Robotic Grasping

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37 papers in the last four weeks, up 270% on the four weeks before. 0.4% of all new papers.

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Latest papers 192

Aug 4, 2026cs.RO

Kitchen Robotic Manipulation utilizing Foundation Models

Deploying robots in everyday human environments requires perception systems that are both robust and adaptable to diverse, dynamic conditions. In this work, we present a modular perception pipeline for household manipulation tasks, with a focus on dishware handling in kitchen environments. The pipeline integrates open-vocabulary object detection, multi-view segmentation, instance-aware 3D reconstruction, and a 2D-3D feature fusion strategy for 6D pose estimation and grasp planning. Its modular design enables systematic substitution of multiple visual and geometric foundation models, allowing us to identify the best-performing configuration through extensive evaluation on a custom kitchen dataset. The best-performing configuration (LLMDet + SAMv2 + DINOv2 + GeoTransformer) achieves an ADI of 89.12% on the 20-scene kitchen benchmark with cluttered and occluded conditions. Furthermore, real-world demonstrations confirm that the best configuration can be deployed on physical robots without environment-specific retraining, successfully executing tasks such as sink-to-dishwasher transfer and cup stacking. It validates the adaptability and scalability of the pipeline and highlights its potential as a practical framework for household robotic systems. Our code and supplementary materials are available at https://raivlab.github.io/FM_kitchen .

Myung-Hwan Jeon, Sankalp Yamsani, Joohyung Kim

School of Electronic Engineering, Kumoh National Institute of Technology, Gyeongbuk, S. Korea. · Kinetic Intelligent Machine Lab (KIMLAB), University of Illinois Urbana-Champaign, Champaign, IL 61801 USA.

Robotic Grasping Robot Foundation Models Object Pose Estimation Robotic Manipulation Robotic Perception

Aug 3, 2026cs.RO

MANGO-Grasp: Mahalanobis Fields over Geometry-Oriented 3D Gaussians for Cross-Embodiment Dexterous Grasping

Cross-embodiment dexterous grasping aims to synthesize stable grasps across heterogeneous multi-fingered hands with little or no embodiment-specific tuning. Existing interaction-centric methods achieve promising results, but their object representations often underrepresent local surface geometry, while their robot descriptors do not explicitly encode both robot morphology and kinematics. We propose MANGO-Grasp, an anisotropic interaction framework that represents objects as geometry-oriented 3D Gaussian primitives and robot hands as surface keypoints encoded into morpho-kinematic descriptors. The object primitives are adaptively allocated by geometric complexity and shaped as surface-aligned plates with outward normals, encoding local geometry. Mahalanobis fields over keypoint--primitive pairs serve as interaction prediction targets during training and as optimization guidance for grasp realization at inference. These fields rise sharply for displacement along the surface normal but only gently within the tangent plane, matching the directional structure of contact. Grasps are realized with one shared optimization formulation and hyperparameter setting across all embodiments. On the CMAP and MultiGripperGrasp benchmarks, MANGO-Grasp outperforms the strongest seen-hand baseline by up to 8.24 percentage points in simulation. It also transfers zero-shot to the unseen SharpaWave hand, improving over the strongest zero-shot baseline by up to 16.57 percentage points, and achieves 86% success in real-world experiments. The code and additional materials will be made available upon publication at https://connor-zh.github.io/MANGO-Grasp/.

Heng Zhang, Kevin Yuchen Ma, Mike Zheng Shou +2

Robotics & Autonomous Systems Division, Institute for Infocomm Research, Agency for Science, Technology and Research (A*STAR-I2R), Singapore · College of Computing and Data Science, Nanyang Technological University, Singapore · Show Lab, National University of Singapore, Singapore

Robotic Grasping Cross-Embodiment Robot Learning

Aug 2, 2026cs.RO

GraRe: Grasp Candidate Re-Ranking for Frozen 6-DoF Grasp Detectors

Existing 6-DoF grasp detectors typically rank grasp candidates by detector confidence. However, our analysis on GraspNet-1Billion shows that detector confidence is often poorly aligned with grasp quality, leaving successful grasp candidates at low ranks. Motivated by this observation, we study whether learned re-ranking can improve candidate ordering while keeping detector parameters and grasp candidates unchanged. We propose GraRe, which estimates grasp quality from candidate attributes, shell-stratified local geometry, and object context. Candidate attributes condition the local geometric and object-context representations, and a Transformer fuses all three feature types. The predicted quality is combined with detector confidence to produce the final ranking. Experiments on GraspNet-1Billion with five frozen detectors show consistent improvements, with gains of up to 13.56 points in Average AP. Real-robot experiments further demonstrate robust grasping in cluttered scenes. These results show that improving candidate ranking provides a practical way to enhance frozen 6-DoF grasp detectors. Project code is available at \href{https://github.com/Minakanmi-Yuki/grare}{\textcolor{grarelink}{\texttt{\textit{https://github.com/Minakanmi-Yuki/grare}}}}.

Jibao Yuan, Yuhui Zhao, Yinzhen Lv +4

College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan, China · Department of Engineering and Information Technology, The University of Melbourne, Parkville, Victoria 3010, Australia

Robotic Grasping

Aug 1, 2026cs.RO

Self Supervised Learning from Automatically Generated Demonstrations for Visual Robotic Manipulation

Robotic manipulation often requires object specific programming, manual data annotation, or calibrated perception pipelines, which limits rapid deployment in practical settings. Learning from demonstration offers a more direct alternative, but collecting demonstrations can still demand human teleoperation or kinesthetic teaching. This paper presents a self supervised visual manipulation method in which a robot automatically generates demonstrations around a target pose and learns relative pose corrections directly from wrist mounted RGB images. The proposed pipeline uses ROS~2 and Isaac Sim to collect labeled image-pose pairs without requiring explicit camera to robot extrinsic calibration. Separate datasets are generated for planar refinement and coarse three dimensional approach, and a convolutional network is trained to regress relative translation and rotation from single frame RGB observations. During execution, a coarse to fine controller first approaches the object using models trained with height variation and then refines the final alignment using planar data. The method is evaluated both in simulation and on a real UR5e collaborative robot equipped with a gripper and a monocular camera. In simulation, the refinement stage reduces the final planar dispersion from 9.69 mm to 5.38 mm. In real world experiments, the system performs end to end grasp attempts on three physical objects and reaches success rates of 66.6% and 63.6% for two objects without object rotation, while still maintaining partial robustness under rotated conditions. These results show that automatically generated demonstrations can support practical visual manipulation with limited setup effort, while also exposing remaining challenges in depth prediction and object dependent generalization.

