Robotic Grasping

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Overview

The field studies how a robot selects contact points and gripper poses, then controls its hand to acquire and manipulate objects safely. Common approaches include geometric grasp sampling from RGB-D images or point clouds, learned grasp policies and diffusion or flow-based generators, and language- or affordance-guided planning for task-specific contacts. Tactile and force feedback, compliant gripper designs, and simulation-to-real training help systems adjust to slip, object properties, and changing contact during execution. A central challenge is making grasps physically reliable and efficient under clutter, partial observations, unknown friction, and different robot embodiments, while benchmarks increasingly test real-world execution as well as predicted grasp quality.

Latest papers

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316 papers35 papers in the last four weeks, down 34% on the four weeks before. 0.2% of all new papers.

Apr 9, 2026

BLaDA: Bridging Language to Functional Dexterous Actions within 3DGS Fields

In unstructured environments, functional dexterous grasping calls for the tight integration of semantic understanding, precise 3D functional localization, and physically interpretable execution. Modular hierarchical methods are more controllable and interpretable than end-to-end VLA approaches, but existing ones still rely on predefined affordance labels and lack the tight semantic--pose coupling needed for functional dexterous manipulation. To address this, we propose BLaDA (Bridging Language to Dexterous Actions in 3DGS fields), an interpretable zero-shot framework that grounds open-vocabulary instructions as perceptual and control constraints for functional dexterous manipulation. BLaDA establishes an interpretable reasoning chain by first parsing natural language into a structured sextuple of manipulation constraints via a Knowledge-guided Language Parsing (KLP) module. To achieve pose-consistent spatial reasoning, we introduce the Triangular Functional Point Localization (TriLocation) module, which utilizes 3D Gaussian Splatting as a continuous scene representation and identifies functional regions under triangular geometric constraints. Finally, the 3D Keypoint Grasp Matrix Transformation Execution (KGT3D+) module decodes these semantic-geometric constraints into physically plausible wrist poses and finger-level commands. Extensive experiments on complex benchmarks demonstrate that BLaDA significantly outperforms existing methods in both affordance grounding precision and the success rate of functional manipulation across diverse categories and tasks. Code will be publicly available at https://github.com/PopeyePxx/BLaDA.

Fan Yang, Wenrui Chen, Guorun Yan +7

School of Artificial Intelligence and Robotics, Hunan University, Changsha 410012, China. · National Engineering Research Center of Robot Visual Perception and Control Technology, Hunan University, Changsha 410082, China. · Key Laboratory of Advanced Manufacturing Technology of the Ministry of Education, Guizhou University, Guiyang 550025, China.

Robotic ManipulationRobotic GraspingLanguage-Conditioned Robot ManipulationZero-Shot Robotic Manipulation3D Visual Grounding
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Apr 8, 2026

Grasp as You Dream: Imitating Functional Grasping from Generated Human Demonstrations

Building generalist robots capable of performing functional grasping in everyday, open-world environments remains a significant challenge due to the vast diversity of objects and tasks. Existing methods are either constrained to narrow object/task sets or rely on prohibitively large-scale data collection to capture real-world variability. In this work, we present an alternative approach, GraspDreamer, a method that leverages human demonstrations synthesized by visual generative models (VGMs) (e.g., video generation models) to enable zero-shot functional grasping without labor-intensive data collection. The key idea is that VGMs pre-trained on internet-scale human data implicitly encode generalized priors about how humans interact with the physical world, which can be combined with embodiment-specific action optimization to enable functional grasping with minimal effort. Extensive experiments on the public benchmarks with different robot hands demonstrate the superior data efficiency and generalization performance of GraspDreamer compared to previous methods. Real-world evaluations further validate the effectiveness on real robots. Additionally, we showcase that GraspDreamer can (1) be naturally extended to downstream manipulation tasks, and (2) can generate data to support visuomotor policy learning.

Chao Tang, Jiacheng Xu, Haofei Lu +4

Department of Robotics, Perception and Learning, KTH Royal Institute of Technology, Stockholm, Sweden. · Shenzhen Key Laboratory of Robotics and Computer Vision, Southern University of Science and Technology, Shenzhen, China.

Robotic GraspingZero-Shot Robotic ManipulationRobot Imitation LearningData-Efficient Robot LearningSynthetic Data Generation for Robotics
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Apr 8, 2026

A Physical Agentic Loop for Language-Guided Grasping with Execution-State Monitoring

Robotic manipulation systems that follow language instructions often execute grasp primitives in a largely single-shot manner: a model proposes an action, the robot executes it, and failures such as empty grasps, slips, stalls, timeouts, or semantically wrong grasps are not surfaced to the decision layer in a structured way. Inspired by agentic loops in digital tool-using agents, we reformulate language-guided grasping as a bounded embodied agent operating over grounded execution states, where physical actions expose an explicit tool-state stream. We introduce a physical agentic loop that wraps an unmodified learned manipulation primitive (grasp-and-lift) with (i) an event-based interface and (ii) an execution monitoring layer, Watchdog, which converts noisy gripper telemetry into discrete outcome labels using contact-aware fusion and temporal stabilization. These outcome events, optionally combined with post-grasp semantic verification, are consumed by a deterministic bounded policy that finalizes, retries, or escalates to the user for clarification, guaranteeing finite termination. We validate the resulting loop on a mobile manipulator with an eye-in-hand D405 camera, keeping the underlying grasp model unchanged and evaluating representative scenarios involving visual ambiguity, distractors, and induced execution failures. Results show that explicit execution-state monitoring and bounded recovery enable more robust and interpretable behavior than open-loop execution, while adding minimal architectural overhead. For the source code and demo refer to our project page: https://wenzewwz123.github.io/Agentic-Loop/

Wenze Wang, Mehdi Hosseinzadeh, Feras Dayoub

Australian Institute for Machine Learning (AIML), Adelaide University, Australia.

