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.

May 20, 2026

Safe and Steerable Geometric Motion Policies for Robotic Dexterous Manipulation

Robotic dexterous manipulation requires continuously reconciling objectives and constraints defined on heterogeneous geometric spaces: a robot controlled on a R7\mathbb{R}^7 configuration manifold may need to track end effector poses on SE(3)\mathrm{SE}(3) while satisfying obstacle avoidance margins in R\mathbb{R}. We present Safe Pullback Bundle Dynamical Systems (SafePBDS), a geometrically consistent framework that computes optimal, certifiably safe configuration manifold accelerations from objectives and safety requirements on arbitrary task manifolds. SafePBDS builds on prior work that combines predefined task manifold dynamical systems to produce autonomous motion. Its first innovation is a pullback control barrier function construction, which converts task manifold safety conditions into linear constraints on configuration manifold accelerations. The second innovation is a task manifold action interface that allows a high-level policy to inject low dimensional residual motions; zero input recovers the autonomous behavior, while safety is preserved under arbitrary inputs. This lets high-level policies efficiently steer exploration while leaving precise motion to the autonomous behavior. We validate SafePBDS in simulation and on a 23-DOF Franka Panda-Allegro Hand platform. On dexterous grasping, SafePBDS achieves a 92.5%92.5\% success rate across 20 household objects and 120 trials. Using the action interface, the method can exclude any one of the four fingers during grasping via a one-dimensional action, achieving 94.4%94.4\% 3-finger grasp success across 3 objects and 36 trials. The efficient planning and safety guarantee of SafePBDS also enables the first model-based, fully actuated palm-down in-hand reorientation, exceeding 360∘360^\circ of yaw rotation in both directions under varying object weight and wrist motion. Demo video and details: https://tml.stanford.edu/safe-pbds

Albert Wu, Riccardo Bonalli, Thomas Lew +1

Computer Science Department, Stanford University, Stanford, CA 94305, USA. · Laboratory of Signals and Systems, University of Paris-Saclay, CNRS, CentraleSup´elec, France. · Toyota Research Institute, Los Altos, CA 94022, USA.

Robotic ControlConstrained Motion PlanningRobotic GraspingDexterous ManipulationIn-Hand ManipulationControl Barrier Functions+4
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May 13, 2026

SECOND-Grasp: Semantic Contact-guided Dexterous Grasping

Achieving reliable robotic manipulation, such as dexterous grasping, requires a synergy between physically stable interactions and semantic task guidance, yet these objectives are often treated as separate, disjoint goals. In this paper, we investigate how to integrate dexterous grasping techniques, i.e., physically stable grasps for object lifting and language-guided grasp generation, to achieve both physical stability and semantic understanding. To this end, we propose SECOND-Grasp (SEmantic CONtact-guided Dexterous Grasping), a unified framework that enables robotic hands to dynamically adjust grasping strategies based on semantic reasoning while ensuring physical feasibility. We begin by obtaining coarse contact proposals through vision-language reasoning to infer where contacts should occur based on object properties, followed by segmentation to localize these regions across views. To further ensure consistency across multiple viewpoints, we introduce Semantic-Geometric Consistency Refinement (SGCR), which refines initial contact predictions by enforcing semantic consistency across views and removing geometrically invalid regions, yielding reliable 3D contact maps. Then, we derive a feasible hand pose for each contact map via inverse kinematics, generating a supervision signal for policy learning. Our approach, trained on DexGraspNet, consistently outperforms baselines in lifting success rate on both seen and unseen categories, achieving 98.2% and 97.7%, respectively, while also improving intent-aware grasping by 12.8% and 26.2%. We further show promising results on additional datasets and robotic hands, including Shadow Hand and Allegro Hand.

Han Yi Shin, Heeju Ko, Jaewon Mun +6

1Korea University · University of Texas at Austin · University of Michigan +1

Robotic GraspingLanguage-Conditioned Robot Manipulation
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May 12, 2026

Introducing Environmental Constraints to Grasping Strategies for Paper-Like Flexible Materials Using a Soft Gripper

Robotic manipulation of flexible objects is widely required in both industrial and service applications. Among such objects, paper-like materials exhibit distinct mechanical characteristics compared to cloth, being more sensitive to compressive stress, where minor variations in physical properties can significantly affect grasping. This study systematically investigates grasping strategies for paper-like materials using a universal soft gripper by exploiting environmental constraints. Based on manipulation primitives employed in existing grasping strategies, we proposed systematic grasping strategies for flexible materials by exploiting environmental constraints and analyzed their mechanical and kinematic models. To investigate the influence of materials and working conditions on grasping, an evaluation system for measuring grasping force and success rate was defined and experimentally evaluated. Finally, we summarized the specific workspaces and characteristics of different strategies that can satisfy various task requirements and lead to potential applications in household service robots for grasping planar flexible objects.

Yi Dong, Yang Li, Jinjun Duan +1

College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, 29 Yudao Street, Nanjing, 211106, Jiangsu, China. · Jiangsu Key Laboratory of Bionic Materials and Equipment, Nanjing University of Aeronautics and Astronautics, 29 Yudao Street, Nanjing, 211106, Jiangsu, China.