Andres Rivas, Anselmo R. Cukla, Rodrigo S. Guerra +2

Robotics and AI Lab, Technological University of Uruguay, Rivera, Uruguay · Center of Technology, Federal University of Santa Maria, Santa Maria, Brazil · Computer Science Center, Federal University of Rio Grande, Rio Grande, Brazil

Robotic Grasping Robot Skill Learning Robotic Manipulation Robot Imitation Learning Vision-Based Robot Control

Aug 1, 2026cs.RO

Grasp Execution Without a Planner: Configuration-Space Grasp Distance Fields with Certified Safety & Guaranteed Quality

Mainstream plan-then-track approaches to multifingered grasp execution entail selecting a grasp, planning a collision-free trajectory, and tracking the resulting trajectory via a feedback controller. Pose-estimation error during execution or scene motion can invalidate this open-loop commitment and trigger replanning. We thus present Grasp Distance Fields (GDFs), smooth softmin distances to finite sets of arm-hand grasp configurations. Using their negative gradients as feedback, we jointly select and execute grasps without planning a trajectory. A CBF-CLF quadratic program (QP) enforces self-collision, workspace, object, and obstacle-clearance constraints, while its CLF slack quantifies obstruction of task progress. We bound the softmin approximation error by

\log N/ρ

and prove forward invariance of the filtered safe set. To handle changes in contact topology, we combine a hysteretic contact-mode transition with a wrench-quality CBF that limits degradation of the realized force-closure margin relative to hold onset. Using our method, a fixed-base manipulator and a Unitree G1 equipped with the same underactuated hand grasp and lift 46 of 50 test objects amid clutter and moving obstacles. The realized grasps also retain a median 94% of their synthesized quality margin, and each QP solve requires 0.09 ms within a 20 ms control interval. Project page: www.clintonenwerem.com/gdf.

Clinton Enwerem, John S. Baras, Calin Belta

Department of Electrical and Computer Engineering and the Institute for Systems Research (ISR), University of Maryland, College Park, MD, USA

Robotic Control Robotic Grasping Signed Distance Functions Contact-Rich Robotic Manipulation

Jul 31, 2026cs.RO

Compliant Sphere Lattice Contact: Distributed Contact Modeling for Sphere-Based Robot Representations

Contact planning in robotics requires models that are both computationally efficient and physically accurate. Sphere-based robot representations satisfy the first requirement by enabling fast collision checking and differentiable geometry, but sacrifice physical accuracy by relying on point contact which cannot capture contact patch area, pressure distributions, rotational stiffness, or frictional moments. We introduce Compliant Sphere Lattice Contact (CSLC), a distributed contact model that operates natively on sphere representations by modeling the robot interface as a compliant lattice of surface spheres connected through anchor and lateral springs. When pressed against an object, the lattice deforms to produce a spatially distributed contact patch that improves the physical accuracy of sphere-based contact. We validate CSLC across two independent solvers and show preliminary results demonstrating contact patch formation and improved grasp stability.

Nataliya Nechyporenko, Ava Abderezaei, Alessandro Roncone

Robotic Grasping Contact-Rich Robotic Manipulation

Jul 31, 2026cs.RO

Developing Combined Manipulation and Locomotion Skills with Interaction Representation and Skill Composition

This paper addresses how to enable a humanoid robot to learn motion policies based on developmental principles and combine policies to create more sophisticated and useful behaviors. Specifically, we present an approach to (1) learning a whole-body reaching and grasping policy and (2) combining it and a standing-up and walking policy to compose a more complex policy of manipulation and locomotion: grasping, standing up, and walking. In (1), our method draws inspiration from harmonic analysis and adopts cubic harmonics as weights to represent the hand-object spatial relationship via spatial convolution. Utilizing an intra-episode finger joint decoupling curriculum based on developmental principles, a robot can autonomously learn a generalizable grasping policy without relying on external datasets or pretrained models. In (2), our method combines the grasping policy with a separately learned getting-up policy by providing both policies with their respective observation vectors and using hand-object interaction scores to determine when each policy should control which robot joints. Our results show a 93% zero-shot success rate for grasping unseen objects and a 96-100% success rate for standing up while holding the object. Our work also demonstrates that combining different policies is only effective if each policy learning happens on the same whole humanoid body even if a policy (such as for locomotion) does not seem to need all the body parts (such as fingers).

