Dexterous Manipulation

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Latest papers 138

26 papers in the last four weeks, up 271% on the four weeks before. 0.2% of all new papers.

Mar 17, 2026

TeleDex: Accessible Dexterous Teleoperation

Despite increasing dataset scale and model capacity, robot manipulation policies still struggle to generalize beyond their training distributions. As a result, deploying state-of-the-art policies in new environments, tasks, or robot embodiments often requires collecting additional demonstrations. Enabling this in real-world deployment settings requires tools that allow users to collect demonstrations quickly, affordably, and with minimal setup. We present TeleDex, an open-source system for intuitive teleoperation of dexterous hands and robotic manipulators using any readily available phone. The system streams low-latency 6-DoF wrist poses and articulated 21-DoF hand state estimates from the phone, which are retargeted to robot arms and multi-fingered hands without requiring external tracking infrastructure. TeleDex supports both a handheld phone-only mode and an optional 3D-printable hand-mounted interface for finger-level teleoperation. By lowering the hardware and setup barriers to dexterous teleoperation, TeleDex enables users to quickly collect demonstrations during deployment to support policy fine-tuning. We evaluate the system across simulation and real-world manipulation tasks, demonstrating its effectiveness as a unified scalable interface for robot teleoperation. All software and hardware designs, along with demonstration videos, are open-source and available at orayyan.com/teledex.

Omar Rayyan, Maximilian Gilles, Yuchen Cui

University of California, Los Angeles

Robot Teleoperation Robotic Data Collection Dexterous Manipulation

Mar 14, 2026

GraspADMM: Improving Dexterous Grasp Synthesis via ADMM Optimization

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

Liangwang Ruan, Jiayi Chen, He Wang +1

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

Robotic Grasping Dexterous Manipulation Contact-Rich Robotic Manipulation

Mar 14, 2026

Robust Sim-to-Real Cloth Untangling through Reduced-Resolution Observations via Adaptive Force-Difference Quantization

Robotic cloth untangling requires progressively disentangling fabric by adapting pulling actions to changing contact and tension conditions. Because large-scale real-world training is impractical due to cloth damage and hardware wear, sim-to-real policy transfer is a promising solution. However, cloth manipulation is highly sensitive to interaction dynamics, and policies that depend on precise force magnitudes often fail after transfer because similar force responses cannot be reproduced due to the reality gap. We observe that untangling is largely characterized by qualitative tension transitions rather than exact force values. This indicates that directly minimizing the sim-to-real gap in raw force measurements does not necessarily align with the task structure. We therefore hypothesize that emphasizing coarse force-change patterns while suppressing fine environment-dependent variations can improve robustness of sim-to-real transfer. Based on this insight, we propose Adaptive Force-Difference Quantization (ADQ), which reduces observation resolution by representing force inputs as discretized temporal differences and learning state-dependent quantization thresholds adaptively. This representation mitigates overfitting to environment-specific force characteristics and facilitates direct sim-to-real transfer. Experiments in both simulation and real-world cloth untangling demonstrate that ADQ achieves higher success rates and exhibits greater robustness in sim-to-real transfer than policies using raw force inputs. Supplementary video is available at https://youtu.be/ZeoBs-t0AWc

Yoshihisa Tsurumine, Yuki Kadokawa, Kohei Hayashi +2

All the authors are with the Nara Institute of Science and Technology, Japan. · Christian Diehm is with the Karlsruhe Institute of Technology, Germany.

Sim-to-Real Transfer Deformable Object Manipulation Dexterous Manipulation

Mar 12, 2026

Concurrent Prehensile and Nonprehensile Manipulation: A Practical Approach to Multi-Stage Dexterous Tasks

Dexterous hands enable concurrent prehensile and nonprehensile manipulation, such as holding one object while interacting with another, a capability essential for everyday tasks yet underexplored in robotics. Learning such long-horizon, contact-rich multi-stage behaviors is challenging because demonstrations are expensive to collect and end-to-end policies require substantial data to generalize across varied object geometries and placements. We present DexMulti, a sample-efficient approach for real-world dexterous multi-task manipulation that decomposes demonstrations into object-centric skills with well-defined temporal boundaries. Rather than learning monolithic policies, our method retrieves demonstrated skills based on current object geometry, aligns them to the observed object state using an uncertainty-aware estimator that tracks centroid and yaw, and executes them via a retrieve-align-execute paradigm. We evaluate on three multi-stage tasks requiring concurrent manipulation (Grasp + Pull, Grasp + Open, and Grasp + Grasp) across two dexterous hands (Allegro and LEAP) in over 1,000 real-world trials. Our approach achieves an average success rate of 66% on training objects with only 3-4 demonstrations per object, outperforming diffusion policy baselines by 2-3x while requiring far fewer demonstrations. Results demonstrate robust generalization to held-out objects and spatial variations up to +/-25 cm.