Robotic GraspingDeformable Object ManipulationSoft Robotics
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May 11, 2026

HeteroGenManip: Generalizable Manipulation For Heterogeneous Object Interactions

Generalizable manipulation involving cross-type object interactions is a critical yet challenging capability in robotics. To reliably accomplish such tasks, robots must address two fundamental challenges: "where to manipulate" (contact point localization) and "how to manipulate" (subsequent interaction trajectory planning). Existing foundation-model-based approaches often adopt end-to-end learning that obscures the distinction between these stages, exacerbating error accumulation in long-horizon tasks. Furthermore, they typically rely on a single uniform model, which fails to capture the diverse, category-specific features required for heterogeneous objects. To overcome these limitations, we propose HeteroGenManip, a task-conditioned, two-stage framework designed to decouple initial grasp from complex interaction execution. First, Foundation-Correspondence-Guided Grasp module leverages structural priors to align the initial contact state, thereby significantly reducing the pose uncertainty of grasping. Subsequently, Multi-Foundation-Model Diffusion Policy (MFMDP) routes objects to category-specialized foundation models, integrating fine-grained geometric information with highly-variable part features via a dual-stream cross-attention mechanism. Experimental evaluations demonstrate that HeteroGenManip achieves robust intra-category shape and pose generalization. The framework achieves an average 31% performance improvement in simulation tasks with broad type setting, alongside a 36.7% gain across four real-world tasks with different interaction types.

Zhenhao Shen, Zeming Yang, Yue Chen +5

1Peking University · 2Tianjin University

Robotic GraspingRobot Foundation ModelsGeneralization in Robotic ManipulationDiffusion PolicyRobotic ManipulationRobot Policy Generalization
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May 10, 2026

MVB-Grasp: Minimum-Volume-Box Filtering of Diffusion-based Grasps for Frontal Manipulation

State-of-the-art 6-DoF grasp generators excel on tabletop benchmarks with overhead cameras but struggle in frontal grasping scenarios on low-cost manipulators with constrained workspaces, where kinematic limits and approach-direction constraints cause high failure rates. We address this challenge for the Unitree Z1 arm by proposing MVB-Grasp, a novel grasping stack that injects a Minimum Volume Bounding Box (MVBB) geometric prior into diffusion-based grasp generation to dramatically improve success rates in frontal, workspace-constrained settings. Our key scientific contributions are threefold: (i) an MVBB-based geometric filter that exploits oriented bounding-box face normals to reject grasps approaching through the table or misaligned with accessible object faces in O(N) time; (ii) a combined re-scoring function that blends learned discriminator scores with face-alignment geometry α=0.85, specifically calibrated for the Z1's frontal workspace and kinematic constraints; and (iii) a systematic MuJoCo evaluation protocol measuring grasp success across object types, distances, lateral positions, and pitch orientations to validate embodiment-specific performance. We implement MVB-Grasp on a Unitree Z1 arm with an Intel RealSense D405 camera, integrating YOLOv8 object detection, GraspGen for candidate generation, Principal Component Analysis (PCA)-based MVBB fitting, and inverse-kinematics trajectory planning. Experiments across 81 MuJoCo episodes (cylinder, asymmetric box, waterbottle) demonstrate that MVB-Grasp achieves 59.3% success versus 24.7% for vanilla GraspGen, a 2.4x improvement, by filtering geometrically infeasible candidates and prioritizing face-aligned grasps suited to the Z1's frontal approach constraints. Real-world trials confirm that the MVBB prior substantially improves grasp reliability on constrained, low-cost manipulators without requiring model retraining.

Bibek Poudel, Abdul Basit, Muhammad Shafique

eBRAIN Lab, Division of Engineering New York University (NYU) Abu Dhabi, Abu Dhabi, UAE

Robotic Grasping
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May 6, 2026

Contact-Free Grasp Stability Prediction with In-Hand Time-of-Flight Sensors

Current approaches to grasp planning for robotics demonstrate high success rates, but degrade with noisy sensors and other factors. Previous works have proposed tactile-based grasp stability classifiers to detect failures, but these approaches rely on making contact and grasping the object to do so. We propose a contact-free grasp stability predictor using multi-zone time-of-flight sensors mounted in the distal links of a gripper. Our method, as it does not require grasping the object to make a prediction, significantly speeds up the stability classification process, cycling at 15 Hz. We collected over 2,500 real-world grasps across 15 objects to train a classifier. Additionally, we conducted grasp attempts over six additional unseen objects, three for validation and model selection, and three for model testing. Our approach demonstrated strong classification performance, with an accuracy of 85.5% on validation and 86.0% on test objects.

Kyle DuFrene, Cindy Grimm

Collaborative Robotics and Intelligent Systems (CoRIS) Institute, Oregon State University, Corvallis, OR 97331

Robotic GraspingRobotic Perception
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May 5, 2026

Learning Reactive Dexterous Grasping via Hierarchical Task-Space RL Planning and Joint-Space QP Control

In this work, we propose a hybrid hierarchical control framework for reactive dexterous grasping that explicitly decouples high-level spatial intent from low-level joint execution. We introduce a multi-agent reinforcement learning architecture, specialized into distinct arm and hand agents, that acts as a high-level planner by generating desired task-space velocity commands. These commands are then processed by a GPU-parallelized quadratic programming controller, which translates them into feasible joint velocities while strictly enforcing kinematic limits and collision avoidance. This structural isolation not only accelerates training convergence but also strictly enforces hardware safety. Furthermore, the architecture unlocks zero-shot steerability, allowing system operators to dynamically adjust safety margins and avoid dynamic obstacles without retraining the policy. We extensively validate the proposed framework through a rigorous simulation-to-reality pipeline. Real-world hardware experiments on a 7-DoF arm equipped with a 20-DoF anthropomorphic hand demonstrate highly robust zero-shot transferability for dexterous grasping to a diverse set of unseen objects, highlighting the system's ability to reactively recover from unexpected physical disturbances in unstructured environments.

