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.

May 18, 2026

DexHoldem: An Agentic Robotics Benchmark for Dexterous Manipulation in Texas Hold'em

Evaluating embodied systems with real dexterous hardware requires more than isolated motor-skill tests: an agent must perceive a changing scene (e.g. a tabletop), choose a context-appropriate action, execute it with a dexterous hand, and leave the scene usable for later decisions. We introduce DexHoldem, a comprehensive real-world benchmark evaluating Texas Hold'em related dexterous manipulations with a ShadowHand. DexHoldem provides 1,470 teleoperated demonstrations across 14 Texas Hold'em manipulation primitives, a standardized physical policy benchmark, and an agentic perception benchmark that tests whether agents can recover the structured game state needed for embodied decision making. On primitive execution,

π_{0.5}

obtains the highest task completion rate (

61.2\%

), while

π_{0.5}

and

π_0

tie on scene-preserving success rate (

47.5\%

). On agentic perception, Opus 5.5 narrowly leads on both strict problem-level accuracy (

49.1\%

) and average field-wise accuracy (

80.6\%

); the gap between the two exposes the distance between isolated visual sub-capabilities and complete routing-relevant state recovery. Finally, we instantiate the full embodied-agent loop with one agent--policy pairing over 33 closed-loop hand-level rollouts, in which only

12.1\%

of hands complete; retries restore the failed primitive in 12 of 34 dispatches and resolve prolonged execution stalls in three of the four completed hands, which would otherwise have required manual termination. Only one hand completes with neither a retry nor a human-help request. DexHoldem therefore evaluates dexterous tabletop execution, agentic perception, and embodied decision routing in a shared physical setting. Project website: https://dexholdem.github.io/Dexholdem/

Feng Chen, Tianzhe Chu, Li Sun +6

Dexterous Manipulation AI Agent Evaluation Robotic Manipulation Robot Manipulation Benchmarks Embodied AI

May 18, 2026

Dexora: Open-source VLA for High-DoF Bimanual Dexterity

Vision-Language-Action (VLA) models have recently become a central direction in embodied AI, but current systems are restricted to either dual-gripper control or single-arm dexterous hand manipulation. While low-dimensional gripper control can often be handled with simpler methods, high-dimensional dexterous hand control benefits greatly from full end-to-end VLA learning. In this work, we introduce Dexora, the first open-source VLA system that natively targets dual-arm, dual-hand high-DoF manipulation. We design a hybrid teleoperation pipeline that decouples gross arm kinematics (captured with a custom exoskeleton backpack) from fine finger motion (markerless hand tracking via Apple Vision Pro), and that drives both a physical dual-arm dual-hand platform and an identical MuJoCo digital twin. Using that interface, we assemble a large training corpus: an embodiment-matched synthetic corpus (100K simulated trajectories, 6.5M frames) and a real-world dataset of 10K teleoperated episodes (2.92M frames). To mitigate noisy teleoperation demonstrations, we propose a data-quality-aware training recipe: an offline discriminator provides clip-level weights for diffusion-transformer policy training, down-weighting low-quality demonstrations. Empirically, Dexora outperforms competitive VLA baselines on both basic and dexterous benchmarks (e.g., average dexterous success 66.7% vs. 51.7%), attains 90% success on basic tasks, and shows robust out-of-distribution and cross-embodiment generalization. Ablations confirm the importance of real data and the discriminator for dexterity.

Zongzheng Zhang, Jingrui Pang, Zhuo Yang +22

Tsinghua University · Beijing Academy of Artificial Intelligence · The University of Hong Kong +3

Dexterous Manipulation Bimanual Robotic Manipulation Vision-Language-Action Models Robot Imitation Learning

May 18, 2026

A Dexterous and Compliant Gripper With Soft Hydraulic Actuation for Microgravity Manipulation

Astrobee's existing one-degree-of-freedom (DOF) underactuated compliant claw gripper enables perching on the International Space Station (ISS), but provides limited capability for continuous dexterous manipulation. More complex microgravity tasks require an end-effector that can maintain stable contact while limiting disturbance to the free-flying base, since contact forces directly couple into base motion. This article presents the integration of DexCoHand, a dexterous and compliant two-finger, 6-DOF gripper, with the Astrobee free-flying robot for microgravity manipulation. The system is evaluated in MuJoCo using Astrobee's standard handrail perching sequence, including approach, perching, and subsequent pan and tilt motions. Compared with Astrobee's existing gripper, DexCoHand preserves the commanded pan and tilt motions while reducing unintended cross-axis base motion. Hardware experiments on Earth further demonstrate DexCoHand's dexterous manipulation capabilities and its potential for more adaptable intelligent manipulation tasks.