Robotic ManipulationLanguage-Conditioned Robot ManipulationRobotic Grasping
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Apr 8, 2026

BiDexGrasp: Coordinated Bimanual Dexterous Grasps across Object Geometries and Sizes

Bimanual dexterous grasping is a fundamental and promising area in robotics, yet its progress is constrained by the lack of comprehensive datasets and powerful generation models. In this work, we propose BiDexGrasp, consisting of a large-scale bimanual dexterous grasp dataset and a novel learning-based framework. For dataset construction, we propose a novel bimanual grasp synthesis pipeline to efficiently annotate physically feasible data. This pipeline addresses the challenges of high-dimensional bimanual grasping through a two-stage synthesis strategy of efficient region-based grasp initialization and decoupled force-closure grasp optimization. Powered by this pipeline, we construct a large-scale bimanual dexterous grasp dataset, comprising 6351 diverse objects with sizes ranging from 30 to 80 cm, along with 9.53 million annotated grasp data. Based on this dataset, we further introduce a novel learning-based dexterous grasping generation framework. The framework lies in two key designs: a bimanual coordination module and a geometry-size-adaptive grasp generation strategy to generate coordinated and high-quality grasps on unseen objects. Extensive experiments conducted in both simulation and real world demonstrate the superior performance of our proposed data synthesis pipeline and learned generative framework.

Mu Lin, Yi-Lin Wei, Jiaxuan Chen +7

Sun Yat-sen University · Peking University · Tsinghua University

Robotic GraspingDexterous ManipulationBimanual Robotic ManipulationSynthetic Data Generation for Robotics
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Apr 7, 2026

GraspSense: Physically Grounded Grasp and Grip Planning for a Dexterous Robotic Hand via Language-Guided Perception and Force Maps

Dexterous robotic manipulation requires more than geometrically valid grasps: it demands physically grounded contact strategies that account for the spatially non-uniform mechanical properties of the object. However, existing grasp planners typically treat the surface as structurally homogeneous, even though contact in a weak region can damage the object despite a geometrically perfect grasp. We present a pipeline for grasp selection and force regulation in a five-fingered robotic hand, based on a map of locally admissible contact loads. From an operator command, the system identifies the target object, reconstructs its 3D geometry using SAM3D, and imports the model into Isaac Sim. A physics-informed geometric analysis then computes a force map that encodes the maximum lateral contact force admissible at each surface location without deformation. Grasp candidates are filtered by geometric validity and task-goal consistency. When multiple candidates are comparable under classical metrics, they are re-ranked using a force-map-aware criterion that favors grasps with contacts in mechanically admissible regions. An impedance controller scales the stiffness of each finger according to the locally admissible force at the contact point, enabling safe and reliable grasp execution. Validation on paper, plastic, and glass cups shows that the proposed approach consistently selects structurally stronger contact regions and keeps grip forces within safe bounds. In this way, the work reframes dexterous manipulation from a purely geometric problem into a physically grounded joint planning problem of grasp selection and grip execution for future humanoid systems.

Elizaveta Semenyakina, Ivan Snegirev, Mariya Lezina +3

Intelligent Space Robotics Laboratory, Skolkovo Institute of Science and Technology

Robotic GraspingDexterous ManipulationForce-Controlled Robotic Manipulation
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Apr 6, 2026

Pickalo: Leveraging 6D Pose Estimation for Low-Cost Industrial Bin Picking

Bin picking in real industrial environments remains challenging due to severe clutter, occlusions, and the high cost of traditional 3D sensing setups. We present Pickalo, a modular 6D pose-based bin-picking pipeline built entirely on low-cost hardware. A wrist-mounted RGB-D camera actively explores the scene from multiple viewpoints, while raw stereo streams are processed with BridgeDepth to obtain refined depth maps suitable for accurate collision reasoning. Object instances are segmented with a Mask-RCNN model trained purely on photorealistic synthetic data and localized using the zero-shot SAM-6D pose estimator. A pose buffer module fuses multi-view observations over time, handling object symmetries and significantly reducing pose noise. Offline, we generate and curate large sets of antipodal grasp candidates per object; online, a utility-based ranking and fast collision checking are queried for the grasp planning. Deployed on a UR5e with a parallel-jaw gripper and an Intel RealSense D435i, Pickalo achieves up to 600 mean picks per hour with 96-99% grasp success and robust performance over 30-minute runs on densely filled euroboxes. Ablation studies demonstrate the benefits of enhanced depth estimation and of the pose buffer for long-term stability and throughput in realistic industrial conditions. Videos are available at https://mesh-iit.github.io/project-jl2-camozzi/

Alessandro Tarsi, Matteo Mastrogiuseppe, Saverio Taliani +2

MESH Facility (formerly iCub Tech), Istituto Italiano di Tecnologia, Genova 16163, Italy · Institut des Systèmes Intelligents et de Robotique (ISIR), Paris 75005, France · Generative Bionics, Genova 16152, Italy +1

Robotic GraspingObject Pose EstimationRobotic ManipulationIndustrial Robotics
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Apr 5, 2026

Learning Dexterous Grasping from Sparse Taxonomy Guidance

Dexterous manipulation requires planning a grasp configuration suited to the object and task, which is then executed through coordinated multi-finger control. However, specifying grasp plans with dense pose or contact targets for every object and task is impractical. Meanwhile, end-to-end reinforcement learning from task rewards alone lacks controllability, making it difficult for users to intervene when failures occur. To this end, we present GRIT, a two-stage framework that learns dexterous control from sparse taxonomy guidance. GRIT first predicts a taxonomy-based grasp specification from the scene and task context. Conditioned on this sparse command, a policy generates continuous finger motions that accomplish the task while preserving the intended grasp structure. Our result shows that certain grasp taxonomies are more effective for specific object geometries. By leveraging this relationship, GRIT improves generalization to novel objects over baselines and achieves an overall success rate of 87.9%. Moreover, real-world experiments demonstrate controllability, enabling grasp strategies to be adjusted through high-level taxonomy selection based on object geometry and task intent.