Ho Jae Lee, Yonghyeon Lee, Alexander Alexiev +3

Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA

Robotic ControlRobotic Grasping
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May 4, 2026

Visibility-Aware Mobile Grasping in Dynamic Environments

We consider mobile grasping in unknown and dynamic environments, where a robot must approach and grasp a target object using a camera with a limited field of view. This setting introduces two visibility constraints. Incomplete environmental observations can create collision risks along the whole-body trajectory, while occlusions can hinder object pose refinement during grasping. Previous systems often assume known or static environments and decouple perception from motion, leaving safety unresolved around unobserved obstacles. We present a unified system that enforces both visibility constraints at runtime through three core components. A velocity-aware gaze policy directs perception toward fast-moving regions of the planned swept volume, a hierarchical subgoal policy samples grasp and intermediate configurations that maintain target visibility, and a real-time whole-body planner replans over a receding horizon as the map updates. Across 400 randomized simulation scenarios and real-world trials on a Fetch mobile manipulator, our system achieves success rates of 70.4% in unknown static environments and 57.9% in dynamic environments, outperforming the decoupled navigation- and-manipulation strategy by 16.3 and 9.3 percentage points, respectively, while significantly reducing collisions.

Tianrun Hu, Anxing Xiao, David Hsu +1

School of Computing, National University of Singapore, Singapore. · Smart Systems Institute, National University of Singapore, Singapore. · Shanghai Innovation Institute, Shanghai, China.

Active PerceptionConstrained Motion PlanningRobotic GraspingMotion PlanningDynamic Obstacle Avoidance
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May 4, 2026

ShapeGrasp: Simultaneous Visuo-Haptic Shape Completion and Grasping for Improved Robot Manipulation

Humans grasp unfamiliar objects by combining an initial visual estimate with tactile and proprioceptive feedback during interaction. We present ShapeGrasp, a robotic implementation of this approach. The proposed method is an iterative grasp-and-complete pipeline that couples implicit surface visuo-haptic shape completion (creation of full 3D shape from partial information) with physics-based grasp planning. From a single RGB-D view, ShapeGrasp infers a complete shape (point cloud or triangular mesh), generates candidate grasps via rigid-body simulation, and executes the best feasible grasp. Each grasp attempt yields additional geometric constraints -- tactile surface contacts and space occupied by the gripper body -- which are fused to update the object shape. Failures trigger pose re-estimation and regrasping using the refined shape. We evaluate ShapeGrasp in the real world using two different robots and grippers. To the best of our knowledge, this is the first approach that updates shape representations following a real-world grasp. We achieved superior results over baselines for both grippers (grasp success rate of 84% with a three-finger gripper and 91% with a two-finger gripper), while improving the 3D shape reconstruction quality in all evaluation metrics used.

Lukas Rustler, Matej Hoffmann

Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic.

Robotic Grasping3D ReconstructionRobotic Manipulation3D Shape CompletionTactile Sensing for Robotic Manipulation
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May 4, 2026

Robotic Desk Organization: A Multi-Primitive Approach to Manipulating Heterogeneous Objects via Environmental Constraints

Desktop organization remains challenging for service robots because of heterogeneous objects and diverse manipulation objectives, such as collection and stacking. In this article, a task-oriented framework is presented for organizing planar rigid and deformable objects on desks. A perception pipeline was developed that augments existing datasets with uncommon desktop items and makes geometry-based pose and keypoint estimation possible, along with the detection of environmental constraints, such as table edges. To handle diverse manipulation requirements, environment-assisted primitives are used, including contact-based grasping for small objects, edge-based push-grasping for planar rigid objects, and levering-based grasping for planar deformable objects. These primitives leverage environmental and interobject constraints to improve robustness. A task planner was designed to integrate these primitives into multiobject organization. Sufficient real-world experiments demonstrate the effectiveness and robustness of the proposed framework. This research provides practical manipulation primitives for planar rigid and deformable objects, highlighting the role of environmental and interobject constraints in complex multiobject manipulation tasks. Code and video are available online.

Yi Dong, Yangjun Liu, Jinjun Duan +2

Robotic GraspingDeformable Object ManipulationRobotic ManipulationRobot Task Planning
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May 1, 2026

Ablation Study of Multimodal Perception, Language Grounding, and Control for Human-Robot Interaction in an Object Detection and Grasping Task

This manuscript extends our previous multimodal human-robot interaction system by introducing a controlled ablation study of the three modules that most strongly influence end-to-end performance: the large language model used for action extraction, the perception system used for visual grounding, and the controller used for motion execution. The goal is not to redesign the full pipeline, but to isolate the contribution of each component under a common experimental protocol and then evaluate the best combinations end-to-end. We therefore compare three language models, five perception configurations, and three controllers, followed by a second-stage factorial study over the best candidates. The resulting analysis is intended to clarify which choices primarily affect execution time, which primarily affect success rate, and where the largest engineering gains are likely to come from in future revisions of the system.