Fanxing Meng, Jing Xiao

Robotics Engineering Department, Worcester Polytechnic Institute, Worcester, MA, USA

Robotic Grasping Robot Skill Learning Humanoid Robot Control Whole-Body Robotic Manipulation

Jul 31, 2026cs.RO

TransGraspNet: Physically and Geometrically Consistent Manipulation of Transparent Labware

Manipulating transparent laboratory glassware that contains liquid is inherently safety-critical: even small geometric errors can cause unstable grasps and hazardous spillage. Although recent progress has been made in transparent object perception and robotic grasping, most existing systems optimize detection, depth reconstruction, and grasp planning independently, which leads to cross-stage inconsistency imperfect boundaries induce depth bleeding, distorted surfaces corrupt normal estimation, and task agnostic grasp scoring yields tilted or off-center grasps that fail under dynamic motion. In this paper, we propose TransGraspNet, a geometry physics consistent framework that explicitly enforces consistency from perception to execution through three coupled principles: boundary consistency to produce structurally reliable object contours as downstream priors, surface consistency to preserve geometric fidelity and surface normal accuracy during depth reconstruction, and physics consistency to refine grasp selection with centroid alignment and wrench-space stability for upright and dynamically robust manipulation. We evaluate TransGraspNet on public benchmarks, a dedicated transparent glassware dataset, and a real robotic platform. The results show improved boundary quality and surface normal fidelity, and demonstrate strong task-level performance in cluttered transparent scenes. Most importantly, the proposed system achieves reliable real-world operation, including high grasp success rates in clutter and zero spillage during high speed liquid transport, highlighting the effectiveness of our method.

Hailing Hu, Mingyi Zhu, Yiquan An +3

Peking University, Beijing, China (Lifeng Zhou is also with the School of Advanced Manufacturing and Robotics, Peking University) · Shanghai Jiao Tong University, Shanghai, China

Robotic Grasping Dynamic Robotic Manipulation

Jul 31, 2026cs.RO

SAGP: Semantic Affordance-Guided Grasp Planning via Coarse-Zone VLM Reasoning

Geometry-based grasp planners ensure physically valid grasps but ignore functional semantics, often generating grasps that are antipodal and collision-free yet practically inappropriate, for example, gripping a mug by its rim, a knife by the blade, or a bottle near its cap. These inconsistencies cause the downstream task to fail even when traditional grasp metrics are met. Existing vision-language model (VLM) approaches either depend on fine-grained, category-specific part segmentation or attempt to directly infer grasp poses, with the latter prone to spatial hallucinations. As a result, no practical, training-free framework has yet been proposed that robustly links high-level semantic reasoning to geometric grasp planning. We introduce Semantic Affordance-Guided Grasp Planning (SAGP), a training-free pipeline built on a coarse-zone abstraction layer. The method first partitions the object point cloud into spatial regions (top, middle, bottom, lateral sides, and protrusions) by applying PCA-based alignment followed by distance-driven DBSCAN clustering, entirely bypassing learned segmentation. A pre-trained VLM then assesses the grasp quality of each region through a structured zero-shot query, and the resulting zone-wise scores are fused with geometric, reachability, and task-alignment signals to re-rank antipodal grasp candidates. Experiments on YCB objects in PyBullet with a Franka Panda robot show that SAGP preserves the high success rate of geometry-only planning while substantially improving the functional appropriateness of selected grasps, particularly on asymmetric, handle-bearing objects where geometry alone is uninformative. The introduced coarse-zone abstraction offers an effective, training-free bridge between VLM-based reasoning and geometric grasp planning, without the need for fine-grained part segmentation.

Muhayy Ud Din, Irfan Hussain

KU Center for Autonomous Robotic Systems (KUCARS), Khalifa University, United Arab Emirates

Robotic Grasping VLMs for Robotics

Jul 31, 2026cs.RO

TacPrint: A Wearable Fingertip Tactile Sensor for Human-to-Robot Contact Reproduction

Human-centric data collection is emerging as a significant paradigm for robot skill acquisition, but seamlessly integrating low-cost, scalable tactile sensing systems that capture fine-grained fingertip interactions without compromising natural operation remains a key challenge. This reduces the reliability of human-to-robot transfer in contact-rich tasks. In this work, we present TacPrint, a wearable fingertip tactile sensor, where protrusions on the inner surface of the silicone skin are aligned one-to-one with 24 capacitive taxels to enable localized capacitive responses. A real-to-sim-to-real pipeline estimates a 35

\times

26 contact-depth map from 24-channel capacitive signals. Against simulation-generated labels, the model achieved a contact-region RMSE of 0.223

\pm

0.161 mm, a weighted-centroid error of 1.213

\pm

2.379 pixels, and an IoU of 0.829

\pm

0.169. With measured capacitive inputs, the network-predicted depth evaluated at the guide-calibrated contact center showed a mean absolute error of 0.085

\pm

0.057 mm across all 40 controlled trials, while the mean contact-position error was 0.250

\pm

0.208 mm across the 37 trials whose reference contact regions were not truncated by the sensing boundary. In human-to-robot replay, tactile-guided compensation increased grasping and wiping success rates from 0% to 91.67% and 90%, respectively. In closed-loop grasping, dense-depth feedback achieved success rates of 87.5% over all tested positions and 85% under edge-contact conditions, compared with 67.5% and 45% for raw-taxel feedback.

Yongxi Liu, Chaofan Zhang, Xingyu Zhang +4

Institute of Automation, Chinese Academy of Sciences · Imprintx Robotics · Beijing Academy of Artificial Intelligence

Robotic Grasping Tactile Sensing Contact-Rich Robotic Manipulation Tactile Sensing for Robotic Manipulation

Jul 30, 2026cs.RO

UniCross: Unified Cross-Skill Dexterous Manipulation Synthesis

Many dexterous manipulation tasks require the object to remain securely held throughout the interaction. From the perspective of hand-object relational motion, such manipulation comprises four canonical skills: grasping, relocation, in-hand rotation, and in-hand translation. Human hands flexibly compose these skills to accomplish complex tasks. Existing approaches, however, model these skills separately with skill-specific action constraints, objectives, or even dedicated hand morphologies, which breaks the compatibility and continuity required for long-horizon composition. In this work, we present a unified framework that models all four skills in a single formulation that shares the same state and action spaces and a common objective structure. This formulation enables distillation of a single cross-skill policy conditioned on the relational motion objectives, which achieves strong performance across all four skills, generalizes to unseen objects, remains robust to disturbances, and chains skills into long-horizon manipulation without switching policies. The framework also transfers effectively across different hand morphologies. Overall, our results suggest that different dexterous manipulation skills can be viewed as instantiations of a shared task formulation, revealing the intrinsic consistency. Project page: https://zdchan.github.io/UniCross/

