cs.ROSep 4, 2026
SaveBenchmarking Dexterity of Multifingered Robot Hands: A Review and Perspective
Organizations: Center for Robotics and Biosystems, Northwestern Univ., Evanston, IL, USA · Department of Mechanical Engineering, Texas A&M Univ., College Station, TX, USA · Department of Electrical and Computer Engineering, Carnegie Mellon Univ., Pittsburgh, PA, USA · Department of Mechanical Engineering, Florida A&M Univ., Tallahassee, FL, USA
Abstract
Robot hands are a key interface between AI and the physical world, making advances in robotic dexterity essential to realizing the vision of physical AI. While impressive dexterity has been demonstrated with simple grippers, multifingered hands offer the potential for substantially greater versatility, precision, and adaptability in manipulation. In this review, we survey the state of the art in benchmarking the dexterity of multifingered robot hands. Recognizing dexterity as a complex and multifaceted concept, we present the perspective of the U.S. National Science Foundation HAND Engineering Research Center, with a particular focus on fine in-hand manipulation. We introduce a framework consisting of three benchmark levels that correspond to increasing system complexity, review representative benchmarks at each level, and propose new benchmarks and metrics to address limitations in the literature. More information can be found at https://hand-erc.github.io/benchmarking/.
Figures & tables
Figure 1: (a) A manipulation task demonstrating direct manipulation of chopsticks and indirect manipulation of the dumpling through tools (the chopsticks). This task also demonstrates the three major types of manipulation: nonprehensile manipulation (pushing the dumpling with chopsticks), fixed grasp manipulation (of the bottom chopstick), and in-hand manipulation (of the top chopstick). (b) Components of a robot hand considered in this review. (c) Four dexterity benchmarking levels, from the lowest component level to the highest application level. The sensitivity of performance to design decisions becomes increasingly complex to estimate for higher-level benchmarks.
Figure 2: Examples of existing system-level dexterity benchmarks. Grasp taxonomy-inspired benchmarks : (a) CMU Foam Hand III performing Elliott and Connolly manipulation patterns (adapted from [ Coulson2021 ] with permission). (b) Equipment for evaluating the POMDAR benchmarks (adapted from [ liconti2026 ] with permission). Task-centric domain-specific benchmarks : (c) NIST taskboards (adapted from [ kimble2020 ] with permission). (d) Dyson household clothing benchmark (adapted from [ dysonbench2023 ] with permission). Task-centric large-scale simulation benchmarks : (e) RLBench simulations (adapted from [ james2019rlbench ] with permission). (f) ManiSkill3 implementations (adapted from [ taomaniskill3 ] with permission).
| Benchmark category | Subcategory | References |
| Taxonomy-inspired benchmarks | Grasp/movement primitive benchmarks | [ Zhou2020 , Coulson2021 ] |
| Atomic task benchmarks | [ liconti2026 ] | |
| Task-centric benchmarks | Clinical dexterity tests | [ jebsen1969objective , light2002establishing , barreca2004development , barreca2005test , mathiowetz1985adult ] |
| Domain-specific benchmarks | [ kimble2020 , Cruciani2020 , corl2020softgym , dysonbench2023 ] | |
| Large-scale simulation benchmarks | [ Mu2021ManiSkillGM , james2019rlbench , mclean2025metaworld , Mu_2025_CVPR , chen2025robotwin , Bao2023 , wang2026 , taomaniskill3 , dexverse , wu2025robomind , Chen2022TowardsHB , chen2023daxbench ] | |
| Atomic task benchmarks | [ Rajeswaran2018 , rlwrld2026dexbench , chen2026manipulationnet ] |
Table 1: Existing system-level benchmarks.
| Task name | Description |
| Box and blocks | Transfer as many small wooden blocks as possible, one at a time, from a cluttered bin to another bin within 60 seconds [ mathiowetz1985adult ] . |
| Peg-in-hole | Insert pegs of various shapes and tolerances into the NIST Assembly Taskboard M1 [ kimble2020 ] . |
| Pick up flat object | Grasp and lift an M6 washer initially resting flat on a rigid tabletop [ rlwrld2026dexbench ] . |
| Tie a knot | Tie a string on a knot board into a tight overhand knot. |
| Twist lid on jar | Pick up a regular mouth Mason jar lid from a table and thread and tighten it onto a fixed jar. |
| Use screwdriver | Beginning with a partially threaded screw, grasp a screwdriver and use it to drive the screw into a threaded hole. |
Table 2: Summary of current HAND system-level benchmarks.