Zi Tian, Guanting Shen

Dalian University of Technology, Dalian, China

Multimodal GroundingRobotic GraspingLanguage-Conditioned Robot ManipulationHuman-Robot InteractionRobotic Perception
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May 1, 2026

A Model-based Visual Contact Localization and Force Sensing System for Compliant Robotic Grippers

Grasp force estimation can help prevent robots from damaging delicate objects during manipulation and improve learning-based robotic control. Integrating force sensing into deformable grippers negotiates trade-offs in cost, complexity, mechanical robustness, and performance. With the growing integration of RGB-D wrist cameras into robotic systems for control purposes, camera-based techniques are a promising solution for indirect visual force estimation. Current approaches mostly utilize end-to-end deep learning, which can be brittle when generalizing to new scenarios, while existing model-based approaches are unsuited to grasping and modern grasper geometries. To address these challenges, we developed a model-based visual force sensing approach integrating an iterative contact localization with generalization to unseen objects. The system extracts structural key points from wrist camera RGB-D images of deforming fin-ray-shaped soft grippers, and uses these key points to define parameters of an inverse finite element analysis simulation in Simulation Open Framework Architecture. The iterative contact localization sub-system utilizes a deep learning-based online 3D reconstruction and pose estimation pipeline to dynamically update contact location, and is robust to visual occlusion and unseen objects. Our system demonstrated an average root mean square error of 0.23 N and normalized root mean square deviation of 2.11% during the load phase, and 0.48 N and 4.34% over the entire grasping process when interacting with different objects under various conditions, showcasing its potential for real-time model-based indirect force sensing of soft grippers.

Kaiwen Zuo, Shuyuan Yang, Zonghe Chua

Department of Electrical, Computer, and Systems Engineering, Case Western Reserve University, Cleveland, OH 44106, USA

Robotic GraspingRobotic ManipulationSoft Robotics
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Apr 30, 2026

Function-based Parametric Co-Design Optimization of Dexterous Hands

Despite advances in dexterous hand manipulation, robotic hand design is still largely decoupled from task-driven evaluation and control, limiting systematic optimization. Existing robotic hand co-design approaches are often limited in scope, optimizing a small subset of design parameters. We introduce a comprehensive parametric framework for robotic hand generation that unifies palm structure, finger kinematics, fingertip geometry, and fine-scale surface curvatures within a single design space. Fine geometric features are introduced through parametric surface deformation kernels that directly influence contact interactions. We validate the framework on design optimization in grasp stability tasks in simulation and real-world dynamic scenarios. Our framework produces simulation- and fabrication-ready hand models and will be released as open-source to enable rapid design iteration for dexterous hand co-design optimization frameworks and cross-embodiment policy training and control research.

Mohammad Amin Mirzaee, Harsh Gupta, Wenzhen Yuan

University of Illinois at Urbana-Champaign, Champaign, IL, USA

Robotic Hand DesignRobotic GraspingDexterous ManipulationRobotics
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Apr 29, 2026

Stochastic Entanglement of Deterministic Origami Tentacles For Universal Robotic Gripping

Origami-inspired robotic grippers have shown promising potential for object manipulation tasks due to their compact volume and mechanical flexibility. However, robust capture of objects with random shapes in dynamic working environments often comes at the cost of additional actuation channels and control complexity. Here, we introduce a tendon-driven origami tentacle gripper capable of universal object gripping by exploiting a synergy between local, deterministic deformation programming and global, stochastic entanglements. Each origami tentacle is made by cutting thin Mylar sheets; It features carefully placed holes for routing an actuation tendon, origami creases for controlling the deformation, and a tapered shape. By tailoring these design features, one can prescribe the shrinking, bending, and twisting deformation, eventually creating deterministic coiling with a simple tendon pull. Then, when multiple coiling tentacles are placed in proximity, stochastic entanglement emerges, allowing the tentacles to braid, knot, and grip objects with random shapes. We derived a simulation model by integrating origami mechanics with Cosserat rods to correlate origami design, tendon deformation, and their collective gripping performance. Then, we experimentally tested how these coiling and entangling origami tentacles can grasp objects under gravity and in water. A stow-and-release deployment mechanism was also tested to simulate in-orbit grasping. Overall, the entertaining origami tentacle gripper presents a new strategy for robust object grasping with simple design and actuation.

Alec Boron, Bokun Zheng, Ziyang Zhou +2

Department of Mechanical Engineering, Virginia Tech, VA, USA

Robotic GraspingRoboticsCosserat Rod ModelingSoft RoboticsRobotic Gripper Design
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Apr 29, 2026

2D and 3D Grasp Planners for the GET Asymmetrical Gripper

In this paper, we introduce GET-2D-1.0, a fast grasp planner for the GET asymmetrical gripper that operates from a single-view RGB-D image, using the Ferrari-Canny metric and a novel sampling strategy, and GET-3D-1.0, a mesh-based method using a 3D gripper model and ray-tracing. We evaluate both grasp planners against baselines with physical experiments, which suggest that GET-2D-1.0 can improve over a bounding box baseline by over 40% in lift success, shake survival, and force resistance. Experiments with GET-3D-1.0 suggest slight improvement compared to GET-2D-1.0 on lift success and shake survival, but are more computationally expensive, averaging 17 seconds of planning compared to 683 ms for GET-2D-1.0.