Hui Zhang, Julian Ferchow, Jie Song +1

ETH Zürich, Switzerland · inspire AG, Zürich, Switzerland · HKUST (Guangzhou), China

Robotic Grasping Dexterous Manipulation Long-Horizon Robotic Manipulation In-Hand Manipulation Generalization in Robotic Manipulation Multi-Task Robotic Manipulation

Jul 29, 2026cs.RO

Speech2Grasp: Data-Efficient Transfer of Text-Conditioned Grasp Detection to Speech in Humanoid Robots

Humanoid robots increasingly require multi-modal understanding for natural interaction with humans. Despite the prominence of vision-language models, they generally assume textual rather than the more natural speech inputs. In this paper, we investigate whether a well-established text-conditioned model can be transferred to speech in a data-efficient manner. Using ALBEF as a case study, we conduct diagnostic analyses showing that a lightweight MLP-based projector effectively adapts it to speech, while preserving semantic discrimination and robustness. Motivated by these findings, we introduce Speech2Grasp, a framework for data-efficient transfer of text-conditioned grasp detection to speech. Real-world humanoid robot experiments show that Speech2Grasp outperforms cascaded ASR-based pipeline, while reducing inference latency. Our findings suggest a practical paradigm for extending established text-conditioned systems to speech.

Hung Nguyen, Kim Nhat Minh Nguyen, Van Duc Vu +6

University of California San Diego · VinMotion · Hanoi University of Science and Technology +2

Robotic Grasping Language-Conditioned Robot Manipulation Transfer Learning VLMs for Robotics

Jul 28, 2026cs.RO

Reeling It In: Flexible Needle Pick Up via Thread Manipulation for Autonomous Suturing

Suture-needle pickup is necessary for autonomous suturing, as a needle can be unexpectedly dropped or strategically released to adjust the grasping configuration. Current methods for autonomous needle pickup typically guide a robot to move straight toward the needle and grasp it, limited to conditions where the needle is observable and directly approachable. In addition, grasping the needle lying on tissue can lead to the robot pinching nearby tissue or the needle jumping around due to its slippery surface, posing potential safety issues. This work proposes an autonomous framework that uses a suture thread as an assistive tool for indirect needle pickup, avoiding unnecessary tool-tissue contact and enabling pickup even when the needle is occluded or inaccessible. The framework spans the entire workflow, including thread and tissue reconstruction, safe grasp-point selection, stable thread lifting, and bimanual thread-following until securing needle grasping. The robot policies account for visual uncertainty to maximize robustness in real-world environments. We evaluate the proposed framework on a da Vinci Research Kit under various real-world conditions. The results demonstrate robust performance even with a challenging thread configuration or a non-approachable needle, closing the gap in applying autonomous robot policies to unstructured suturing environments.

Emma Huang, Zih-Yun Chiu, Neelay Joglekar +2

Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093 USA. · Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218 USA. · CMU Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213 USA. +1

Robotic Grasping Robotic Manipulation Surgical Robotics

Jul 27, 2026cs.RO

FIRMGrasp: A Friction-Informed Risk Margin for Robust Grasp Synthesis

Classical grasp quality metrics assume one deterministic friction coefficient and therefore cannot assess whether a grasp maintains force closure across plausible friction values. We present FIRMGrasp, a family of grasp quality metrics that incorporates friction uncertainty through Conditional Value-at-Risk (CVaR). At confidence level

β

, we evaluate the force-closure margin at the mean of the adverse friction tail. This evaluation defines the risk-adjusted margin

\varepsilon^{(β)}

, the inscribed-ball radius of the corresponding grasp wrench space. We prove that

\varepsilon^{(β)}

varies monotonically with

β

, remains differentiable in the grasp parameters, and certifies that any grasp with

\varepsilon^{(β)} > 0

achieves force closure with probability at least

β

. Across 1,599 LEAP Hand and Allegro Hand grasps,

\varepsilon^{(β)}

identifies friction-sensitive grasps that receive high nominal Ferrari-Canny scores, and 53% of the nominally force-closed grasps lose closure in the adverse friction tail. The nominal margin ranks a successful grasp above a failed grasp with probabilities of only 0.53 in the shake test and 0.67 in the pick test, whereas

\varepsilon^{(β)}

achieves 0.63 and 0.78. At an adverse friction coefficient of 0.2, 70% of grasps with positive

\varepsilon^{(β)}

withstand a simulated lift and lateral pull, compared with 25% of grasps with positive nominal margin and nonpositive

\varepsilon^{(β)}

. We also synthesize grasps with positive

\varepsilon^{(β)}

for the RealHand L6 and LEAP Hand, both of which retain the object during adverse-friction lifts. In MuJoCo trials with the RealHand L6, 95% of grasps that establish contact and have positive

\varepsilon^{(β)}

retain the object at the same adverse friction coefficient.