Juhan Park, Taerim Yoon, Seungmin Kim +10

Department of Artificial Intelligence, Korea University, Seoul, Korea · Korea University of Technology and Education, Cheonan, Korea · Naver AI Lab, Korea +4

RL for RoboticsRobotic GraspingDexterous Manipulation
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Mar 31, 2026

SuperGrasp: Single-View Object Grasping via Superquadric Similarity Matching, Evaluation, and Refinement

Robotic grasping from single-view observations remains a critical challenge in manipulation. However, existing methods still struggle to generate reliable grasp candidates and stably evaluate grasp feasibility under incomplete geometric information. To address these limitations, we present SuperGrasp, a new two-stage framework for single-view parallel-jaw grasping. In the first stage, we introduce a Similarity Matching Module that efficiently retrieves valid and diverse grasp candidates by matching the input single-view point cloud with a precomputed primitive dataset based on superquadric coefficients. In the second stage, we propose E-RNet, an end-to-end network that expands the grasp-aware region and takes the initial grasp closure region as a local anchor region, capturing the contextual relationship between the local region and its surrounding spatial neighborhood, thereby enabling more accurate and reliable grasp evaluation and introducing small-range local refinement to improve grasp adaptability. To enhance generalization, we construct a primitive dataset containing 1.2k standard geometric primitives for similarity matching and collect a point cloud dataset of 100k samples from 124 objects, annotated with stable grasp labels for network training. Extensive experiments in both simulation and real-world environments demonstrate that our method achieves stable grasping performance and good generalization across novel objects and clutter scenes.

Lijingze Xiao, Jinhong Du, Supeng Diao +2

Superquadric RepresentationRobotic GraspingGeneralization in Robotic Manipulation
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Mar 29, 2026

Probe-to-Grasp Manipulation Using Self-Sensing Pneumatic Variable-Stiffness Joints

Grasping deformable objects with varying stiffness remains a significant challenge in robotics. Estimating the local stiffness of a target object is important for determining an optimal grasp pose that enables stable pickup without damaging the object. This paper presents a probe-to-grasp manipulation framework for estimating the relative stiffness of objects using a passive soft-rigid two-finger hybrid gripper equipped with self-sensing pneumatic variable-stiffness joints. Each finger of the gripper consists of two rigid links connected by a soft pneumatic ring placed at the joint, enabling both compliant interaction and controllable joint stiffness via internal pressurization. By measuring the pressure inside the pneumatic ring, we can estimate the interaction force during contact. Building on this, we propose a practical probing strategy to infer relative object stiffness by correlating the estimated normal force with known gripper closing displacement. We validate the self-sensing model through stiffness characterization experiments across bending angles and pressure ranges, and demonstrate stiffness-aware probing-and-grasping in real-life applications: selecting grasp locations on fruits with spatially varying stiffness. The proposed system offers a minimal, low-cost sensing approach for stiffness-aware soft manipulation while retaining probing and grasping capability.

Ngoc Duy Tran, Yeman Fan, Feng Dai +5

Robotic GraspingDeformable Object ManipulationSoft RoboticsTactile Sensing for Robotic Manipulation
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Mar 27, 2026

UMI-Underwater: Learning Underwater Manipulation without Underwater Teleoperation

Underwater robotic grasping is difficult due to degraded, highly variable imagery and the expense of collecting diverse underwater demonstrations. We introduce a system that (i) autonomously collects successful underwater grasp demonstrations via a self-supervised data collection pipeline and (ii) transfers grasp knowledge from on-land human demonstrations through a depth-based affordance representation that bridges the on-land-to-underwater domain gap and is robust to lighting and color shift. An affordance model trained on on-land handheld demonstrations is deployed underwater zero-shot via geometric alignment, and an affordance-conditioned diffusion policy is then trained on underwater demonstrations to generate control actions. In pool experiments, our approach improves grasping performance and robustness to background shifts, and enables generalization to objects seen only in on-land data, outperforming RGB-only baselines. Code, videos, and additional results are available at https://umi-under-water.github.io.

Hao Li, Long Yin Chung, Jack Goler +5

All authors are with Stanford University.

Affordance-Based Robotic ManipulationRobotic GraspingRobotic ManipulationGeneralization in Robotic ManipulationZero-Shot Robotic Manipulation
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Mar 27, 2026

Generalizable task-oriented object grasping through LLM-guided ontology and similarity-based planning

Task-oriented grasping (TOG) is more challenging than simple object grasping because it requires precise identification of object parts and careful selection of grasping areas to ensure effective and robust manipulation. While recent approaches have trained large-scale vision-language models to integrate part-level object segmentation with task-aware grasp planning, their instability in part recognition and grasp inference limits their ability to generalize across diverse objects and tasks. To address this issue, we introduce a novel, geometry-centric strategy for more generalizable TOG that does not rely on semantic features from visual recognition, effectively overcoming the viewpoint sensitivity of model-based approaches. Our main proposals include: 1) an object-part-task ontology for functional part selection based on intuitive human commands, constructed using a Large Language Model (LLM); 2) a sampling-based geometric analysis method for identifying the selected object part from observed point clouds, incorporating multiple point distribution and distance metrics; and 3) a similarity matching framework for imitative grasp planning, utilizing similar known objects with pre-existing segmentation and grasping knowledge as references to guide the planning for unknown targets. We validate the high accuracy of our approach in functional part selection, identification, and grasp generation through real-world experiments. Additionally, we demonstrate the method's generalization capabilities to novel-category objects by extending existing ontological knowledge, showcasing its adaptability to a broad range of objects and tasks.