Andrew Goldberg, Ethan Ransing, Anton Kourakin +3

The AUTOLab at UC Berkeley · Massachusetts Institute of Technology

Robotic GraspingRobotic Manipulation
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Apr 28, 2026

Variational Neural Belief Parameterizations for Robust Dexterous Grasping under Multimodal Uncertainty

Contact variability, sensing uncertainty, and external disturbances make grasp execution stochastic. Expected-quality objectives ignore tail outcomes and often select grasps that fail under adverse contact realizations. Risk-sensitive POMDPs address this failure mode, but many use particle-filter beliefs that scale poorly, obstruct gradient-based optimization, and estimate Conditional Value-at-Risk (CVaR) with high-variance approximations. We instead formulate grasp acquisition as variational inference over latent contact parameters and object pose, representing the belief with a differentiable Gaussian mixture. We use Gumbel-Softmax component selection and location-scale reparameterization to express samples as smooth functions of the belief parameters, enabling pathwise gradients through a differentiable CVaR surrogate for direct optimization of tail robustness. In simulation, our variational neural belief improves robust grasp success under contact-parameter uncertainty and exogenous force perturbations while reducing planning time by roughly an order of magnitude relative to particle-filter model-predictive control. On a serial-chain robot arm with a multifingered hand, we validate grasp-and-lift success under object-pose uncertainty against a Gaussian baseline. Both methods succeed on the tested perturbations, but our controller terminates in fewer steps and less wall-clock time while achieving a higher tactile grasp-quality proxy. Our learned belief also calibrates risk more accurately, keeping mean absolute calibration error below 0.14 across tested simulation regimes, compared with 0.58 for a Cross-Entropy Method planner.

Clinton Enwerem, Shreya Kalyanaraman, John S. Baras +1

Department of Electrical & Computer Engineering and Institute for Systems Research, University of Maryland, College Park, MD, USA

Belief-Space PlanningStochastic OptimizationRobotic GraspingRisk-Sensitive PlanningVariational InferenceDecision-Making under Uncertainty
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Apr 28, 2026

HANDFUL: Sequential Grasp-Conditioned Dexterous Manipulation with Resource Awareness

Dexterous robot hands offer rich opportunities for multifunctional manipulation, where a robot must execute multiple skills in sequence while maintaining control over previously grasped objects. Most prior work in dexterous manipulation focuses on single-object, single-skill tasks. In contrast, our insight is that many sequential tasks require resource-aware grasps that conserve fingers for future actions. In this paper, we study sequential grasp-conditioned dexterous manipulation, where a robot first grasps an object and then performs a second, distinct manipulation subtask while preserving the initial grasp. We introduce HANDFUL, a learning framework that models finger usage as a limited resource and encourages exploration of resource-aware grasps through finger-level contact rewards. These grasps are subsequently selected for downstream tasks via curriculum-based policy learning. We further propose HANDFUL-Bench, a simulation benchmark that introduces sequential dexterous manipulation tasks across multiple secondsubtask objectives, including pushing, pulling, and pressing, under a shared grasp-conditioned setup. Extensive simulation results demonstrate that prioritizing resource-aware grasps improves second-subtask success and robustness compared to a baseline that greedily optimizes the initial grasp before attempting the second subtask. We additionally validate our approach on a real dexterous LEAP hand. Together, this work establishes resource-aware grasp planning as a key principle for multifunctional dexterous manipulation. Supplementary material is available on our website: https://handful-dex.github.io.

Ethan Foong, Yunshuang Li, Hao Jiang +2

Department of Computer Science at Northwestern University · Viterbi School of Engineering at the University of Southern California

Robotic GraspingDexterous ManipulationRobot Manipulation Benchmarks
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Apr 27, 2026

An analysis of sensor selection for fruit picking with suction-based grippers

Robotic fruit harvesting often fails to reliably detect whether a fruit has been successfully picked, limiting efficiency and increasing crop damage. This problem is difficult due to compliant fruit and grippers, variable stem attachment, and occlusions in orchard environments. Prior work has explored vision-based perception and multi-sensor learning approaches for pick state estimation. However, minimal sensor sets and phase-dependent sensing strategies for accurate pick and slip detection remain largely unexplored. In this work, we design and evaluate a multimodal sensing suite integrated into a compliant suction-based apple gripper. Our approach is unique because it identifies which sensors are most informative at different phases of the pick, enabling predictive detection of failures before they occur. The contributions of this paper are a phase-dependent evaluation of multimodal sensors and the identification of minimal sensor sets for reliable pick state classification. Experiments in a real apple orchard show that Random Forest and Multilayer Perceptron classifiers detect successful picks and impending failures with over 90% accuracy, and Random Forest predicts pick/slip events within 0.09 s of human-annotated ground truth.

Eva Krueger, Marcus Rosette, Joseph R. Davidson

Collaborative Robotics and Intelligent Systems (CoRIS) Institute, Oregon State University, Corvallis OR 97331, USA

Robotic GraspingTactile SensingAgricultural RoboticsMultimodal Sensor Fusion
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Apr 22, 2026

A Kinematic Framework for Screening Candidate Pinch Configurations in Robotic Hand Design without Object or Contact Models

Evaluating the pinch capability of a robotic hand is important for understanding its functional dexterity. However, many existing grasp evaluation methods rely on object geometry or contact force models, which limits their applicability during the early stages of robotic hand design. This study proposes a kinematic evaluation method for analyzing pinch configurations of robotic hands based on interactions between fingertip workspaces. First, the reachable workspace of each fingertip is computed from the joint configurations of the fingers. Then, feasible pinch configurations are detected by evaluating the relationships between fingertip pairs. Since the proposed method does not require information about object geometry or contact force models, the pinch capability of a robotic hand can be evaluated solely based on its kinematic structure. In addition, analyses are performed on four different kinematic structures of the hand to investigate their impact on the pinch configurations. The proposed evaluation framework can serve as a useful tool for comparing different robotic hand designs and analyzing pinch capability during the design stage.