Clinton Enwerem, John S. Baras, Calin Belta

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

Robotic Grasping Conditional Value-at-Risk

Jul 24, 2026cs.RO

A Monolithic Hand with Asymmetric Origami Bending and Dual-chamber Actuators

The passive adaptability inherent in soft robotic hands affords them advantages in applications that require safe and compliant interaction. However, existing soft robotic hands often struggle to simultaneously achieve adequate output performance and easy manufacturing due to their complicated structures. In this paper, we introduce the asymmetric origami bending (AOB) pattern for generating bending motion and the asymmetric dual-chamber (ADC) design for obtaining multifunction capability. The AOB single (AOB-S) chamber and AOB dual-chamber (AOB-D) units are designed and constitute the finger and palm actuators of the proposed Origami-inspired SOft Robotic (OSOR) hand. The OSOR hand achieves bio-inspired fingers-palm motions and adequate output performance within a monolithic structure that significantly simplifies the manufacturing process. By defining the asymmetric ratio to characterize the geometric asymmetry of the unit, the analytical models of the AOB and ADC structures are proposed. The Finite Element Analysis tool for the design of AOB actuators is obtained by geometric analysis. The asymmetric origami design grants the integrated manufacturing of the OSOR hand through a Selective Laser Sintering printing process with a single thermoplastic polyurethane material. The model and simulations are validated by experimental results. Experiments show the finger and palm maximum bending motion range of 203° and 40°, respectively, with output forces of 6.3 N and 16 N. The OSOR hand is capable of pinching a piece of tissue, stably grasping water bottles with two fingers, palm-only grasping, and completing the power grasps in the taxonomy of manufacturing grasps. The compactness, performance, and easy manufacturing of the proposed hand benefit the development of the soft robotic hand with new possibilities.

Nan Huang, Yuming Zhu, Zicong Zhang +5

Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, 518000, China. · University of Pennsylvania, Philadelphia, PA 19104, USA. · Sino-German College of Intelligent Manufacturing, Shenzhen Technology University, Pingshan, China.

Robotic Hand Design Robotic Grasping Soft Robotics

Jul 23, 2026cs.RO

Grasp, Handover, Rotate: Bimanual Object Reorientation via Compositional Diffusion and Energy-Based Optimization

Bimanual object reorientation - picking an object, handing it over between two arms, and placing it in a desired target pose - is valuable when direct placement from the initial grasp is infeasible due to collisions, kinematic constraints, or poor final orientation. However, achieving this under multiple competing objectives remains challenging. We introduce BiCompoDiff, a compositional diffusion and energy-based framework that jointly optimizes grasp selection, handover, regrasp, and motion planning under multiple constraints. By combining a pretrained grasp diffusion model with bimanual planning energy-based models (EBMs), our method injects gradient guidance during reverse diffusion to enforce collision avoidance, trajectory smoothness (via differentiable inverse kinematics), handover feasibility, and regrasp safety. Annealed MCMC sampling further refines grasp poses over the composite energy landscape. Experiments across diverse simulated household reorientation tasks demonstrate that BiCompoDiff achieves over 20% higher success rates and up to 37% smoother trajectories (measured by joint displacement) compared to strong sampling-based baselines. Real-world validation confirms effective sim-to-real transfer and robust performance on challenging scenes.

Wun Lam Yeung, Wenjun Liu, Yui Cheung Yu +5

The Hong Kong University of Science and Technology · Shenzhen Loop Area Institute

Robotic Grasping Bimanual Robotic Manipulation Diffusion-Based Planning Energy-Based Models

Jul 22, 2026cs.RO

SeededGrasp: Language-Guided Grasping in Complex Scenes with Multiple Embodiments

Practical robotic grasping in complex scenes requires both 3D spatial reasoning and alignment with task-specific requirements. Vision-language models (VLMs) offer a natural way to specify these requirements using language, but existing approaches either use a VLM to predict the grasp directly with limited spatial awareness, or train the VLM together with the grasping model, which requires significantly more data and compute. These limitations impede performance and have prevented scaling to multiple embodiments in complex scenes. We address this by proposing SeededGrasp, a novel data-efficient framework that enables a VLM to predict a seed point to be used as conditioning for a subsequent lightweight grasp-generation model. Our architecture decouples high-level semantic reasoning from low-level geometric execution, enabling multi-embodiment support while bypassing the need for expensive end-to-end training. To enable training such models, we release the first multi-embodiment tabletop grasping dataset comprising over 2.5M grasps in cluttered scenes. Experimental results demonstrate that our approach outperforms existing baselines, achieving 72% success in simulation and 78% in real-world grasping experiments. See our project site for data and code: https://uoft-isl.github.io/seeded-grasp/

Yang Xu, Gurpreet Singh Mukker, Raymond Wang +3

University of Toronto · Vector Institute · University of British Columbia +1

Robotic Grasping Language-Conditioned Robot Manipulation Cross-Embodiment Robot Learning VLMs for Robotics

Jul 22, 2026cs.RO

V2F: Vision-Informed Grasp Force Prediction for Damage-Aware Robotic Handling of Date Fruits

This paper presents a vision-informed grasp force prediction framework for robotic handling of date fruits. Addressing the dual challenge of high detachment forces and low bruise thresholds, we first conduct mechanical characterization on date samples to define a safe grasping envelope and quantify the relationship between fruit geometry and bioyield stress. In this work, we develop a Vision-to-Force (V2F) pipeline that combines computer vision-based segmentation, active-contour refinement, and geometric feature extraction with a physics-informed residual neural network that augments a Hertz contact equation. The resulting model maps non-contact visual descriptors and cultivar metadata to predict a safe grasp force with mean validation performance of

R^2 \approx 0.7

across unseen cultivar groups, which is a good result given the inherent mechanical variability of biological tissue. Experimental validation using a gripper and load cell indicates that the predicted forces enable stable manipulation of different types of date fruits, with residual deformations below 1 mm and no observable damage. These results show that pre-emptive, vision-driven force estimation% can replace slow and potentially damaging tactile exploration , enabling safer robotic handling of fragile fruits.