Figure 3: Existing hand-level benchmarks. Mobility benchmarks: (a) Twelve of the 33 grasps in the GRASP taxonomy, representing the precision grasp category (adapted from [ Feix2016TheGT ] with permission). Robot hands are given a score based on how many of the grasps in the GRASP taxonomy they can achieve. (b) A visualization of the 10 points on the hand for the Kapandji score [ kapandji_cotation_1986 ] . A hand is given a score based on how many of these points the thumb can touch. (c) The KaRMA benchmark evaluates a hand’s ability to translate or rotate an object in a two-finger pinch grasp for various object locations in the hand’s workspace (adapted from [ peticco_karma_2026 ] with permission). Physical performance benchmarks: (d) Performance measures proposed by NIST and the associated measurement instruments (adapted from [ falco_benchmarking_2020 ] with permission).
| Hand | Finger | Wrist | ||||
| Metric | Reference | Metric | Reference | Metric | Reference | |
| Effort | Pullout strength | [ kragten_proposal_2010 , falco_grasping_2015 ] | Fingertip force | [ falco_benchmarking_2020 ] | Maximum torque | [ fanweiren2022robotica , peticcoulloamarangola2025arxiv , friedlhoppnerpetit2011iros , bajajspiersdollar2019tro , bridgwater_robonaut_2012 ] |
| Grasp strength | [ falco_grasping_2015 , ma_yale_2017 ] | Fingertip force control accuracy | [ falco_performance_2018 ] | |||
| Fingertip force sensing accuracy | [ falco_performance_2018 ] | |||||
| Flow | Mobility and connectivity | [ salisbury_articulated_1982 ] | Finger speed | [ bridgwater_robonaut_2012 , ShadowDEXEE , zhou_design_2024 , ShadowDexterousHand , roboteraxhand ] | Maximum speed | [ fanweiren2022robotica , peticcoulloamarangola2025arxiv , friedlhoppnerpetit2011iros , bajajspiersdollar2019tro , bridgwater_robonaut_2012 ] |
| Anthropomorphism, anthropomorphic grasping ability | [ Feix2016TheGT , Cutkosky1989OnGC , llop-harillo_anthropomorphic_2019 , feix_metric_2013 ] | Motion repeatability | [ falco_performance_2018 , falco_benchmarking_2020 ] | Range of motion | [ bajajspiersdollar2019tro ] | |
Table 3: A summary of existing hand-level benchmark metrics grouped by effort, flow, dynamics, and durability. Hand grasping metrics are grouped with the motion benchmarks.
Figure 4: (a) The mapping of efforts and flows between actuators and fingers describes hand transparency. A generalized physical model provides categories for benchmarking hand-level physical performance based on efforts, flows, and mechanical responsiveness. The durability category is added, as it is critical to real-world deployment of hands. (b) HAND finger testbeds under development at Texas A&M University (left) and Northwestern University (right). The non-backdrivable fingertip dynamometer (right) with collocated force and position sensing at the output is used to measure finger dynamic properties. (c) The mechanical impedance frequency response for the unpowered robot finger [ batteas2025design ] as measured by the fingertip dynamometer in (b). A dynamic model is fit to the data, and the effective mass, stiffness, and damping of the finger is found to be g, N/m, and Ns/m. For reference, a human finger measured with the same device yielded values of g, N/m, and Ns/m.
Figure 5: Existing component-level benchmarking testbeds. (a) An actuator dynamometer designed to evaluate the dynamic performance of electric motors (adapted from [ Towards_ACT_Char2026 ] with permission). (b) Routing elements and load cells are used to characterize frictional losses in a tendon transmission with sliding surfaces (adapted from [ PalliBorghesanMelchiorri2009ICRA ] with permission). (c) A multi-axis stage equipped with a force sensor is used to measure the response of a tactile sensor (adapted from [ Buscher2015IROS ] with permission).