HyoJae Kang, Joonho Lee, Hyunmok Jung +1

Advanced Robotics Research Center, Korea Institute of Machinery & Materials (KIMM)

Robotic Hand DesignRobotic GraspingRobotics
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Apr 22, 2026

ETac: A Lightweight and Efficient Tactile Simulation Framework for Learning Dexterous Manipulation

Tactile sensors are increasingly integrated into dexterous robotic manipulators to enhance contact perception. However, learning manipulation policies that rely on tactile sensing remains challenging, primarily due to the trade-off between fidelity and computational cost of soft-body simulations. To address this, we present ETac, a tactile simulation framework that models elastomeric soft-body interactions with both high fidelity and efficiency. ETac employs a lightweight data-driven deformation propagation model to capture soft-body contact dynamics, achieving high simulation quality and boosting efficiency that enables large-scale policy training. When serving as the simulation backend, ETac produces surface deformation estimates comparable to FEM and demonstrates applicability for modeling real tactile sensors. Then, we showcase its capability in training a blind grasping policy that leverages large-area tactile feedback to manipulate diverse objects. Running on a single RTX 4090 GPU, ETac supports reinforcement learning across 4,096 parallel environments, achieving a total throughput of 869 FPS. The resulting policy reaches an average success rate of 84.45% across four object types, underscoring ETac's potential to make tactile-based skill learning both efficient and scalable.

Zhe Xu, Feiyu Zhao, Xiyan Huang +1

School of Information Science and Technology at ShanghaiTech University, Shanghai 201210, China

Robotic GraspingRobotics SimulationTactile Sensing for Robotic Manipulation
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Apr 20, 2026

Muscle-inspired magnetic actuators that push, pull, crawl, and grasp

Functional magnetic composites capable of large deformation, load bearing, and multifunctional motion are essential for next-generation adaptive soft robots. Here, we present muscle-inspired magnetic actuators (MMA), additively manufactured from a thermoplastic/permanent magnet polyurethane/Nd2Fe14B (TPU/MQP-S) composite using laser powder bed fusion (LPBF). By tuning the laser-energy scale between 1.0 and 3.0, both mechanical stiffness and magnetic response are precisely controlled: the tensile strength increases from 0.28 to 0.99 MPa while maintaining 30-45% elongation at break. This process enables the creation of 0.5 mm-thick flexural hinges, which reversibly bend and fold under moderate magnetic fields without damage. Two actuator types are reported showing the system versatility. The elongated actuator with self-weight of 1.57 g, magnetized in its contracted state, achieves linear contraction under a 500 mT field, lifting 50 g (32x its own weight) and sustaining performance over at least 50 cycles. Equipped with anisotropic frictional feet, it supports movement of a magnetic crawling robot that achieves up to 100% locomotion success on textured substrates. The expandable actuator exhibits reversible opening and closing under a 300 mT field, reliably grasping and releasing different objects, including soft berries and rigid 3D printed geometries. It can also anchor in a tube while holding suspended 50 g loads. This work demonstrates a LPBF-based strategy to program both stiffness and magnetization within a single material system, enabling remotely driven, reconfigurable, and fatigue-resistant soft actuators. The approach opens new possibilities for force controlled, multifunctional magnetic soft robots for adaptive gripping, locomotion, and minimally invasive manipulation of biomedical tools.

Muhammad Bilal Khan, Florian Hofmann, Kilian Schäfer +2

1Functional Materials, Institute of Materials Science, Technical University of Darmstadt, 64287 Darmstadt, Germany · 2Additive Manufacturing Center, Technical University of Darmstadt, 64289 Darmstadt, Germany

Robotic GraspingAdditive ManufacturingSoft Robotics
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Apr 20, 2026

SpaceDex: Generalizable Dexterous Grasping in Tiered Workspaces

Generalizable grasping with high-degree-of-freedom (DoF) dexterous hands remains challenging in tiered workspaces, where occlusion, narrow clearances, and height-dependent constraints are substantially stronger than in open tabletop scenes. Most existing methods are evaluated in relatively unoccluded settings and typically do not explicitly model the distinct control requirements of arm navigation and hand articulation under spatial constraints. We present SpaceDex, a hierarchical framework for dexterous manipulation in constrained 3D environments. At the high level, a Vision-Language Model (VLM) planner parses user intent, reasons about occlusion and height relations across multiple camera views, and generates target bounding boxes for zero-shot segmentation and mask tracking. This stage provides structured spatial guidance for downstream control instead of relying on single-view target selection. At the low level, we introduce an arm-hand Feature Separation Network that decouples global trajectory control for the arm from geometry-aware grasp mode selection for the hand, reducing feature interference between reaching and grasping objectives. The controller further integrates multi-view perception, fingertip tactile sensing, and a small set of recovery demonstrations to improve robustness to partial observability and off-nominal contacts. In 100 real-world trials involving over 30 unseen objects across four categories, SpaceDex achieves a 63.0% success rate, compared with 39.0% for a strong tabletop baseline. These results indicate that combining hierarchical spatial planning with arm-hand representation decoupling improves dexterous grasping performance in spatially constrained environments.

Wensheng Wang, Chuanjun Guo, Wei Wei +2

Sun Yat-sen University, Guangzhou, China. · Stellarobot Company, Shenzhen, China.