Hao Jiang, Yue Wu, Yue Wang +2

Thomas Lord Department of Computer Science at the University of Southern California, USA.

Dexterous Manipulation Long-Horizon Robotic Manipulation Robot Imitation Learning Data-Efficient Robot Learning Generalization in Robotic Manipulation

Mar 9, 2026

Characterization, Analytical Planning, and Hybrid Force Control for the Inspire RH56DFX Hand

Commercially accessible dexterous robot hands are increasingly prevalent, but many remain difficult to use as scientific instruments. For example, the Inspire RH56DFX hand exposes only uncalibrated proprioceptive information and shows unreliable contact behavior at high speed (up to 1618% force limit overshoot). Furthermore, its underactuated, coupled finger linkages make antipodal grasps non-trivial. We contribute three improvements to the Inspire RH56DFX to transform it from a black-box device to a research tool: (1) hardware characterization (force calibration, latency, and overshoot), (2) a sim2real validated MuJoCo model for analytical width-to-grasp planning, and (3) a hybrid, closed-loop speed-force grasp controller. We validate these components on peg-in-hole insertion, achieving 65% success and outperforming a wrist-force-only baseline of 10% and on 300 grasps across 15 physically diverse objects, achieving 87% success and outperforming plan-free grasps and learned grasps. Our approach is modular, designed for compatibility with external object detectors and vision-language models for width & force estimation and high-level planning, and provides an interpretable and immediately deployable interface for dexterous manipulation with the Inspire RH56DFX hand, open-sourced at this website https://correlllab.github.io/rh56dfx.html.

Xuan Tan, William Xie, Nikolaus Correll

Department of Computer Science, University of Colorado Boulder

Robotic Grasping Dexterous Manipulation Force-Controlled Robotic Manipulation Robot Task Planning

Mar 7, 2026

TacDexGrasp: Compliant and Robust Dexterous Grasping with Tactile Feedback

Multi-fingered hands offer great potential for compliant and robust grasping of unknown objects, yet their high-dimensional force control presents a significant challenge. This work addresses two key problems: (1) distributing forces across multiple contacts to counteract an object's weight, and (2) preventing rotational slip caused by gravitational torque when a grasp is distant from the object's center of mass. We address these challenges via tactile feedback and a Second-Order Cone Programming (SOCP)-based controller, without explicit torque modeling or slip detection. Our key insights are (1) rotational slip inevitably induces translational slip at some contact points for a multi-fingered grasp, and (2) the ratio of tangential to normal force at each contact is an effective early stability indicator. By actively constraining this ratio for each finger below the estimated friction coefficient, our controller maintains grasp stability against both translational and rotational slip. Real-world experiments on 12 diverse objects demonstrate the robustness and compliance of our approach.

Yubin Ke, Jiayi Chen, Hang Lv +2

Peking University · Galbot · Beijing Academy of Artificial Intelligence

Robotic Grasping Force-Controlled Robotic Manipulation Dexterous Manipulation

Mar 4, 2026

PTLD: Sim-to-real Privileged Tactile Latent Distillation for Dexterous Manipulation

Tactile dexterous manipulation is essential to automating complex household tasks, yet learning effective control policies remains a challenge. While recent work has relied on imitation learning, obtaining high quality demonstrations for multi-fingered hands via robot teleoperation or kinesthetic teaching is prohibitive. Alternatively, with reinforcement we can learn skills in simulation, but fast and realistic simulation of tactile observations is challenging. To bridge this gap, we introduce PTLD: sim-to-real Privileged Tactile Latent Distillation, a novel approach to learning tactile manipulation skills without requiring tactile simulation. Instead of simulating tactile sensors or relying purely on proprioceptive policies to transfer zero-shot sim-to-real, our key idea is to leverage privileged sensors in the real world to collect real-world tactile policy data. This data is then used to distill a robust state estimator that operates on tactile input. We demonstrate from our experiments that PTLD can be used to improve proprioceptive manipulation policies trained in simulation significantly by incorporating tactile sensing. On the benchmark in-hand rotation task, PTLD achieves a 182% improvement over a proprioception only policy. We also show that PTLD enables learning the challenging task of tactile in-hand reorientation where we see a 57% improvement in the number of goals reached over using proprioception alone. Website: https://akashsharma02.github.io/ptld-website/.

