These tasks impose perception constraints on the robot motion. However, solving motion and perception tasks simultaneously is challenging, as these objectives often impose conflicting requirements. Furthermore, while robots must react quickly to changes in the environment, directly evaluating the quality of perception (e.g., object detection confidence) is often expensive or infeasible at runtime. This problem is especially important in human-centered environments, such as homes and hospitals, where effective perception is essential for safe and reliable operation. In this work, we address the challenge of solving motion planning problems for high-degree-of-freedom (DoF) robots from a start to a goal configuration with continuous perception constraints under both static and dynamic environments. Our solution is a GPU-parallelized perception-score-guided probabilistic roadmap planner with a neural surrogate model. Unlike existing active perception-aware, visibility-aware, or learning-based planners, our work jointly considers perception tasks and constraints when searching for a solution to the motion planning problem. Our method uses a neural surrogate model to approximate perception scores, incorporates them into a roadmap-based solution, and leverages GPU parallelism to enable efficient online replanning in dynamic settings. We demonstrate that our planner, evaluated on high-<sc>DoF</small> robots, outperforms reinforcement learning-based and trajectory-optimization-based baseline methods in both static and dynamic environments in both simulation and real-robot experiments.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"42 ","pages":"3666-3685"},"PeriodicalIF":11.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148902322","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}

{"title":"A Third-Order Gaussian Process Trajectory Representation Framework With Closed-Form Kinematics for Continuous-Time Motion Estimation","authors":"Thien-Minh Nguyen;Ziyu Cao;Kailai Li;William Talbot;Tongxing Jin;Shenghai Yuan;Timothy D. Barfoot;Lihua Xie","doi":"10.1109/TRO.2026.3701957","DOIUrl":"10.1109/TRO.2026.3701957","url":null,"abstract":"In this article, we propose a third-order, i.e., white-noise-on-jerk, Gaussian process trajectory representation (TR) framework for continuous-time motion estimation (ME) tasks. Our framework features a unified TR that encapsulates the kinematic models of both <inline-formula><tex-math>$mathrm{SO(3)}times mathbb {R}^{3}$</tex-math></inline-formula> and <inline-formula><tex-math>$mathrm{SE(3)}$</tex-math></inline-formula> pose representations. This encapsulation strategy allows users to use the same implementation of measurement-based factors for either choice of pose representation, which facilitates experimentation and comparison to make a better choice for the ME task. In addition, unique to our framework, we derive the kinematic models with the <italic>closed-form temporal derivatives of the local variables of <inline-formula><tex-math>$mathrm{SO(3)}$</tex-math></inline-formula> and <inline-formula><tex-math>$mathrm{SE(3)}$</tex-math></inline-formula></i>, which so far has only been approximated based on Taylor expansion in the literature. Our experiments show that these kinematic models can improve the estimation accuracy in high-speed scenarios. All analytical Jacobians of the interpolated states with respect to the support states of the TR, as well as the motion prior factors, are also provided for accelerated Gauss–Newton optimization. Our experiments demonstrate the efficacy and efficiency of the framework in various ME tasks, such as localization, calibration, and odometry, facilitating fast prototyping for ME researchers. We release the source code for the benefit of the community.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"42 ","pages":"2648-2667"},"PeriodicalIF":11.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148291466","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}

{"title":"Parallel-Elastic Actuation With Reactive Latch Elevates Robotic Hopping Performance: Jump Height and Continuity","authors":"Songnan Bai;Runze Ding;Song Li;Ruihan Jia;Ruobing Wang;Zhiyuan Zhang;Fangzheng Wang;Pakpong Chirarattananon","doi":"10.1109/TRO.2026.3701316","DOIUrl":"10.1109/TRO.2026.3701316","url":null,"abstract":"While many animals exhibit impressive hopping capabilities, machines have struggled to match their performance. Current hopping robots face limitations in power density, energy efficiency, and control stability. Here, we present a parallel-elastic actuation mechanism with a reactive latch that optimizes energy transfer, enabling a legged robot to achieve hopping heights and continuity previously unattainable. This mechanism efficiently stores and releases energy, extending the actuation period over the aerial phase while minimizing stance time. Our robot achieves a maximum hopping height of 3.6 m, surpassing both human and animal records while demonstrating sustained, high-frequency hopping cycles with minimal power requirement. By integrating inertia-based onboard sensorimotor autonomy, we demonstrate stable, controlled hopping in environments without external aid. These results represent a step toward bridging the performance gap between biological and robotic locomotion, with potential to influence the design of future legged systems.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"42 ","pages":"2521-2540"},"PeriodicalIF":11.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148291474","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}