Robotic GraspingDexterous ManipulationGeneralization in Robotic Manipulation
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Apr 19, 2026

A Rapid Deployment Pipeline for Autonomous Humanoid Grasping Based on Foundation Models

Deploying a humanoid robot to manipulate a new object has traditionally required one to two days of effort: data collection, manual annotation, 3D model acquisition, and model training. This paper presents an end-to-end rapid deployment pipeline that integrates three foundation-model components to shorten the onboarding cycle for a new object to approximately 30 minutes: (i) Roboflow-based automatic annotation to assist in training a YOLOv8 object detector; (ii) 3D reconstruction based on Meta SAM 3D, which eliminates the need for a dedicated laser scanner; and (iii) zero-shot 6-DoF pose tracking based on FoundationPose, using the SAM3D-generated mesh directly as the template. The estimated pose drives a Unity-based inverse kinematics planner, whose joint commands are streamed via UDP to a UnitreeG1 humanoid and executed through the Unitree SDK. We demonstrate detection accuracy of [email protected] = 0.995, pose tracking precision of σ<1.05σ< 1.05 mm, and successful grasping on a real robot at five positions within the workspace. We further verify the generality of the pipeline on an automobile-window glue-application task. The results show that combining foundation models for perception with everyday imaging devices (e.g., smartphones) can substantially lower the deployment barrier for humanoid manipulation tasks.

Yifei Yan, Yankai Liao, Linqi Ye

School of Future Technology, Shanghai University, Shanghai, 200444, China · School of Mechanical Engineering, Shanghai Institute of Technology, Shanghai, 201418, China

Robotic GraspingHumanoid Robotics
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Apr 16, 2026

HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments

We present HRDexDB, a real-world 4D dexterous grasping dataset capturing 3D hand-object interaction trajectories over time across five embodiments. The dataset comprises 3.2K trials over 100 diverse objects. Using a synchronized multi-camera system and an integrated reconstruction pipeline, HRDexDB provides multi-view and egocentric RGB observations, 3D hand geometry, robot states, and object 6D pose trajectories, together with success/failure annotations. Human and robotic hands interact with shared objects, enabling the study of embodiment-dependent grasp strategies and contact patterns. We demonstrate the dataset's utility through human-to-robot contact map transfer, visual robot-object contact estimation, and retrieval-assisted grasping. Together, these results establish HRDexDB as a resource for studying and learning dexterous interactions across human and robotic embodiments.

Jongbin Lim, Taeyun Ha, Seongho Cha +6

Seoul National University · RLWRLD

Robotic Data CollectionDexterous ManipulationHuman-Object InteractionRobotic GraspingCross-Embodiment Robot LearningRobot Manipulation Benchmarks+1
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Apr 16, 2026

Differentiable Object Pose Connectivity Metrics for Regrasp Sequence Optimization

Regrasp planning is often required when one pick-and-place cannot transfer an object from an initial pose to a goal pose while maintaining grasp feasibility. The main challenge is to reason about shared-grasp connectivity across intermediate poses, where discrete search becomes brittle. We propose an implicit multi-step regrasp planning framework based on differentiable pose sequence connectivity metrics. We model grasp feasibility under an object pose using an Energy-Based Model (EBM) and leverage energy additivity to construct a continuous energy landscape that measures pose-pair connectivity, enabling gradient-based optimization of intermediate object poses. An adaptive iterative deepening strategy is introduced to determine the minimum number of intermediate steps automatically. Experiments show that the proposed cost formulation provides smooth and informative gradients, improving planning robustness over other alternatives. They also demonstrate generalization to unseen grasp poses and cross-end-effector transfer, where a model trained with suction constraints can guide parallel gripper grasp manipulation. The multi-step planning results further highlight the effectiveness of adaptive deepening and minimum-step search.

Liang Qin, Weiwei Wan, Kensuke Harada

All authors are with the Graduate School of Engineering Science, The University of Osaka, Japan.

Robotic GraspingRobotic Manipulation
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Apr 14, 2026

FastGrasp: Learning-based Whole-Body Control Method for Fast Dexterous Grasping with Mobile Manipulators

Fast dexterous grasping while a mobile base remains in motion requires coordinated whole-body control and rapid adaptation to physical contact. We propose FastGrasp, a two-stage learning framework that integrates grasp guidance, whole-body control, and tactile feedback. First, a pretrained conditional variational autoencoder generates diverse grasp candidates from object point clouds. Candidates are filtered by approach direction and supporting-surface constraints, then ranked using an envelopment-based criterion combining grasp width and depth coverage measures. Second, a reinforcement learning policy jointly controls the mobile base, arm, and dexterous hand, using the selected grasp as guidance and tactile observations for online grasp adjustment. The policy is trained with domain randomization and deployed with command filtering and tactile-triggered grasp tightening. Simulation experiments show higher grasp success rates than the evaluated baselines under full and partial point-cloud observations, while real-world experiments demonstrate sim-to-real transfer across diverse object geometries.

Heng Tao, Yiming Zhong, Zemin Yang +1

ShanghaiTech University

RL for RoboticsRobotic GraspingMobile ManipulationWhole-Body Robotic ManipulationWhole-Body Control
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Apr 13, 2026

CLASP: Closed-loop Asynchronous Spatial Perception for Open-vocabulary Desktop Object Grasping

Robot grasping of desktop object is widely used in intelligent manufacturing, logistics, and agriculture.Although vision-language models (VLMs) show strong potential for robotic manipulation, their deployment in low-level grasping faces key challenges: scarce high-quality multimodal demonstrations, spatial hallucination caused by weak geometric grounding, and the fragility of open-loop execution in dynamic environments. To address these challenges, we propose Closed-Loop Asynchronous Spatial Perception(CLASP), a novel asynchronous closed-loop framework that integrates multimodal perception, logical reasoning, and state-reflective feedback. First, we design a Dual-Pathway Hierarchical Perception module that decouples high-level semantic intent from geometric grounding. The design guides the output of the inference model and the definite action tuples, reducing spatial illusions. Second, an Asynchronous Closed-Loop Evaluator is implemented to compare pre- and post-execution states, providing text-based diagnostic feedback to establish a robust error-correction loop and improving the vulnerability of traditional open-loop execution in dynamic environments. Finally, we design a scalable multi-modal data engine that automatically synthesizes high-quality spatial annotations and reasoning templates from real and synthetic scenes without human teleoperation. Extensive experiments demonstrate that our approach significantly outperforms existing baselines, achieving an 87.0% overall success rate. Notably, the proposed framework exhibits remarkable generalization across diverse objects, bridging the sim-to-real gap and providing exceptional robustness in geometrically challenging categories and cluttered scenarios.