Hao Chen, Takuya Kiyokawa, Weiwei Wan +1

Graduate School of Engineering Science, The University of Osaka, 1-3, Machikaneyamacho, Toyonaka, Osaka, 560-8531, Japan

Robotic GraspingGeneralization in Robotic Manipulation
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Mar 25, 2026

A Sensorless, Inherently Compliant Anthropomorphic Musculoskeletal Hand Driven by Electrohydraulic Actuators

Robotic manipulation in unstructured environments requires end-effectors that combine kinematic dexterity with physical compliance. While traditional rigid hands rely on complex external sensors for safe interaction, electrohydraulic actuators offer a promising alternative by combining muscle-like compliance with self-sensing capability. This paper presents the design, control, and evaluation of a musculoskeletal robotic hand architecture powered entirely by remote Peano-HASEL actuators, optimized for safe manipulation. By relocating the actuators to the forearm, we isolate the grasping interface from electrical hazards while maintaining a slim, human-like profile. To address the inherently limited linear contraction of these soft actuators, we integrate a 1:2 pulley routing mechanism that mechanically amplifies tendon displacement. The resulting system prioritizes compliant interaction over high payload capacity, leveraging the intrinsic force-limiting characteristics of the actuators to provide inherent safety. This physical safety is augmented by the self-sensing nature of the HASEL actuators: by monitoring the operating current alone, we achieve real-time grasp detection and closed-loop contact-aware control without external force transducers or encoders. Experimental results demonstrate the system's dexterity and safety through the execution of grasp types from standard taxonomies and the non-destructive grasping of highly fragile objects such as a paper balloon. These findings represent a step toward simplified, inherently compliant soft robotic manipulation.

Misato Sonoda, Ronan Hinchet, Amirhossein Kazemipour +2

Soft Robotics Lab, ETH Zurich, Switzerland · The University of Tokyo, Japan

Robotic Hand DesignRobotic ManipulationSoft RoboticsRobotic Grasping
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Mar 24, 2026

PHANTOM Hand

Tendon-driven underactuated hands excel in adaptive grasping but often suffer from kinematic unpredictability and highly non-linear force transmission. This ambiguity limits their ability to perform precise free-motion shaping and deliver reliable payloads for complex manipulation tasks. To address this, we introduce the PHANTOM Hand (Hybrid Precision-Augmented Compliance): a modular, 1:1 human-scale system featuring 6 actuators and 15 degrees of freedom (DoFs). We propose a unified framework that bridges the gap between precise analytic shaping and robust compliant grasping. By deriving a sparse mapping from physical geometry and integrating a mechanics-based compensation model, we effectively suppress kinematic drift caused by spring counter-tension and tendon elasticity. This approach achieves sub-degree kinematic reproducibility for free-motion planning while retaining the inherent mechanical compliance required for stable physical interaction. Experimental validation confirms the system's capabilities through (1) kinematic analysis verifying sub-degree global accuracy across the workspace; (2) static expressibility tests demonstrating complex hand gestures; (3) diverse grasping experiments covering power, precision, and tool-use categories; and (4) quantitative fingertip force characterization. The results demonstrate that the PHANTOM hand successfully combines analytic kinematic precision with continuous, predictable force output, significantly expanding the payload and dexterity of underactuated hands. To drive the development of the underactuated manipulation ecosystem, all hardware designs and control scripts are fully open-sourced for community engagement.

Teng Yan, Jiongxu Chen, Qixiang Hua +4

The Hong Kong University of Science and Technology (Guangzhou)

Robotic Hand DesignRobotic ManipulationRobotic GraspingTendon-Driven Actuation
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Mar 22, 2026

GAPG: Geometry Aware Push-Grasping Synergy for Goal-Oriented Manipulation in Clutter

Grasping target objects is a fundamental skill for robotic manipulation, but in cluttered environments with stacked or occluded objects, a single-step grasp is often insufficient. To address this, previous work has introduced pushing as an auxiliary action to create graspable space. However, these methods often struggle with both stability and efficiency because they neglect the scene's geometric information, which is essential for evaluating grasp robustness and ensuring that pushing actions are safe and effective. To this end, we propose a geometry-aware push-grasp synergy framework that leverages point cloud data to integrate grasp and push evaluation. Specifically, the grasp evaluation module analyzes the geometric relationship between the gripper's point cloud and the points enclosed within its closing region to determine grasp feasibility and stability. Guided by this, the push evaluation module predicts how pushing actions influence future graspable space, enabling the robot to select actions that reliably transform non-graspable states into graspable ones. By jointly reasoning about geometry in both grasping and pushing, our framework achieves safer, more efficient, and more reliable manipulation in cluttered settings. Our method is extensively tested in simulation and real-world environments in various scenarios. Experimental results demonstrate that our model generalizes well to real-world scenes and unseen objects.

Lijingze Xiao, Jinhong Du, Yang Cong +2

College of Automation Science and Engineering, South China University of Technology, Guangzhou, China.