Rosy Chen, Mustafa Mukadam, Michael Kaess +4

Carnegie Mellon University · University of Washington · FAIR at Meta +1

Dexterous Manipulation Sim-to-Real Transfer Tactile Sensing for Robotic Manipulation In-Hand Manipulation Learning Using Privileged Information

Feb 26, 2026

DigiArm: An Anthropomorphic 3D-Printed Prosthetic Hand with Enhanced Dexterity for Typing Tasks

Despite recent advancements, existing prosthetic limbs are unable to replicate the dexterity and intuitive control of the human hand. Current control systems for prosthetic hands are often limited to grasping, and commercial prosthetic hands lack the precision needed for dexterous manipulation or applications that require fine finger motions. Thus, there is a critical need for accessible and replicable prosthetic designs that enable individuals to interact with electronic devices and perform precise finger pressing, such as keyboard typing or piano playing, while preserving current prosthetic capabilities. This paper presents a low-cost, lightweight, 3D-printed robotic prosthetic hand, specifically engineered for enhanced dexterity with electronic devices such as a computer keyboard or piano, as well as general object manipulation. The robotic hand features a mechanism to adjust finger abduction/adduction spacing, a 2-D wrist with the inclusion of controlled ulnar/radial deviation optimized for typing, and control of independent finger pressing. We conducted a study to demonstrate how participants can use the robotic hand to perform keyboard typing and piano playing in real time, with different levels of finger and wrist motion. This supports the notion that our proposed design can allow for the execution of key typing motions more effectively than before, aiming to enhance the functionality of prosthetic hands.

Dean Zadok, Tom Naamani, Yuval Bar-Ratson +5

Department of Computer Science, Technion, Haifa, Israel · Department of Mechanical Engineering, Technion, Haifa, Israel

Robotic Hand Design Assistive Robotics Dexterous Manipulation Robotic Tool Use

Feb 25, 2026

DexRepNet++: Learning Dexterous Robotic Manipulation with Geometric and Spatial Hand-Object Representations

Robotic dexterous manipulation is a challenging problem due to high degrees of freedom (DoFs) and complex contacts of multi-fingered robotic hands. Many existing deep reinforcement learning (DRL) based methods aim at improving sample efficiency in high-dimensional output action spaces. However, existing works often overlook the role of representations in achieving generalization of a manipulation policy in the complex input space during the hand-object interaction. In this paper, we propose DexRep, a novel hand-object interaction representation to capture object surface features and spatial relations between hands and objects for dexterous manipulation skill learning. Based on DexRep, policies are learned for three dexterous manipulation tasks, i.e. grasping, in-hand reorientation, bimanual handover, and extensive experiments are conducted to verify the effectiveness. In simulation, for grasping, the policy learned with 40 objects achieves a success rate of 87.9% on more than 5000 unseen objects of diverse categories, significantly surpassing existing work trained with thousands of objects; for the in-hand reorientation and handover tasks, the policies also boost the success rates and other metrics of existing hand-object representations by 20% to 40%. The grasp policies with DexRep are deployed to the real world under multi-camera and single-camera setups and demonstrate a small sim-to-real gap.

Qingtao Liu, Zhengnan Sun, Yu Cui +5

the College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China · the College of Control Science and Engineering and the State Key Laboratory of Industrial Control Technology, Zhejiang University, China · the Department of Computer Science, National University of Singapore, 15 Computing Dr, 117418, Singapore

Dexterous Manipulation Representation Learning Robotic Grasping Generalization in Robotic Manipulation Robotic RL Bimanual Robotic Manipulation

Feb 24, 2026

Grasp to Act: Dexterous Grasping for Tool Use in Dynamic Settings

Achieving robust grasping with dexterous hands remains challenging, especially when manipulation involves dynamic forces such as impacts, torques, and continuous resistance--situations common in real-world tool use. Existing methods largely optimize grasps for static geometric stability and often fail once external forces arise during manipulation. We present Grasp-to-Act, a hybrid system that combines physics-based grasp optimization with reinforcement-learning-based grasp adaptation to maintain stable grasps throughout functional manipulation tasks. Our method synthesizes robust grasp configurations informed by human demonstrations and employs an adaptive controller that residually issues joint corrections to prevent in-hand slip while tracking the object trajectory. Grasp-to-Act enables robust zero-shot sim-to-real transfer across five dynamic tool-use tasks--hammering, sawing, cutting, stirring, and scooping--consistently outperforming baselines. Across simulation and real-world hardware trials with a 16-DoF dexterous hand, our method reduces translational and rotational in-hand slip and achieves the highest task completion rates, demonstrating stable functional grasps under dynamic, contact-rich conditions.