Yiran Ling, Wenxuan Li, Siying Dong +5

Harbin Institute of Technology · National Key Laboratory of Smart Farm Technologies and Systems · Northeastern University +1

Spatial Grounding for VLA ModelsRobotic GraspingRobotic ManipulationVLMs for RoboticsVision-Language Grounding
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Apr 10, 2026

Kinematics of continuum planar grasping

This paper presents an analytical framework to study the geometry arising when a soft continuum arm grasps a planar object. Both the arm centerline and the object boundary are modeled as smooth curves. The grasping problem is formulated as a kinematic boundary following problem, in which the object boundary acts as the arm's 'shadow curve'. This formulation leads to a set of reduced kinematic equations expressed in terms of relative geometric shape variables, with the arm curvature serving as the control input. An optimal control problem is formulated to determine feasible arm shapes that achieve optimal grasping configurations, and its solution is obtained using Pontryagin's Maximum Principle. Based on the resulting optimal grasp kinematics, a class of continuum grasp quality metrics is proposed using the algebraic properties of the associated continuum grasp map. Feedback control aspects in the dynamic setting are also discussed. The proposed methodology is illustrated through systematic numerical simulations.

Udit Halder, Nicolas Echeverria Zambrano, Xincheng Li

Department of Mechanical and Aerospace Engineering, University of South Florida.

Robotic Grasping
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Apr 10, 2026

A Benchmark of Dexterity for Anthropomorphic Robotic Hands

Dexterity is a central yet ambiguously defined concept in the design and evaluation of anthropomorphic robotic hands. In practice, the term is often used inconsistently, with different systems evaluated under disparate criteria, making meaningful comparisons across designs difficult. This highlights the need for a unified, performance-based definition of dexterity grounded in measurable outcomes rather than proxy metrics. In this work, we introduce POMDAR, a comprehensive dexterity benchmark that formalizes dexterity as task performance across a structured set of manipulation and grasping motions. The benchmark was systematically derived from established taxonomies in human motor control. It is implemented in both real-world and simulation and includes four manipulation configurations: vertical and horizontal configurations, continuous rotation, and pure grasping. The task designs contain mechanical scaffolding to constrain task motion, suppress compensatory strategies, and enable metrics to be measured unambiguously. We define a quantitative scoring metric combining task correctness and execution speed, effectively measuring dexterity as throughput. This enables objective, reproducible, and interpretable evaluation across different hand designs. POMDAR provides an open-source, standardized, and taxonomy-grounded benchmark for consistent comparison and evaluation of anthropomorphic robot hands to facilitate a systematic advancement of dexterous manipulation platforms. CAD, simulation files, and evaluation videos are publicly available at https://srl-ethz.github.io/POMDAR/.

Davide Liconti, Yuning Zhou, Yasunori Toshimitsu +2

Soft Robotics Lab, D-MAVT, ETH Zurich

Dexterous ManipulationRobot Manipulation BenchmarksRobotic Hand DesignRobotic Grasping
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Apr 9, 2026

Task-Aware Bimanual Affordance Prediction via VLM-Guided Semantic-Geometric Reasoning

Bimanual manipulation requires reasoning about where to interact with an object and which arm should perform each action, a joint affordance localization and arm allocation problem that geometry-only planners cannot resolve without semantic understanding of task intent. Existing approaches either treat affordance prediction as coarse part segmentation or rely on geometric heuristics for arm assignment, failing to jointly reason about task-relevant contact regions and arm allocation. We reframe bimanual manipulation as a joint affordance localization and arm allocation problem and propose a hierarchical framework for task-aware bimanual affordance prediction that leverages a Vision-Language Model (VLM) to generalize across object categories and task descriptions without requiring category-specific training. Our approach fuses multi-view RGB-D observations into a consistent 3D scene representation and generates global 6-DoF grasp candidates, which are then spatially and semantically filtered by querying the VLM for task-relevant affordance regions on each object, as well as for arm allocation to the individual objects, thereby ensuring geometric validity while respecting task semantics. We evaluate our method on a dual-arm platform across nine real-world manipulation tasks spanning four categories: parallel manipulation, coordinated stabilization, tool use, and human handover. Our approach achieves consistently higher task success rates than geometric and semantic baselines for task-oriented grasping, demonstrating that explicit semantic reasoning over affordances and arm allocation helps enable reliable bimanual manipulation in unstructured environments.

Fabian Hahne, Vignesh Prasad, Georgia Chalvatzaki +2

Department of Computer Science, Technical University of Darmstadt · Hessian Center for Artificial Intelligence (Hessian.AI), Darmstadt · Robotics Institute Germany (RIG) +2

Bimanual Robotic ManipulationRobotic ManipulationRobotic Grasping
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