Robotic GraspingRobotic Manipulation
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Mar 22, 2026

Affordance-Guided Enveloping Grasp Demonstration Toward Non-destructive Disassembly of Pinch-Infeasible Mating Parts

Robotic disassembly of complex mating components often renders pinch grasping infeasible, necessitating multi-fingered enveloping grasps. However, visual occlusions and geometric constraints complicate teaching appropriate grasp motions when relying solely on 2D camera feeds. To address this, we propose an affordance-guided teleoperation method that pre-generates enveloping grasp candidates via physics simulation. These Affordance Templates (ATs) are visualized with a color gradient reflecting grasp quality to augment operator perception. Simulations demonstrate the method's generality across various components. Real-robot experiments validate that AT-based visual augmentation enables operators to effectively select and teach enveloping grasp strategies for real-world disassembly, even under severe visual and geometric constraints.

Masaki Tsutsumi, Takuya Kiyokawa, Gen Sako +1

Department of Systems Innovation, Graduate School of Engineering Science, The University of Osaka, 1-3 Machikaneyama, Toyonaka, Osaka, Japan. · Industrial Cyber-physical Systems Research Center, The National Institute of Advanced Industrial Science and Technology (AIST), 2-3-26 Aomi, Koto-ku, Tokyo, Japan.

Robotic ManipulationRobotic GraspingRobot Teleoperation
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Mar 19, 2026

TiBCLaG: Hybrid design approach for a trigger-induced bistable compliant laparoscopic grasper

Industrial laparoscopic graspers rely on multi-link rigid mechanisms manufactured to tight tolerances, resulting in high manufacturing and assembly costs. This work presents the design and proof-of-concept validation of a monolithic, fully compliant, bistable, laparoscopic grasper that eliminates the need for multiple rigid links, thereby reducing part count. The device integrates a compliant trigger and a compliant gripper end-effector, coupled via a control push-rod, to achieve stable grasping without continuous user input. A hybrid approach combining an analytical model, iterative prototyping, and finite element analyses is employed to design the laparoscopic grasper. The trigger mechanism is synthesized using a Two-Element Beam Constraint Model as a design framework to control the deformation and stiffness of I-beam-like elements. This technique enables elastic energy storage while preventing snap-through instability. The end-effector is designed as a compliant gripper to achieve adaptive grasping through elastic deformation. The laparoscopic design presented here is fabricated using fused deposition 3D printing. A comparative study of the jaws' opening and closing performance is presented using nonlinear finite element analysis and experimentation. The fabricated prototype demonstrates reliable bistable actuation, confirming the feasibility of such compliant laparoscopic grasper architectures.

Joel J Nellikkunnel, Prabhat Kumar

Department of Mechanical and Aerospace Engineering, Indian Institute of Technology Hyderabad, Telangana 502285, India

Mechanism DesignRobotic GraspingSurgical Robotics
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Mar 17, 2026

DexGrasp-Zero: A Morphology-Aligned Policy for Zero-Shot Cross-Embodiment Dexterous Grasping

To meet the demands of increasingly diverse dexterous hand hardware, it is crucial to develop a policy that enables zero-shot cross-embodiment grasping without redundant re-learning. Cross-embodiment alignment is challenging due to heterogeneous hand kinematics and physical constraints. Existing approaches typically predict intermediate motion targets and retarget them to each embodiment, which may introduce errors and violate embodiment-specific limits, hindering transfer across diverse hands. To overcome these limitations, we propose DexGrasp-Zero, a policy that learns universal grasping skills from diverse embodiments, enabling zero-shot transfer to unseen hands. We first introduce a morphology-aligned graph representation that maps each hand's kinematic keypoints to anatomically grounded nodes and equips each node with tri-axial orthogonal motion primitives, enabling structural and semantic alignment across different morphologies. Relying on this graph-based representation, we design a Morphology-Aligned Graph Convolutional Network (MAGCN) to encode the graph for policy learning. MAGCN incorporates a Physical Property Injection mechanism that fuses hand-specific physical constraints into the graph features, enabling adaptive compensation for varying link lengths and actuation limits for precise and stable grasping. Our extensive simulation evaluations on the YCB dataset demonstrate that our policy, jointly trained on four heterogeneous hands (Allegro, Shadow, Schunk, Ability), achieves an 85% zero-shot success rate on unseen hardware (LEAP, Inspire), outperforming the state-of-the-art method by 59.5%. Real-world experiments further evaluate our policy on three robot platforms (LEAP, Inspire, Revo2), achieving an 82% average success rate on unseen objects.

Yuliang Wu, Yanhan Lin, WengKit Lao +4

School of Computer Science and Engineering, Sun Yat-sen University, China

Robotic GraspingZero-Shot Robotic ManipulationCross-Embodiment Robot LearningConvolutional Neural Networks
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Mar 17, 2026

Dexterous grasp data augmentation based on grasp synthesis with fingertip workspace cloud and contact-aware sampling

Robotic grasping is a fundamental yet crucial component of robotic applications, as effective grasping often serves as the starting point for various tasks. With the rapid advancement of neural networks, data-driven approaches for robotic grasping have become mainstream. However, efficiently generating grasp datasets for training remains a bottleneck. This is compounded by the diverse structures of robotic hands, making the design of generalizable grasp generation methods even more complex. In this work, we propose a teleoperation-based framework to collect a small set of grasp pose demonstrations, which are augmented using FSG--a Fingertip-contact-aware Sampling-based Grasp generator. Based on the demonstrated grasp poses, we propose AutoWS, which automatically generates structured workspace clouds of robotic fingertips, embedding the hand structure information directly into the clouds to eliminate the need for inverse kinematics calculations. Experiments on grasping the YCB objects show that our method significantly outperforms existing approaches in both speed and valid pose generation rate. Our framework enables real-time grasp generation for hands with arbitrary structures and produces human-like grasps when combined with demonstrations, providing an efficient and robust data augmentation tool for data-driven grasp training.