William Su, Jordan Kam, Yixiao Wang +1

Aerospace Engineering Program, University of California, Berkeley, Berkeley, CA 94720, USA. · Department of Mechanical Engineering, University of California, Berkeley, Berkeley, CA 94720, USA. · Department of Mechanical Engineering, National University of Singapore, Singapore 117575.

Dexterous Manipulation Space Robotics Robotics Robotic Manipulation Soft Robotics

May 15, 2026

DexJoCo: A Benchmark and Toolkit for Task-Oriented Dexterous Manipulation on MuJoCo

Achieving human-level manipulation requires dexterous robotic hands capable of complex object interactions. Advancing such capabilities further demands standardized benchmarks for systematic evaluation. However, prior work does not focus on task-oriented dexterous manipulation. Existing benchmarks also often lack high quality human demonstrations and an easy-to-use evaluation pipeline. In this paper, we present DexJoCo, a benchmark and toolkit for task-oriented dexterous manipulation, comprising 11 functionally grounded tasks that evaluate tool-use, bimanual coordination, long-horizon execution, and reasoning. We develop a low-cost data collection system and collect 1.1K trajectories across these tasks, with support for domain randomization to assess robustness. We benchmark modern models under diverse settings, including visual and dynamics randomization, multi-task training, and action-head adaptation. Through extensive empirical analysis, we identify several important insights and common limitations of current policies in dexterous manipulation, highlighting key challenges for future research in dexterous hand robot learning. Code is available at: https://dexjoco.github.io

Hanwen Wang, Weizhi Zhao, Xiangyu Wang +11

NLPR & MAIS, CASIA · SJTU · MBZUAI +3

Dexterous Manipulation Long-Horizon Robotic Manipulation Bimanual Robotic Manipulation Robot Manipulation Benchmarks

May 15, 2026

KaRMA: A Kinematic Metric for Fine Manipulation Ability in Robotic Hands

Traditional robotic hand metrics focus on static properties such as workspace, manipulability, and grasp stability. However, these metrics do not directly measure dexterity under the standard definition in robotic manipulation: the ability to continuously change an object's pose within the hand while maintaining contact from an initial grasp. We introduce Kinematic Rolling Manipulation Ability (KaRMA), a kinematic-only metric for fine manipulation that quantifies reachable in-hand translation and reorientation of a spherical test object within a two-finger precision pinch through feasible rolling motions. KaRMA enforces joint limits, collision constraints, rolling contact, and antipodal force feasibility, then investigates reachable in-hand object poses via breadth-first search over translation and rotation primitives. KaRMA reports three scores: translational coverage (KaRMA-T), rotational coverage (KaRMA-R), and sensitivity to the initial grasp (KaRMA-S). We evaluate KaRMA on 16 widely used robotic hands and compare against static baselines, showing that KaRMA separates hands that rank identically under static proxies, reveals translation-rotation tradeoffs invisible to existing baselines, and is qualitatively consistent with selected published task benchmarks where Jacobian-based metrics can be misleading.