Shahd Shami, Obadah Wali, Eric Feron +1

Computer, Electrical and Mathematical Sciences and Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia

Robotic Grasping Robotic Manipulation Agricultural Robotics

Jul 21, 2026cs.RO

Design and stability analysis of an underactuated hand with passively rotating fingers

This paper presents an innovative design and stability analysis of an underactuated robotic finger with spatial mobility, designed to enhance gripping dexterity in robotic hands. The finger architecture incorporates a revolute joint at its base, enabling passive spatial rotation that facilitates both cylindrical and spherical grasping. With only two phalanges per finger, the design simplifies kinematic complexity while supporting precision and enveloping grasps. Stability criteria, based on the moment at the finger base joint induced by contact forces, are introduced to ensure reliable object gripping and prevent ejection during manipulation. The study also examines a differential mechanism that distributes a single actuation torque across multiple fingers, allowing adaptive and coordinated motion. This mechanism enhances the hand's ability to grasp diverse object shapes with minimal pre-grasp adjustments, leveraging passivity for autonomous adaptation. Theoretical findings are experimentally validated using a fully mechanical prototype, demonstrating versatility in performing cylindrical, spherical, parallel, and enveloping grasps. The integration of underactuation-both within individual fingers and among multiple fingers-reduces mechanical complexity, cost, and control demands while preserving functional adaptability. This work advances the development of compliant robotic hands suitable for applications requiring dexterity and robustness, such as agricultural robotics, logistics, assistive technologies, and waste sorting. Future research will focus on automating actuation and refining control strategies to further improve grasp stability and precision, paving the way for autonomous manipulation in unstructured environments.

Léonie Plancoulaine, Sylvain Guégan, Franck Plestan +1

LS2N, LS2N - équipe RoMas · Nantes Université, École Centrale Nantes, CNRS, LS2N, UMR 6004, 44300 Nantes, France · LGCGM +2

Robotic Hand Design Robotic Grasping

Jul 20, 2026cs.RO

Optimization of sim-to-real transfer in the humanoid robot NICO

Robotic grasping requires accurate coordination between visual perception, object localization, inverse kinematics, and hand control. However, when movements planned in simulation are executed on a physical robot, the sim-to-real gap can cause small positioning errors that prevent successful grasping. In our previous work, we introduced a low-cost haptic calibration method that improved 2D reaching accuracy of the humanoid robot NICO. In this paper, we extend this approach from reaching to tabletop object grasping by adding YOLO-based object and hand detection, stereo vision-based localization using the robot's built-in low-resolution fisheye cameras, and task-specific corrections for grasp execution. Together, these components form a novel calibration-based grasping pipeline that does not require RGB-D cameras, motion capture, or external tracking systems. We also implemented a visual feedback model that aligns the robot hand with the detected object before grasping. Our results show that the fully nonlinear calibration model achieved the best performance inside the calibrated area, while the visual feedback model achieved the highest overall grasping success across the full tabletop workspace.

Juraj Gavura, Igor Farkaš

Department of Applied Informatics Comenius University Bratislava, Slovakia

Robotic Grasping Sim-to-Real Transfer Robotics Vision-Based Robot Control

Jul 20, 2026cs.RO

Receiver-Centered Robot-to-Human Handover with Grasp-Aware Object Orientation

Collaborative robots are increasingly sharing workspaces with human operators, making tool handover a frequent and safety-critical micro-interaction. However, traditional static handovers often lead to awkward grasps when handling asymmetric industrial tools. This paper presents a receiver-centered voice-driven adaptive handover system for mechanical tools, built on a Franka cobot. Using an LLM for intention recognition and MediaPipe for real-time 3D hand tracking, the framework dynamically adjusts the end-effector's orientation to present tools in an ergonomically optimal, handle-first pose. A within-subjects study compared this adaptive approach with an object-agnostic static baseline. The results demonstrate that the adaptive system reduces the grasp delay for asymmetric tools, improving the fluency of the interaction. Furthermore, the adaptive strategy improved specific trust-related perceptions, particularly motion predictability and perceived task simplicity.

Federico Biagi, Dario Onfiani, Simone Silenzi +1

Department of Engineering "Enzo Ferrari", University of Modena and Reggio Emilia, Italy.

Robotic Grasping Human-Robot Collaboration Human-Robot Interaction

Jul 20, 2026cs.RO

Seg2Grasp: A Robust Modular Suction Grasping in Bin Picking

Current bin picking methods that rely heavily on end-to-end learning often falter when confronted with unfamiliar or complex objects in unstructured environments. To overcome these limitations, we introduce Seg2Grasp, a modular pipeline designed for robust suction grasping in dynamic and cluttered bin scenarios. Seg2Grasp is built on a three-step process: Segmentation, Grasping, and Classification. The Segmentation module employs a Transformer-based model to generate class-agnostic object masks from RGB-D images, ensuring accurate detection across various conditions. The Grasping module uses surface normals and mask proposals to determine the optimal suction points, enhancing grasp success. Finally, the Classification module leverages fine-tuned open-vocabulary Mask-CLIP for precise object identification, enabling versatile handling of diverse objects. Real-world robotic experiments demonstrate that Seg2Grasp outperforms existing methods in success rates and adaptability, establishing it as a powerful tool for automated bin picking in industrial settings.

Hye-Jung Yoon, Juno Kim, Yesol Park +2

1Interdisciplinary Program in AI, Seoul National University · 2Artificial Intelligence Institute, Seoul National University · Department of Computer Science, Seoul National University

Robotic Grasping Robotic Manipulation Robotic Perception

Jul 20, 2026cs.RO

Predicting Grasping Compliance in Robotic Hands through Analytical-Model-Informed Neural Networks

In robotic manipulation studies, grasping is often treated as a binary success or failure problem, usually defined by whether the object simply stays in the hand. For forceful tool use, however, this view is insufficient because grasp compliance becomes a critical factor governing how the hand and tool behave under load. Compliance arises from coupled kinematics, grasp configuration, passive mechanics, and contact conditions, producing nonlinear behavior in which deformation and interaction forces influence each other. Understanding this relationship is essential for predictive models of how a grasped tool and a compliant hand jointly respond to external loading. In underactuated hands, these effects are amplified: such designs offer low cost and adaptive grasping, but make compliance behavior more difficult to model and predict. Our goal is therefore to develop a predictive model for grasped tool behavior during forceful interactions. To address this challenge, we introduce an analytical model informed neural network (AMINN), a hybrid predictive model that combines an analytical mechanics layer with data driven learning to estimate grasp stability and in hand tool displacement under external loading. The model is evaluated on a three finger underactuated robotic hand and shows strong predictive capability with mechanically meaningful outputs across diverse loading conditions. Compared with a black box multilayer perceptron baseline, AMINN also achieves better energy based physical consistency. Beyond prediction accuracy alone, this framework advances physically interpretable learning for robotic manipulation and supports more reliable, safer, and more trustworthy autonomous tool use in safety critical settings during forceful interaction.