Liqi Wu, Haoyu Jia, Kento Kawaharazuka +2

The Department of Mechano-Informatics, Graduate School of Information Science and Technology, The University of Tokyo, Bunkyo-ku, Tokyo, Japan

Robotic GraspingSynthetic Data Generation for RoboticsRobotic Data Collection
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Mar 17, 2026

A Pin-Array Structured Climbing Robot for Stable Locomotion on Steep Rocky Terrain

Climbing robots face significant challenges when navigating unstructured environments, where reliable attachment to irregular surfaces is critical. We present a novel mobile climbing robot equipped with compliant pin-array structured grippers that passively conform to surface irregularities, ensuring stable ground gripping without the need for complicated sensing or control. Each pin features a vertically split design, combining an elastic element with a metal spine to enable mechanical interlocking with microscale surface features. Statistical modeling and experimental validation indicate that variability in individual pin forces and contact numbers are the primary sources of grasping uncertainty. The robot demonstrated robust and stable locomotion in indoor tests on inclined walls (10-30 degrees) and in outdoor tests on natural rocky terrain. This work highlights that a design emphasizing passive compliance and mechanical redundancy provides a practical and robust solution for real-world climbing robots while minimizing control complexity.

Keita Nagaoka, Kentaro Uno, Kazuya Yoshida

The authors are with the Space Robotics Lab. (SRL) in Department of Aerospace Engineering, Graduate School of Engineering, Tohoku University, Sendai 980-8579, Japan.

Robot LocomotionRobotic GraspingTerrain-Aware Robot Locomotion
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Mar 17, 2026

LIMBERO: A Limbed Climbing Exploration Robot Toward Traveling on Rocky Cliffs

In lunar and planetary exploration, legged robots have attracted significant attention as an alternative to conventional wheeled robots, which struggle to traverse rough and uneven terrain. To enable locomotion over highly irregular and steeply inclined surfaces, limbed climbing robots equipped with grippers on their feet have emerged as a promising solution. In this paper, we present LIMBERO, a 10 kg-class quadrupedal climbing robot that employs spine-type grippers for stable locomotion and climbing on rugged and steep terrain. We first introduce a novel gripper design featuring coupled finger-closing and spine-hooking motions, tightly actuated by a single motor, which achieves exceptional grasping performance (>150 N) despite its lightweight design (525 g). Furthermore, we develop an efficient algorithm to visualize a geometry-based graspability index on continuous rough terrain. Finally, we integrate these components into LIMBERO and demonstrate its ability to ascend steep rocky surfaces under a 1 G gravity condition, a performance not previously achieved yet for limbed climbing robots of this scale.

Kentaro Uno, Masazumi Imai, Kazuki Takada +7

The authors are with the Space Robotics Lab. (SRL) in Department of Aerospace Engineering, Graduate School of Engineering, Tohoku University, Sendai 980-8579, Japan. · The author is with the National Institute of Applied Sciences of Toulouse, 31400 Toulouse, France.

Robot LocomotionQuadruped LocomotionRobotic Grasping
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Mar 17, 2026

MG-Grasp: Metric-Scale Geometric 6-DoF Grasping Framework with Sparse RGB Observations

Single-view RGB-D grasp detection remains a common choice in 6-DoF robotic grasping systems, which typically requires a depth sensor. While RGB-only 6-DoF grasp methods has been studied recently, their inaccurate geometric representation is not directly suitable for physically reliable robotic manipulation, thereby hindering reliable grasp generation. To address these limitations, we propose MG-Grasp, a novel depth-free 6-DoF grasping framework that achieves high-quality object grasping. Leveraging two-view 3D foundation model with camera intrinsic/extrinsic, our method reconstructs metric-scale and multi-view consistent dense point clouds from sparse RGB images and generates stable 6-DoF grasp. Experiments on GraspNet-1Billion dataset and real world demonstrate that MG-Grasp achieves state-of-the-art (SOTA) grasp performance among RGB-based 6-DoF grasping methods.

Kangxu Wang, Siang Chen, Chenxing Jiang +3

The Department of Electronic Engineering, Tsinghua University, Beijing 100084, China. · The Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong, China.

Robotic Grasping
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Mar 17, 2026

EFF-Grasp: Energy-Field Flow Matching for Physics-Aware Dexterous Grasp Generation

Denoising generative models have recently become the dominant paradigm for dexterous grasp generation, owing to their ability to model complex grasp distributions from large-scale data. However, existing diffusion-based methods typically formulate generation as a stochastic differential equation (SDE), which often requires many sequential denoising steps and introduces trajectory instability that can lead to physically infeasible grasps. In this paper, we propose EFF-Grasp, a novel Flow-Matching-based framework for physics-aware dexterous grasp generation. Specifically, we reformulate grasp synthesis as a deterministic ordinary differential equation (ODE) process, which enables efficient and stable generation through smooth probability flows. To further enforce physical feasibility, we introduce a training-free physics-aware energy guidance strategy. Our method defines an energy-guided target distribution using adapted explicit physical energy functions that capture key grasp constraints, and estimates the corresponding guidance term via a local Monte Carlo approximation during inference. In this way, EFF-Grasp dynamically steers the generation trajectory toward physically feasible regions without requiring additional physics-based training or simulation feedback. Extensive experiments on five benchmark datasets show that EFF-Grasp achieves superior performance in grasp quality and physical feasibility, while requiring substantially fewer sampling steps than diffusion-based baselines.