Harsh Gupta, Mohammad Amin Mirzaee, Wenzhen Yuan

University of Illinois Urbana-Champaign

Robotic Grasping Dexterous Manipulation Dynamic Robotic Manipulation Robotic Tool Use RL for Tool Use

Feb 20, 2026

Latent Diffeomorphic Co-Design of End-Effectors for Deformable and Fragile Object Manipulation

Manipulating deformable and fragile objects remains a fundamental challenge in robotics due to complex contact dynamics and strict requirements on object integrity. Existing approaches typically optimize either end-effector design or control strategies in isolation, limiting achievable performance. In this work, we present the first co-design framework that jointly optimizes end-effector morphology and manipulation control for deformable and fragile object manipulation. We introduce (1) a latent diffeomorphic shape parameterization enabling expressive yet tractable end-effector geometry optimization, (2) a stress-aware bi-level co-design pipeline coupling morphology and control optimization, and (3) a privileged-to-pointcloud policy distillation scheme for zero-shot real-world deployment. We evaluate our approach on challenging food manipulation tasks, including grasping and pushing jelly and scooping fillets. Simulation and real-world experiments demonstrate the effectiveness of the proposed method.

Kei Ikemura, Yifei Dong, Florian T. Pokorny

Robot Morphology-Control Co-Design Deformable Object Manipulation Robotic Grasping Dexterous Manipulation

Feb 20, 2026

WHED: A Wearable Hand Exoskeleton for Natural, High-Quality Demonstration Collection

Scalable learning of dexterous manipulation remains bottlenecked by the difficulty of collecting natural, high-fidelity human demonstrations of multi-finger hands due to occlusion, complex hand kinematics, and contact-rich interactions. We present WHED, a wearable hand-exoskeleton system designed for in-the-wild demonstration capture, guided by two principles: wearability-first operation for extended use and a pose-tolerant, free-to-move thumb coupling that preserves natural thumb behaviors while maintaining a consistent mapping to the target robot thumb degrees of freedom. WHED integrates a linkage-driven finger interface with passive fit accommodation, a modified passive hand with robust proprioceptive sensing, and an onboard sensing/power module. We also provide an end-to-end data pipeline that synchronizes joint encoders, AR-based end-effector pose, and wrist-mounted visual observations, and supports post-processing for time alignment and replay. We demonstrate feasibility on representative grasping and manipulation sequences spanning precision pinch and full-hand enclosure grasps, and show qualitative consistency between collected demonstrations and replayed executions.

Mingzhang Zhu, Alvin Zhu, Jose Victor S. H. Ramos +9

Department of Mechanical and Aerospace Engineering, UCLA, Los Angeles, CA, USA. · Department of Computer Science, UCLA, Los Angeles, CA, USA. · Department of Electrical Engineering, UCLA, Los Angeles, CA, USA.

Dexterous Manipulation Robot Imitation Learning

Feb 16, 2026

DexEvolve: Evolutionary Optimization for Robust and Diverse Dexterous Grasp Synthesis

Dexterous grasping is fundamental to robotics, yet data-driven grasp prediction heavily relies on large, diverse datasets that are costly to generate and typically limited to a narrow set of gripper morphologies. Analytical grasp synthesis can be used to scale data collection, but necessary simplifying assumptions often yield physically infeasible grasps that need to be filtered in high-fidelity simulators, significantly reducing the total number of grasps and their diversity. We propose a scalable generate-and-refine pipeline for synthesizing large-scale, diverse, and physically feasible grasps. Instead of using high-fidelity simulators solely for verification and filtering, we leverage them as an optimization stage that continuously improves grasp quality without discarding precomputed candidates. More specifically, we initialize an evolutionary search with a seed set of analytically generated, potentially suboptimal grasps. We then refine these proposals directly in a high-fidelity simulator (Isaac Sim) using an asynchronous, gradient-free evolutionary algorithm, improving stability while maintaining diversity. In addition, this refinement stage can be guided toward human preferences and/or domain-specific quality metrics without requiring a differentiable objective. We further distill the refined grasp distribution into a diffusion model for robust real-world deployment, and highlight the role of diversity for both effective training and during deployment. Experiments on a newly introduced Handles dataset and a DexGraspNet subset demonstrate that our approach achieves over 120 distinct stable grasps per object (a 1.7-6x improvement over unrefined analytical methods) while outperforming diffusion-based alternatives by 46-60% in unique grasp coverage.