Martin Peticco, Pulkit Agrawal

Improbable AI Lab, Massachusetts Institute of Technology

Dexterous Manipulation In-Hand Manipulation Robot Manipulation Benchmarks

May 13, 2026

Towards Robotic Dexterous Hand Intelligence: A Survey

Robotic dexterous hands are central to contact-rich manipulation, with rapid progress driven by advances in hardware, sensing, control, simulation, and data generation. However, existing studies are often developed under different assumptions regarding hand embodiments, sensory configurations, task settings, training data, and evaluation protocols, making systematic comparison difficult and obscuring the developmental trajectory of the field. This survey provides a holistic review of dexterous hand research from four complementary aspects. First, we present a hardware-level analysis covering actuation, transmission, perception, and representative hand designs, highlighting the key trade-offs in force capability, compliance, bandwidth, integration, and system complexity. Furthermore, we review control and learning methods for dexterous manipulation from a methodological perspective, grouping representative works by major paradigms and tracing their evolution in chronological order. In addition, we consolidate datasets, modality design, and evaluation practices, which enables methodological progress to be interpreted together with the ways in which it is trained, benchmarked, and assessed. Finally, we discuss the major limitations of current dexterous hand research and summarize the corresponding future directions. By connecting hardware analysis, methodological development, data resources, and evaluation, this survey aims to provide a structured understanding of dexterous hand research and to clarify the most important open challenges for future study.

Weiguang Zhao, Tian Liang, Xihao Guo +3

University of Liverpool · Xi’an Jiaotong-Liverpool University · Duke Kunshan University +1

Robotic Control Robotic Hand Design Dexterous Manipulation Robot Manipulation Benchmarks

May 12, 2026

DexTwist: Dexterous Hand Retargeting for Twist Motion via Mixed Reality-based Teleoperation

Dexterous teleoperation via Mixed Reality (MR)-based interfaces offers a scalable paradigm for transferring human manipulation skills to dexterous robot hands. However, conventional retargeting approaches that minimize kinematic dissimilarity (e.g., joint angle or fingertip position error) often fail in contact-rich rotational manipulation, such as cap opening, key turning, and bolt screwing. This failure stems from the embodiment gap: mismatched link lengths, joint axes/limits, and fingertip geometry can cause direct pose imitation to induce tangential fingertip sliding rather than stable object rotation, resulting in screw axis drift, contact slip, and grasp instability. To address this, we propose DexTwist, a functional twist-retargeting framework for MR-based dexterous teleoperation. DexTwist detects a tripod pinch, estimates the operator's intended screw axis and twist magnitude, and applies a real-time residual joint-space refinement that tracks turning progress while regularizing the robot tripod geometry. The refinement minimizes a virtual-object objective defined by turning angle, screw axis consistency, fingertip closure, and tripod stability. Simulation and real-world experiments show that DexTwist improves turning angle tracking and screw axis stability compared with a vector-based retargeting baseline.

Dongmyoung Lee, Chengxi Li, Dongheui Lee

Autonomous Systems Lab, Institute of Computer Technology, TU Wien, Vienna, Austria. · Institute of Robotics and Mechatronics (DLR), German Aerospace Center, Wessling, Germany.

Dexterous Manipulation Contact-Rich Robotic Manipulation Robot Teleoperation Motion Retargeting

May 12, 2026

Rainbow Deep Q-Learning with Kinematics-Aware Design for Cooperative Delta and 3-RRS Parallel Robot Insertion

This paper presents a kinematics-aware deep reinforcement learning framework based on Rainbow Deep Q-Networks (DQN) for cooperative peg-in-hole manipulation by a Delta parallel robot and a 3-RRS (Revolute--Revolute--Spherical) parallel manipulator. A key contribution is the integration of a geometric design-optimization stage that precedes learning: the 3-RRS geometry is tuned to maximize the singularity-free workspace and improve conditioning, which in turn enlarges the safe region in which the reinforcement learning policy can explore. Together the two manipulators expose a 6~degree-of-freedom (DoF) controllable subspace (three Delta translations, two 3-RRS rotations, and one 3-RRS vertical translation); the peg-in-hole task is invariant to rotation about the peg axis, so the task-relevant manifold is five dimensional. The cooperative insertion problem is cast as a Markov Decision Process with a 12-dimensional state vector and a discrete action set containing

6 \times 2 = 12

incremental commands (one positive and one negative per controlled DoF). A shaped reward combines dense proximity guidance, penalties for kinematic and workspace violations, and sparse bonuses for successful insertions. The Rainbow DQN -- integrating double Q-learning, dueling architecture, prioritized replay, multi-step returns, noisy linear layers for exploration, and a distributional value head -- is trained with a two-stage curriculum. The co-designed framework is validated in a high-fidelity kinematic simulator, where it achieves stable policy convergence, reliable insertions, and reduced constraint violations compared against a vanilla DQN agent and a classical sampling-based planner.