Yukun Zhao, Zichen Zhong, Yongshun Gong +2

Shandong University, Jinan, China

Robotic GraspingPhysics-Informed Generative ModelingFlow Matching
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Mar 16, 2026

DGS-Net: Learning Dexterous Grasps from Single-View Point Clouds via a Multi-Object Scene Dataset

Dexterous grasping in multi-object scene constitutes a fundamental challenge in robotic manipulation. Current mainstream grasping datasets predominantly focus on single-object scenarios and predefined grasp configurations, often neglecting environmental interference and the modeling of dexterous pre-grasp gesture, thereby limiting their generalizability in real-world applications. To address this, we propose DGS-Net, an end-to-end grasp prediction network capable of learning dense grasp configurations from single-view point clouds in multi-object scene. Furthermore, we propose a two-stage grasp data generation strategy that progresses from dense single-object grasp synthesis to dense scene-level grasp generation. Our dataset comprises 307 objects, 240 multi-object scenes, and over 350k validated grasps. By explicitly modeling grasp offsets and pre-grasp configurations, the dataset provides more robust and accurate supervision for dexterous grasp learning. Experimental results show that DGS-Net achieves grasp success rates of 88.63% in simulation and 78.98% on a real robotic platform, while exhibiting lower penetration with a mean penetration depth of 0.375 mm and penetration volume of 559.45 mm^3, outperforming existing methods and demonstrating strong effectiveness and generalization capability. Our dataset is available at https://github.com/4taotao8/DGS-Net.

Tao Geng, Dapeng Yang, Ziwei Liu +6

Robotic GraspingSynthetic Data Generation for Robotics
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Mar 16, 2026

Coupled Particle Filters for Robust Affordance Estimation

Robotic affordance estimation is challenging due to visual, geometric, and semantic ambiguities in sensory input. We propose a method that disambiguates these signals using two coupled recursive estimators for sub-aspects of affordances: graspable and movable regions. Each estimator encodes property-specific regularities to reduce uncertainty, while their coupling enables bidirectional information exchange that focuses attention on regions where both agree, i.e., affordances. Evaluated on a real-world dataset, our method outperforms three recent affordance estimators (Where2Act, Hands-as-Probes, and HRP) by 308%, 245%, and 257% in precision, and remains robust under challenging conditions such as low light or cluttered environments. Furthermore, our method achieves a 70% success rate in our real-world evaluation. These results demonstrate that coupling complementary estimators yields precise, robust, and embodiment-appropriate affordance predictions.

Patrick Lowin, Vito Mengers, Oliver Brock

Robotics and Biology Laboratory, Technische Universität Berlin · Robotics Institute Germany · Science of Intelligence (SCIoI), Cluster of Excellence, Berlin, Germany

Robotic Affordance InferenceRobotic ManipulationRobust EstimationRobotic GraspingParticle Filtering
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Mar 16, 2026

AeroGrab: A Unified Framework for Aerial Grasping in Cluttered Environments

Reliable aerial grasping in cluttered environments remains challenging due to occlusions and collision risks. Existing aerial manipulation pipelines largely rely on centroid-based grasping and lack integration between the grasp pose generation models, active exploration, and language-level task specification, resulting in the absence of a complete end-to-end system. In this work, we present an integrated pipeline for reliable aerial grasping in cluttered environments. Given a scene and a language instruction, the system identifies the target object and actively explores it to gain better views of the object. During exploration, a grasp generation network predicts multiple 6-DoF grasp candidates for each view. Each candidate is evaluated using a collision-aware feasibility framework, and the overall best grasp is selected and executed using standard trajectory generation and control methods. Experiments in cluttered real-world scenarios demonstrate robust and reliable grasp execution, highlighting the effectiveness of combining active perception with feasibility-aware grasp selection for aerial manipulation.

Shivansh Pratap Singh, Naveen Sudheer Nair, Samaksh Ujjawal +4

Robotics Research Center, IIIT Hyderabad, India. · Department of Computer Science, University of Manchester, UK.

Aerial RoboticsRobotic GraspingAerial Manipulation
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Mar 16, 2026

RealVLG-R1: A Large-Scale Real-World Visual-Language Grounding Benchmark for Robotic Perception and Manipulation

Visual-language grounding aims to establish semantic correspondences between natural language and visual entities, enabling models to accurately identify and localize target objects based on textual instructions. Existing VLG approaches focus on coarse-grained, object-level localization, while traditional robotic grasping methods rely predominantly on geometric cues and lack language guidance, which limits their applicability in language-driven manipulation scenarios. To address these limitations, we propose the RealVLG framework, which integrates the RealVLG-11B dataset and the RealVLG-R1 model to unify real-world visual-language grounding and grasping tasks. RealVLG-11B dataset provides multi-granularity annotations including bounding boxes, segmentation masks, grasp poses, contact points, and human-verified fine-grained language descriptions, covering approximately 165,000 images, over 800 object instances, 1.3 million segmentation, detection, and language annotations, and roughly 11 billion grasping examples. Building on this dataset, RealVLG-R1 employs Reinforcement Fine-tuning on pretrained large-scale vision-language models to predict bounding boxes, segmentation masks, grasp poses, and contact points in a unified manner given natural language instructions. Experimental results demonstrate that RealVLG supports zero-shot perception and manipulation in real-world unseen environments, establishing a unified semantic-visual multimodal benchmark that provides a comprehensive data and evaluation platform for language-driven robotic perception and grasping policy learning. All data and code are publicly available at https://github.com/lif314/RealVLG-R1.