{"title":"STTRL-DVO: Transformer-Based Reinforcement Learning for Robust Dynamic Target Tracking in Cluttered Environments","authors":"Fanghao Wang;Binghong Chen;Youchao Zhang;Xiangyu Guo;Yining Lyu;Chuanjie Liu;Alois Knoll;Di Cui;Huanyu Jiang;Yibin Ying;Mingchuan Zhou","doi":"10.1109/TRO.2026.3701578","DOIUrl":"10.1109/TRO.2026.3701578","url":null,"abstract":"Magnetic microrobots capable of autonomous operation hold significant promise for critical cell and small creature manipulation tasks, including trapping, transportation, sorting, etc. Although conventional microrobot navigation methods have shown notable performance, they often lack adaptability to novel environments. To address these limitations, we propose a learning-based framework for real-world microrobot navigation and dynamic target tracking. Our approach employs spatial-temporal transformer reinforcement learning (STTRL) with a deterministic velocity obstacle (DVO) that processes historical navigation states and virtual light detection and ranging scans to predict optimal actions. The key innovation lies in the model’s ability to extract and utilize contextual information from observation histories, enabling adaptive behavior even in previously unseen environments. Through large-scale model-free reinforcement learning trained in randomized simulation environments, our method achieves remarkable real-world performance with zero-shot transfer capability. Experimental results demonstrate superior navigation agility with an 89.8% success rate in the base environment, representing a 7.4% improvement over state-of-the-art algorithms. Furthermore, the method exhibits robust generalization in diverse unseen environments, validating its adaptability to different environmental characteristics.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"42 ","pages":"2555-2575"},"PeriodicalIF":11.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148291661","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}

{"title":"A Caterpillar-Type Miniature Robot for Adaptive Locomotion and Exploration of Tiny Rigid/Soft Pipes","authors":"Jinyang Gao;Zhengtao Hu;Jingyao Zhang;Yanfei Cao;Kunpeng Zhang;Guozheng Yan;Helei Dong;Qiulin Tan;Li Zhang","doi":"10.1109/TRO.2026.3697140","DOIUrl":"https://doi.org/10.1109/TRO.2026.3697140","url":null,"abstract":"Caterpillar-type robots are widely used for medium- and large-sized pipe inspections. However, existing prototypes smaller than 80 mm lack both an active variable-diameter capability and a contact force sensing function, which are crucial for safe and automatic exploration of unknown rigid/soft pipes (e.g., the colon). This study develops a variable-diameter caterpillar-type miniature robot (VCMR) featuring a small size of Φ34.6 mm × 41 mm, a large variable-diameter range of 34.6–89.6 mm, and an integrated contact force sensing function. The VCMR actively adapts to pipe diameter changes using contact force feedback, demonstrates high load capacity in both vertical and horizontal rigid/soft pipes, and traverses a 150-cm colon phantom with sharp bends at an average velocity of 3.07 ± 0.48 cm/s. It holds promise for exploring tiny variable-diameter rigid/soft pipes and delivering cargoes through such pipes.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"42 ","pages":"2345-2359"},"PeriodicalIF":11.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148433142","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}

{"title":"Corrections to “Optimal Energy Shaping and Force Amplification Framework for Task-Agnostic, Biomimetic Ankle Exoskeletons”","authors":"Katharine Walters;Gray C. Thomas;Robert D. Gregg","doi":"10.1109/TRO.2026.3733800","DOIUrl":"https://doi.org/10.1109/TRO.2026.3733800","url":null,"abstract":"This note addresses unstated assumptions and errors in the proof of Theorem 1 in [1]. The correctness of Theorem 1 relies on two additional assumptions that were not stated in the original manuscript: that the human gravity compensation in the impedance model (10) compensates the shaped potential rather than the gravitational potential alone, and that the controller's velocity-dependent shaping vanishes at the equilibrium configuration, which holds for the configurations used in the experimental stability evaluation. Independent of these assumptions, the proof in the Appendix requires correction in three places: a scalar sufficient condition, a derivation step that relies on the inverse of a rank-deficient matrix, and the forward-invariance argument. Below, we state the assumptions enabling the original Theorem 1, give a generalized statement of Theorem 1 that removes the second assumption, and provide a corrected proof for the general case. No experimental results, figures, or conclusions of [1] are affected.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"42 ","pages":"3861-3863"},"PeriodicalIF":11.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11738880","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149188502","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}