Qianwen Zhao, Long Wang

Department of Mechanical Engineering, Stevens Institute of Technology, Hoboken, NJ, USA

Robotic Grasping Robotic Tool Use Physics-Informed ML

Jul 17, 2026cs.RO

Continuously Stable Structure through Plastic Deformation

Soft robots have seen widespread adoption in interactive tasks due to their inherent compliance and adaptability. However, these advantages often come at the cost of stability, posing challenges in a dynamic environment. This limitation is especially critical in soft grippers, where instability under acceleration or external disturbances can result in grasp failure. In this study, we present a continuously stable structure through plastic deformation (CSSPD), integrated into a soft gripper. By leveraging the mechanism of plastic deformation, the gripper maintains continuous configurations without energy input, while the added stiffness ensures both static and dynamic stability. We introduce a bioinspired paw pad that significantly enhances stability and enables sensing-based rapid object grasping. Then we develop the mathematical model and optimize the kirigami structure of the metal layer. Experimental results show that the gripper can sustain a passive holding force of up to 16 N without energy input, achieving performance comparable to pneumatic actuation at 0.3 MPa. When combined with pneumatic actuation, it remains stable under pulsed accelerations of up to 400 m/s^2. It can also passively perch on tree branches for extended periods without power, demonstrating promise for mobile robotic applications.

Junlong Xiao, Yaoqiang Pan, Xuan Zhang +2

Faculty of Engineering, Monash University, Clayton, VIC 3800, Australia · Michael Yu Wang. School of Engineering, Great Bay University, Songshan Lake, Dongguan, Guangdong, China · School of Engineering, Great Bay University, Songshan Lake, Dongguan, Guangdong, China

Robotic Hand Design Robotic Grasping Soft Robotics

Jul 16, 2026cs.RO

Hybrid Rigid-Soft Robotic Gripper with Shape Adaptation, Uniform Force Distribution, and Self-Locking Capabilities

Conventional robotic grippers face a significant challenge in agricultural automation: the trade-off between compliant, adaptive grasping, pressure balancing among all joints, and high load capacity, often at the cost of high energy consumption. This paper presents a novel hybrid rigid-soft gripper that integrated low-cost, membrane-based pneumatic actuators with 3D-printed dual ratchet-pawl mechanisms to simultaneously achieve shape adaptation, uniform force distribution, and energy-free self-locking. The dual-ratchet structure assembled in an offset configuration significantly increased the angular resolution of the joint locking mechanism. Key experimental results demonstrated the gripper's superior performance: a remarkable maximum load capacity of 4200 g, far exceeding that of conventional soft grippers (45-210 g); more uniform force distribution across object sizes (1.75-35.29% difference ratio) compared to a rigid gripper (56.77-66.44%), with peak contact forces remaining below surface damage thresholds; and a 50.05% reduction in total energy consumption to 42.6 J per grasp cycle, achieved by eliminating the need for continuous pneumatic pressure through the self-locking mechanism, compared to 85.28 J for a conventional soft gripper. The combination of additive manufacturing for ratchets and commercially available materials for pneumatic chambers ensured a low-cost and easily fabricated design. These findings validated that the proposed gripper successfully bridged the gap between soft compliance and rigid reliability, offering a robust and efficient solution for scalable agricultural harvesting and manipulation tasks.

Xi Chen, Yun Wang, Lichao Yang +2

Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China. · College of Engineering, China Agricultural University, Beijing, China. · College of Engineering, Shanxi Agricultural University, Jinzhong, China.

Robotic Hand Design Robotic Grasping Agricultural Robotics Soft Robotics

Jul 15, 2026cs.RO

Beyond Visual Grasping: Benchmarking Complex Grasping from Detection to Execution

Robust robotic grasping remains a fundamental challenge for complex real-world applications. Recent advances in large-scale models demonstrate promising capabilities for reasoning in robotic tasks. However, existing benchmarks for grasping primarily focus on isolated, visual-based grasp pose detection, failing to capture the complexity of grasping tasks that require multi-step reasoning and semantic understanding during execution. To address this gap, we propose GCA-Bench, a benchmark featuring challenging \textit{grasping with complex action} scenarios that involve both scene-level reasoning and semantic constraints. GCA-Bench enables the evaluation of recent large foundation models under the same settings. To demonstrate the effectiveness of our new benchmark, we implement a diverse set of baselines, ranging from traditional grasp detection pipelines to end-to-end learning methods. Empirical studies achieve success rates below 70% on complex grasping scenarios, underscoring critical limitations. In addition, we propose new evaluation metrics, analyze critical failure models, and provide insights to guide the development of more robust and generalizable grasping strategies.