Linfei Li, Lin Zhang, Ying Shen

School of Computer Science and Technology, Tongji University, China

Vision-Language GroundingLanguage-Conditioned Robot ManipulationRobotic PerceptionRobotic Grasping
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Mar 16, 2026

GraspALL: Adaptive Structural Compensation from Illumination Variation for Robotic Garment Grasping in Any Low-Light Conditions

Achieving accurate garment grasping under dynamically changing illumination is crucial for all-day operation of service robots.However, the reduced illumination in low-light scenes severely degrades garment structural features, leading to a significant drop in grasping robustness.Existing methods typically enhance RGB features by exploiting the illumination-invariant properties of non-RGB modalities, yet they overlook the varying dependence on non-RGB features under varying lighting conditions, which can introduce misaligned non-RGB cues and thereby weaken the model's adaptability to illumination changes when utilizing multimodal information.To address this problem, we propose GraspALL, an illumination-structure interactive compensation model.The innovation of GraspALL lies in encoding continuous illumination changes into quantitative references to guide adaptive feature fusion between RGB and non-RGB modalities according to varying lighting intensities, thereby generating illumination-consistent grasping representations.Experiments on the self-built garment grasping dataset demonstrate that GraspALL improves grasping accuracy by 32-44% over baselines under diverse illumination conditions.

Haifeng Zhong, Wenshuo Han, Zhouyu Wang +10

School of Artificial Intelligence, Jilin University · Chinese Academy of Sciences · National Cheng-Kung University +3

Robotic GraspingMultimodal Sensor Fusion
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Mar 14, 2026

LDHP: Library-Driven Hierarchical Planning for Non-prehensile Dexterous Manipulation

Non-prehensile manipulation is essential for handling thin, large, or otherwise ungraspable objects in unstructured settings. Prior planning and search-based methods often rely on ad-hoc manual designs or generate physically unrealizable motions by ignoring critical gripper properties, while training-based approaches are data-intensive and struggle to generalize to novel, out-of-distribution tasks. We propose a library-driven hierarchical planner (LDHP) that makes executability a first-class design goal: a top-tier contact-state planner proposes object-pose paths using MoveObject primitives, and a bottom-tier grasp planner synthesizes feasible grasp sequences with AdjustGrasp primitives; feasibility is certified by collision checks and quasi-static mechanics, and contact-sensitive segments are recovered via a bounded dichotomy refinement. This gripper-aware decomposition decouples object motion from grasp realizability, yields a task-agnostic pipeline that transfers across manipulation tasks and geometric variations without re-design, and exposes clean hooks for optional learned priors. Real-robot studies on zero-mobility lifting and slot insertion demonstrate consistent execution and robustness to shape and environment changes.

Tierui He, Jiahui Zuo, Fumin Zhang +1

Shanghai Marine Equipment Research Institute, Shanghai 200031, China. · J. Zuo and F. Zhang are with The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong. · C. Zhao is with Jilin University, Changchun, China.

Hierarchical PlanningContact-Rich ManipulationRobotic GraspingRobot Task PlanningNon-Prehensile ManipulationContact-Rich Robotic Manipulation+1
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Mar 14, 2026

GraspADMM: Improving Dexterous Grasp Synthesis via ADMM Optimization

Synthesizing high-quality dexterous grasps is a fundamental challenge in robot manipulation, requiring adherence to diversity, kinematic feasibility (valid hand-object contact without penetration), and dynamic stability (secure multi-contact forces). The recent framework Dexonomy successfully ensures broad grasp diversity through dense sampling and improves kinematic feasibility via a simulator-based refinement method that excels at resolving exact collisions. However, its reliance on fixed contact points restricts the hand's reachability and prevents the optimization of grasp metrics for dynamic stability. Conversely, purely gradient-based optimizers can maximize dynamic stability but rely on simplified contact approximations that inevitably cause physical penetrations. To bridge this gap, we propose GraspADMM, a novel grasp synthesis framework that preserves sampling-based diversity while improving kinematic feasibility and dynamic stability. By formulating the refinement stage using the Alternating Direction Method of Multipliers (ADMM), we decouple the target contact points on the object from the actual contact locations on the hand. This decomposition allows the pipeline to alternate between updating the target object points to directly maximize dynamic grasp metrics, and adjusting the hand pose to physically reach these targets while strictly respecting collision boundaries. Extensive experiments demonstrate that GraspADMM significantly outperforms state-of-the-art baselines, achieving a nearly 15% absolute improvement in grasp success rate for type-unaware synthesis and roughly a 100% relative improvement in type-aware synthesis. Furthermore, our approach maintains robust, physically plausible grasp generation even under extreme low-friction conditions.

Liangwang Ruan, Jiayi Chen, He Wang +1

School of Computer Science, Peking University. · Galbot. · School of Intelligence Science and Technology, Peking University.

Robotic GraspingDexterous ManipulationContact-Rich Robotic Manipulation
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Mar 12, 2026

CRAFT: A Tendon-Driven Hand with Hybrid Hard-Soft Compliance

We introduce CRAFT hand, a tendon-driven anthropomorphic hand with hybrid hard-soft compliance for contact-rich manipulation. The design is based on a simple idea: contact is not uniform across the hand. Impacts concentrate at joints, while links carry most of the load. CRAFT places soft material at joints and keeps links rigid, and uses rollingcontact joint surfaces to keep flexion on repeatable motion paths. Fifteen motors mounted on the fingers drive the hand through tendons, keeping the form factor compact and the fingers light. In structural tests, CRAFT improves strength and endurance while maintaining comparable repeatability. In teleoperation, CRAFT improves handling of fragile and low-friction items, and the hand covers 33/33 grasps in the Feix taxonomy. The full design costs under $600 and will be released open-source with visionbased teleoperation and simulation integration. Project page: http://craft-hand.github.io/

Leo Lin, Shivansh Patel, Jay Moon +2

University of Illinois Urbana-Champaign · UC Irvine

Robotic Hand DesignRobotic GraspingContact-Rich Robotic ManipulationRobotic ManipulationTendon-Driven Actuation
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