René Zurbrügg, Andrei Cramariuc, Marco Hutter

ETH Zürich

Robotic Grasping Dexterous Manipulation Synthetic Data Generation for Robotics Evolutionary Optimization

Jan 27, 2026

Tactile Memory with Soft Robot: Robust Object Insertion via Masked Encoding and Soft Wrist

Tactile memory, the ability to store and retrieve touch-based experience, is critical for contact-rich tasks such as key insertion under uncertainty. To replicate this capability, we introduce Tactile Memory with Soft Robot (TaMeSo-bot), a system that integrates a soft wrist with tactile retrieval-based control to enable safe and robust manipulation. The soft wrist allows safe contact exploration during data collection, while tactile memory reuses past demonstrations via retrieval for flexible adaptation to unseen scenarios. The core of this system is the Masked Tactile Trajectory Transformer (MAT

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), which jointly models spatiotemporal interactions between robot actions, distributed tactile cues, force-torque measurements, and proprioceptive signals. Through masked token prediction, MAT

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learns rich spatiotemporal representations by inferring missing sensory information from context, autonomously extracting task-relevant features without explicit subtask segmentation. We validate our approach on peg-in-hole tasks with diverse pegs and conditions in real-robot experiments. Our extensive evaluation demonstrates that MAT

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achieves higher success rates than the baselines over all conditions and shows remarkable capability to adapt to unseen pegs and conditions.

Tatsuya Kamijo, Mai Nishimura, Nodoka Shibasaki +3

OMRON SINIC X Corporation, 5-24-5 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan · The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8654, Japan · Northeastern University, 360 Huntington Ave, Boston, MA, 02115, USA

Robotic Control Dexterous Manipulation Contact-Rich Robotic Manipulation Generative Modeling Tactile Sensing for Robotic Manipulation Soft Robotics

Dec 3, 2025

House of Dextra: Cross-embodied Co-design for Dexterous Hands

Dexterous manipulation is limited by both control and design, without consensus as to what makes manipulators best for performing dexterous tasks. This raises a fundamental challenge: how should we design and control robot manipulators that are optimized for dexterity? We present a co-design framework that learns task-specific hand morphology and complementary dexterous control policies. The framework supports 1) an expansive morphology search space including joint, finger, and palm generation, 2) scalable evaluation across the wide design space via morphology-conditioned cross-embodied control, and 3) real-world fabrication with accessible components. We evaluate the approach across multiple dexterous tasks, including in-hand rotation with simulation and real deployment. Our framework enables an end-to-end pipeline that can design, train, fabricate, and deploy a new robotic hand in under 24 hours. The full framework and generated robot hands are open-sourced and available on our website.

Kehlani Fay, Darin Anthony Djapri, Anya Zorin +6

University of California, San Diego · University of California, Santa Barbara

Robotic Hand Design Dexterous Manipulation Robot Morphology-Control Co-Design Cross-Embodiment Robot Learning Robotics

Nov 29, 2025

MILE: A Mechanically Isomorphic Hand Exoskeleton and Visuotactile Robotic Hand for Data Collection in Dexterous Manipulation

Dexterous robotic hands perform complex, contact-rich manipulation. Imitation learning provides a route to such skills, but collecting human demonstrations with accurate hand actions and rich tactile information remains a key bottleneck. We present MILE, a teleoperation-based data-collection system comprising the wearable MILE exoskeleton and the mechanically corresponding MILE-Tac robotic hand. Because human-hand anatomy and wearability place tighter constraints on the high-DoF wearable, our human-first design begins with the MILE exoskeleton, equipped with custom modular joint encoders for accurate joint-angle acquisition. We then design the MILE-Tac robotic hand to share the exoskeleton's selected kinematic topology and joint-axis arrangement while satisfying robot-side implementation constraints, and equip its fingertips with compact visuotactile sensor modules. This correspondence enables direct exoskeleton-to-robot joint-space command transfer without online task-space inverse-kinematics retargeting. During teleoperation, the system synchronously records task-specific visual observations, four fingertip visuotactile streams, robot-hand proprioception, and exoskeleton-derived action commands. In a four-task teleoperation benchmark, MILE achieved a mean success rate of 76%, compared with 28% and 8% for glove-based and vision-based baselines, respectively. For downstream imitation learning, we trained paired ACT and DP policies with and without tactile input on MILE-collected demonstrations. The tactile-input variants achieved higher success rates in all paired evaluations.