{"title":"Scalable Unseen Objects 6-DoF Absolute Pose Estimation With Robotic Integration","authors":"Jian Liu;Wei Sun;Kai Zeng;Jin Zheng;Hui Yang;Hossein Rahmani;Ajmal Mian;Lin Wang","doi":"10.1109/TRO.2026.3686251","DOIUrl":"10.1109/TRO.2026.3686251","url":null,"abstract":"Pose estimation-guided unseen object 6-degree of freedom (6-DoF) robotic manipulation is a key task in robotics. However, the scalability of current pose estimation methods to unseen objects remains a fundamental challenge, as they generally rely on computer-aided design (CAD) models or dense reference views of unseen objects, which are difficult to acquire, ultimately limit their scalability. In this article, we introduce a novel task setup, referred to as SinRef-6D, which addresses 6-DoF absolute pose estimation for unseen objects using only a single pose-labeled reference RGB-D image captured during robotic manipulation. This setup is more scalable yet technically nontrivial due to large pose discrepancies and the limited geometric and spatial information contained in a single view. To address these issues, our key idea is to iteratively establish pointwise alignment in a common coordinate system with state space models (SSMs) as backbones. Specifically, to handle large pose discrepancies, we introduce an iterative object-space pointwise alignment strategy. Then, Point and RGB SSMs are proposed to capture long-range spatial dependencies from a single view, offering superior spatial modeling capability with linear complexity. Once pretrained on synthetic data, SinRef-6D can estimate the 6-DoF absolute pose of an unseen object using only a single reference view. With the estimated pose, we further develop a hardware–software robotic system and integrate the proposed SinRef-6D into it in real-world settings. Extensive experiments on six benchmarks and in diverse real-world scenarios demonstrate that our SinRef-6D offers superior scalability. Additional robotic grasping experiments further validate the effectiveness of the developed robotic system.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"42 ","pages":"1884-1901"},"PeriodicalIF":10.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147731749","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}

{"title":"Acceleration-Free Analytical Regressor Filtering for Robot Online Identification and Control","authors":"Tian Shi;Weibing Li;Yongping Pan","doi":"10.1109/TRO.2026.3677083","DOIUrl":"10.1109/TRO.2026.3677083","url":null,"abstract":"Avoiding the usage of joint accelerations during robot online identification and control is significant in improving modeling and tracking accuracy. Regressor filtering is feasible to achieve acceleration-free online identification, where the robot dynamics is linearly parameterized and filtered to obtain an acceleration-free filtered regressor. But existing calculation methods of the filtered regressor are either applicable only to robots with low degrees of freedom (DoFs) or restricted by robot modeling techniques. We propose an acceleration-free analytical regressor filtering (AF-ARF) method to obtain the filtered regressor without restrictions on the number of DoFs or robot modeling techniques, where joint accelerations are eliminated by integration by parts, and the filtered regressor is derived by using the skew-symmetric property of the inertia matrix and some matrix operations. An acceleration-free composite learning robot control strategy based on AF-ARF is developed for exact online identification and control, where closed-loop exponential stability with parameter convergence is established under a weakened condition of interval excitation. Simulative and experimental comparisons based on a seven-DoF industrial robot have validated the superiority of our method over state-of-the-art methods in online identification, model prediction, and tracking control under reduced computing burden.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"42 ","pages":"1856-1871"},"PeriodicalIF":10.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147506844","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}

{"title":"Model-Free Co-Optimization of Manufacturable Sensor Layouts and Deformation Proprioception","authors":"Yingjun Tian;Guoxin Fang;Aoran Lyu;Xilong Wang;Zikang Shi;Yuhu Guo;Weiming Wang;Charlie C. L. Wang","doi":"10.1109/TRO.2026.3677076","DOIUrl":"10.1109/TRO.2026.3677076","url":null,"abstract":"Flexible sensors are increasingly employed in soft robotics and wearable devices to provide proprioception of freeform deformations. Although supervised learning can train shape predictors from sensor signals, prediction accuracy strongly depends on sensor layout, which is typically determined heuristically or through trial-and-error. This work introduces a model free, data-driven computational pipeline that jointly optimizes the number, length, and placement of flexible length-measurement sensors together with the parameters of a shape prediction network for large freeform deformations. Unlike model-based approaches, the proposed method relies solely on datasets of deformed shapes, without requiring physical simulation models, and is therefore broadly applicable to diverse robotic sensing tasks. The pipeline incorporates differentiable loss functions that account for both prediction accuracy and manufacturability constraints. By co-optimizing sensor layouts and network parameters, the method significantly improves deformation prediction accuracy over unoptimized layouts while ensuring practical feasibility. The effectiveness and generality of the approach are validated through numerical and physical experiments on multiple soft robotic and wearable systems.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"42 ","pages":"1662-1679"},"PeriodicalIF":10.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147506842","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}