Hassen Nigatu, Gaokun Shi, Jituo Li +2

Robotics Institute of Zhejiang University (Yuyao Robotics Research Centre), Yuyao Technology Innovation Center, No. 479, Yuyao, Ningbo 315400, Zhejiang, China

Dexterous Manipulation Deep Q-Learning Robotics Robotic Insertion Robotic Manipulation

May 7, 2026

DexSynRefine: Synthesizing and Refining Human-Object Interaction Motion for Physically Feasible Dexterous Robot Actions

Learning dexterous manipulation from human-object interaction (HOI) data offers a scalable alternative to robot teleoperation, but HOI demonstrations are typically sparse and purely kinematic, making direct retargeting unreliable under embodiment mismatch and contact-rich dynamics. We present DexSynRefine, a coupled framework that treats HOI data as structured motion priors rather than executable robot actions. DexSynRefine first synthesizes hand-object trajectories conditioned on the task and initial object state using HOI Motion Manifold Flow Primitives (HOI-MMFP), a motion prior for coupled hand-object motion. It then physically grounds them with task-space residual reinforcement learning and adapts execution by inferring missing contact-dynamics context from proprioceptive history. Across five dexterous manipulation tasks, each stage addresses a complementary bottleneck: HOI-MMFP improves trajectory consistency and smoothness, task-space residuals provide the strongest grounding representation among the tested alternatives, and contact-dynamics adaptation enables robust real-world execution. Together, DexSynRefine improves real-world success rates over kinematic retargeting by 50-70~percentage points.

Hyesung Lee, Hyunwoo Jung, Si-Hwan Heo +1

Korea Institute of Science and Technology · KAIST · Hanyang University

HOI Generation Dexterous Manipulation Contact-Rich Robotic Manipulation Cross-Embodiment Robot Learning Dynamic Robotic Manipulation Robot Imitation Learning+1

May 3, 2026

DexSim2Real: Foundation Model-Guided Sim-to-Real Transfer for Generalizable Dexterous Manipulation

Sim-to-real transfer remains a critical bottleneck for deploying dexterous manipulation policies learned in simulation to real-world robots. Existing approaches rely on manually designed domain randomization or task-specific adaptation, limiting their generalizability across diverse manipulation scenarios. We present DexSim2Real, an integrated framework that leverages vision-language foundation models to bridge the sim-to-real gap for dexterous manipulation. Our system combines three components: (1) Foundation Model-Guided Domain Randomization (FM-DR), which uses a vision-language model as a visual realism critic to optimize simulation parameters via closed-loop CMA-ES, complementing text-based approaches like DrEureka with direct visual feedback; (2) a Tactile-Visual Cross-Attention Policy (TVCAP) that adapts cross-attention visuo-tactile fusion to zero-shot sim-to-real RL; and (3) a Progressive Skill Curriculum (PSC) that builds on LLM-based task decomposition with a difficulty scheduler tailored to contact-rich dexterous tasks. Extensive experiments on six challenging manipulation tasks with blinded evaluation demonstrate that DexSim2Real achieves a 78.2% average real-world success rate, outperforming DrEureka and DeXtreme while reducing the sim-to-real performance gap to only 8.3%.