Jinda Du, Jieji Ren, Qiaojun Yu +6

State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China. · Shanghai Key Laboratory of Intelligent Robotics, Shanghai Jiao Tong University, Shanghai 200240, China. · Shanghai Artificial Intelligence Laboratory, Shanghai, China. +1

Robotic Hand Design Dexterous Manipulation Robotic Data Collection Robot Teleoperation Cross-Embodiment Robot Learning Robot Imitation Learning

Nov 8, 2025

Adversarial Game-Theoretic Algorithm for Dexterous Grasp Synthesis

For many complex tasks, multi-finger robot hands are poised to revolutionize how we interact with the world, but reliably grasping objects remains a significant challenge. We focus on the problem of synthesizing grasps for multi-finger robot hands that, given a target object's geometry and pose, computes a hand configuration. Existing approaches often struggle to produce reliable grasps that sufficiently constrain object motion, leading to instability under disturbances and failed grasps. A key reason is that during grasp generation, they typically focus on resisting a single wrench, while ignoring the object's potential for adversarial movements, such as escaping. We propose a new grasp-synthesis approach that explicitly captures and leverages the adversarial object motion in grasp generation by formulating the problem as a two-player game. One player controls the robot to generate feasible grasp configurations, while the other adversarially controls the object to seek motions that attempt to escape from the grasp. Simulation experiments on various robot platforms and target objects show that our approach achieves a success rate of 75.78%, up to 19.61% higher than the state-of-the-art baseline. The two-player game mechanism improves the grasping success rate by 27.40% over the method without the game formulation. Our approach requires only 0.28-1.04 seconds on average to generate a grasp configuration, depending on the robot platform, making it suitable for real-world deployment. In real-world experiments, our approach achieves an average success rate of 85.0% on ShadowHand and 87.5% on LeapHand, which confirms its feasibility and effectiveness in real robot setups.

Yu Chen, Botao He, Yuemin Mao +7

Robotics Institute, Carnegie Mellon University, PA 15213 · Department of Computer Science, University of Maryland, MD 20742 · Department of Computer and Information Science, University of Pennsylvania, PA 19104

Robotic Grasping Dexterous Manipulation Robotic Manipulation

Aug 7, 2025

Do Robots Really Need Anthropomorphic Hands? A Comparison of Human and Robotic Hands

Human manipulation skills represent a pinnacle of voluntary motor functions, requiring the coordination of many degrees of freedom and the processing of high-dimensional sensor input to achieve remarkable dexterity. Thus, this study investigates whether the human hand, with its associated biomechanical properties, sensors, and control mechanisms, is an ideal that should be strived for in robotics. Do robots need anthropomorphic hands? First, characteristics of the human hand in terms of biomechanics and perception are extracted to compare them with currently commercially available robotic hands. From this comparison, research questions are derived that connect manipulation system complexity to skill repertoire size and dexterity. These questions are addressed through a systematic literature review, analyzing the manipulation capabilities demonstrated in 125 papers published between 2019 and 2025. Although complex five-fingered hands are often considered the ultimate goal for robotic manipulators, they are not necessary for all tasks. Findings indicate that in-hand manipulation does not benefit from anthropomorphic hand design, as simpler mechanisms are sufficient; however, mechanism complexity correlates with the breadth of manipulation tasks a hand can perform. Sensor integration and intelligent manipulation strategies remain underexplored, which may be due to a misalignment with hand design: instead of replicating the number of fingers and degrees of freedom, focusing on robustness and softness would allow more intelligent control and learning to exploit environmental contacts and integrate more sensors. Finally, the article argues for standardized evaluation criteria to enable the systematic comparison of hand designs and manipulation systems.

Alexander Fabisch, Wadhah Zai El Amri, Chandandeep Singh +1

Robotics Innovation Center, Deutsches Forschungszentrum für Künstliche Intelligenz GmbH (DFKI), Robert-Hooke-Straße 1, Bremen, 28359, Bremen, Germany. · Leibniz Universität Hannover, L3S Research Center, Appelstraße, Hanover, 30167, Lower Saxony, Germany.

Robotic Hand Design Dexterous Manipulation Robotics Robotic Manipulation Humanoid Robotics