Hanyi Zhang, Khang Nguyen, Charith Munasinghe +10

University of Liverpool, UK · Mohamed bin Zayed University of Artificial Intelligence, UAE · Zurich University of Applied Science, Switzerland +3

Robotic Grasping Robot Foundation Models Robot Manipulation Benchmarks

Jul 15, 2026cs.RO

Vision-Based Obstacle Separation for Strawberry Harvesting in Clusters Using Hierarchical Reinforcement Learning

Selective harvesting in clustered strawberry environments is challenging because ripe fruits are often occluded by surrounding unripe fruits, making direct grasping unreliable. To address this problem, this paper proposes a hierarchical reinforcement learning framework, termed VGPA, which integrates a vision-guided decision mechanism and a Progressive Adaptive Exploration Strategy (PAES) for vision-based obstacle separation and harvesting. The task was decomposed into two sequential stages: obstacle separation and target grasping. At the high level, the vision-guided mechanism improved option selection and accelerated policy convergence. At the low level, PAES improved exploration efficiency and training stability during continuous control learning. In simulation experiments, the learned policy achieved a success rate of 96.7%. In addition, sim-to-real transfer experiments on a self-developed parallel robot showed that the proposed method achieved success rates ranging from 71.7% to 88.3%, outperforming direct picking while requiring only 1.22~s more average harvesting time. These results verified the effectiveness, generalization ability, and practical potential of the proposed method for robotic harvesting in complex clustered environments.

Teng Li, Hanfei Shi, Chunjiang Zhao +1

The Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China. · School of Intelligence Science and Technology, University of Science and Technology Beijing, Beijing 100083, China. · Happy Elements Ltd., Beijing 100094, China.

Robotic Grasping Hierarchical RL Robotic Manipulation Agricultural Robotics Vision-Based Robot Control

Jul 15, 2026cs.RO

Towards a Modular Bin-picking Framework for Handling Object Pose Uncertainties

In recent years, there has been growing interest in robust robotic systems for precise bin-picking applications. To achieve reliable performance, such systems must address errors arising from both the object pose estimation and the grasping process. Although various approaches have been proposed, they typically target specific challenges and do not offer general solutions. In this paper, we present a modular framework that jointly handles both error types. The framework incorporates object pose distribution estimation to account for pose uncertainty, which frequently arises in situations with ambiguous observations where a single correct pose cannot be determined. To further reduce uncertainty, we introduce a second-viewpoint module that computes complementary pose distributions, which are subsequently fused. This fusion decreases overall uncertainty and improves system efficiency. Additionally, two independent modules are included to compensate for grasping errors. The modular design allows the components to be combined for optimal performance or used individually, depending on the physical setup. The proposed method is evaluated in a real-world setup with three different objects, with no errors, and all modules are shown to improve efficiency. These results suggest that incorporating pose distributions with grasping pose errors is a promising direction for developing more flexible and reliable robotic production systems. To the best of our knowledge, this is the first framework that jointly addresses both grasping and object pose uncertainties using interchangeable modules. We believe there is ample opportunity to integrate additional modules, resulting in improved performance and flexibility. The current framework is limited to pose uncertainties in SO(2), but it could be extended to SE(3), enabling additional modules to improve the system.

Frederik Hagelskjær

SDU Robotics The Mærsk Mc-Kinney Møller Institute University of Southern Denmark Odense, Denmark

Robotic Grasping Object Pose Estimation Robotic Manipulation

Jul 13, 2026cs.RO

Robust In-Hand Manipulation via Priors in Reinforcement Learning and Mechanical Design

In-hand manipulation without external sensing is challenging due to uncertainties from finger-object contacts and disturbances by gravity. While reinforcement learning has shown promise in learning complex finger gaiting, existing approaches do not prioritize maintaining well-conditioned grasps for sustained manipulation. We introduce two complementary physics priors for robust in-hand rolling: a global grasp-quality prior derived from classical grasp analysis and a local contact-geometry prior based on fingertip curvature. The grasp-quality prior is used as a dense reward-shaping term that encourages well-distributed contacts with improved worst-case wrench resistance. The contact-geometry prior is expressed in the fingertip geometry that mechanically shapes the contact interface toward task-aligned rolling while reducing off-axis drift. We evaluate the effect of these priors on learning in-hand rolling manipulation for a multifingered robotic hand manipulating three different objects at four palm orientations. Results show significant improvement in rotation efficiency, grasp stability, and disturbance rejection, suggesting that physics priors embedded in both learning and fingertip morphology improve task robustness and sim-to-real transfer. An overview video can be found at https://youtu.be/pdd1wHxQnJM?si=dM-U5kiiPTYsk3Pk.

Yifei Chen, Shihan Lu, Ed Colgate +1

Center for Robotics and Biosystems, Northwestern University

Robotic Hand Design Robotic Grasping Reinforcement Learning In-Hand Manipulation Physics-Informed RL Robust RL

Jul 13, 2026cs.RO

GraspGraphNet: Graph-Structured Multi-Embodiment Dexterous Grasp Generation

Dexterous grasp generation across robot hands is challenging because hands differ in kinematic topology, actuation dimensions, and native command spaces. We introduce GraspGraphNet, a topology-aware grasp generation framework that represents each hand as a URDF-derived kinematic graph and directly generates executable palm poses and joint configurations. GraspGraphNet combines hierarchical object surface encoding, differentiable forward kinematics, and dynamic world-edge message passing to model evolving robot-object interactions. It applies conditional flow matching directly in executable palm-pose and joint-state space, avoiding post-processing optimization, inverse kinematics, and retargeting. Using a shared model trained on Barrett Hand, Allegro Hand, and Shadow Hand, GraspGraphNet achieves an average success rate of 83.48% with 40ms inference time per grasp on a 40-object benchmark. Without retraining, the same model achieves 72.70% success on controlled finger-removal variants, demonstrating robustness to hand-topology variations. These results suggest that graph-structured hand representations can effectively support dexterous grasp generation across robot hands with different kinematic structures. Project: https://lysees.github.io/graspgraphnet-page

Yeonseo Lee, Taeyeop Lee, Hyosup Shin +2

Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea.

Robotic Grasping Graph Neural Networks Generalization in Robotic Manipulation