Zijian Zeng, Fei Ding, Huiming Yang +2

Tsinghua University · Alibaba Group · Bengbu University +1

Domain Randomization Dexterous Manipulation Sim-to-Real Transfer Generalization in Robotic Manipulation Robotic RL VLMs for Robotics

Apr 30, 2026

Function-based Parametric Co-Design Optimization of Dexterous Hands

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

Mohammad Amin Mirzaee, Harsh Gupta, Wenzhen Yuan

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

Robotic Hand Design Robotic Grasping Dexterous Manipulation Robotics

Apr 28, 2026

HANDFUL: Sequential Grasp-Conditioned Dexterous Manipulation with Resource Awareness

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

Ethan Foong, Yunshuang Li, Hao Jiang +2

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

Robotic Grasping Dexterous Manipulation Robot Manipulation Benchmarks

Apr 26, 2026

MUSIC: Learning Muscle-Driven Dexterous Hand Control

We present a data-driven approach for physics-based, muscle-driven dexterous control that enables musculoskeletal hands to perform precise piano playing for novel pieces of music outside the reference dataset. Our approach combines high-frequency muscle-level control with low-frequency latent-space coordination in a hierarchical architecture. At the low level, general single-hand policies are trained via reinforcement learning to generate dynamic muscle-tendon activations while tracking trajectories from a large reference motion dataset. The resulting tracking policies are then distilled into variational autoencoder (VAE) models, yielding smooth and structured latent spaces that abstract away low-level muscle dynamics. For the high level, we train piece-specific policies to operate in this latent space, coordinating bimanual motions based on specific goals, denoted by note events extracted from given musical scores, to synthesize performances beyond the reference data. In addition, we present an enhanced musculoskeletal hand model that supports fine control of fingers for accurate low-level motion tracking and diverse high-level motion synthesis. We evaluate the control pipeline of our approach on a diverse piano repertoire spanning multiple musical styles and technical demands. Results demonstrate that our approach can synthesize coordinated bimanual motions with accurate key presses, and achieve the state-of-the-art performance of piano playing in physics-based dexterous control. We also show that our musculoskeletal hand model demonstrates superior biomechanical stability and tracking precision compared to the existing model, and validate that our musculoskeletal hand model and muscle-driven controller can generate physiologically plausible activation patterns that align with human electromyography (EMG) recordings.

Pei Xu, Yufei Ye, Shuchun Sun +3

Stanford University, USA · Clemson University, USA

Robotic Control RL Control Dexterous Manipulation

Apr 24, 2026

A Kinematic Analysis of Palm Degrees of Freedom for Enhancing Thumb Opposability in Robotic Hands

This study investigates the kinematic role of palm degrees of freedom (DoF) in enhancing thumb opposability in a five-finger robotic hand. A hand model consisting of a five DoF thumb and four fingers with three to four DoF is analyzed, where palm motion is introduced between adjacent fingers. To quantitatively evaluate thumb-finger interaction, the overlap workspace volume is defined based on voxelized fingertip reachable regions. Seven cases are considered, including configurations with increased total DoF and configurations in which the total DoF is maintained by redistributing DoF from the fingers to the palm. The results show that palm DoF significantly improves opposability, particularly for the ring and little fingers, by repositioning their base locations rather than simply extending their reachable range. However, when the total DoF is constrained, redistributing DoF to the palm leads to trade-offs between overlap workspace expansion and kinematic redundancy. These findings indicate that palm DoF and finger DoF play distinct roles in hand kinematics and should be considered jointly in design. This study provides a quantitative framework for evaluating palm-induced opposability without relying on object or contact models and offers practical design guidelines for incorporating palm motion in robotic hands.

HyoJae Kang, Yeong Jae Park, Hyunmok Jung +2

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

Robotic Hand Design Dexterous Manipulation Robotics Robotic Manipulation

Apr 23, 2026

FingerViP: Learning Real-World Dexterous Manipulation with Fingertip Visual Perception

Current dexterous manipulation systems commonly rely on external or wrist-mounted cameras for visual feedback. However, such sensing setups are prone to occlusion and can limit performance on tasks requiring close-range, multi-view perception. In this work, we design a vision-enhanced fingertip module that brings clear visual perception directly to the fingertips and present FingerViP, a learning system that leverages fingertip visual perception for dexterous manipulation. Specifically, the fingertip module integrates miniature-camera sensing within compact-footprint hardware and is installed on each finger of a multi-fingered hand, which substantially improves visual perception by providing comprehensive, multi-view observations of objects and surrounding environment. Building on the integrated fingertip modules, we develop a diffusion-based whole-body visuomotor policy conditioned on a third-view camera and multi-view fingertip vision, enabling the robot to effectively learn complex manipulation skills directly from human demonstrations. To improve view-proprioception alignment and contact awareness in the policy observation representation, each fingertip visual feature is augmented with its corresponding camera-pose encoding and per-finger joint-current encoding. We validate the effectiveness of multi-view fingertip vision and demonstrate the robustness and adaptability of FingerViP across a range of challenging real-world tasks, including retrieving objects behind an occluding curtain, achieving a

56.6\%

relative improvement in average success rate over the baseline using an external third-view camera and a wrist-mounted camera. Videos, code, and hardware designs are available on our project page: https://fingervip.github.io/.

Zhen Zhang, Weinan Wang, Hejia Sun +4

The Chinese University of Hong Kong · The Hong Kong Polytechnic University

Dexterous Manipulation Visuomotor Policy Learning Robot Imitation Learning Robotic Perception

Apr 22, 2026

FingerEye: Learning Dexterous Manipulation with Continuous Vision-Tactile Sensing

Dexterous robotic manipulation requires perception that remains informative from pre-contact approach to contact initiation and post-contact control. We introduce FingerEye, a sensing and learning framework that strengthens robotic dexterity through continuous vision-tactile feedback throughout interaction. On the sensing side, FingerEye integrates binocular RGB cameras with a compliant contact interface to support perception both before and after contact. Before contact, the fingertip cameras provide close-range visual cues and implicit stereo for precise approach and object localization. After contact, marker-tracked deformation of the compliant ring provides a proxy for contact wrench sensing. On the learning side, we build real-and-sim infrastructure for data collection and evaluation, systematically study policy-interface designs for learning with multiple FingerEye sensors, and develop FingerEye Policy, which applies group-structured modality fusion to reduce modality shortcuts and better exploit distributed fingertip feedback. Across seven contact-sensitive task settings, FingerEye improves wrist-only policy by over 30 percentage points in mean success rate in both simulation and the real world.

Zhixuan Xu, Yichen Li, Xuanye Wu +2

National University of Singapore · RoboScience · Huazhong University of Science and Technology +1

Dexterous Manipulation Robotic Tactile Sensing Multimodal Sensor Fusion

Apr 22, 2026

Kinematic Optimization of Phalanx Length Ratios in Robotic Hands Using Potential Dexterity

In the design stage of robotic hands, it is not straightforward to quantitatively evaluate the effect of phalanx length ratios on dexterity without defining specific objects or manipulation tasks. Therefore, this study presents a framework for optimizing the phalanx length ratios of a five-finger robotic hand based on potential dexterity within a kinematic structure. The proposed method employs global manipulability, workspace volume, overlap workspace volume, and fingertip sensitivity as evaluation metrics, and identifies optimal design configurations using a weighted objective function under given constraints. The reachable workspace is discretized using a voxel-based representation, and joint motions are discretized at uniform intervals for evaluation. The optimization is performed over design sets for both the thumb and the other fingers, and design combinations that do not generate overlap workspace are excluded. The results show that each phalanx does not contribute equally to the overall dexterity, and the factors influencing each phalanx are identified. In addition, it is observed that the selection of weighting coefficients does not necessarily lead to the direct maximization of individual performance metrics, due to the non-uniform distribution of evaluation measures within the design space. The proposed framework provides a systematic approach to analyze the trade-offs among reachability, dexterity, and controllability, and can serve as a practical guideline for the kinematic design of multi-fingered robotic hands.

HyoJae Kang, Joonho Lee, Jeongdo Ahn +1

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

Robotic Hand Design Mechanism Design Dexterous Manipulation Robotics

Apr 20, 2026

SpaceDex: Generalizable Dexterous Grasping in Tiered Workspaces

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

Wensheng Wang, Chuanjun Guo, Wei Wei +2

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

Robotic Grasping Dexterous Manipulation Generalization in Robotic Manipulation

Apr 20, 2026

Periodic Steady-State Control of a Handkerchief-Spinning Task Using a Parallel Anti-Parallelogram Tendon-driven Wrist

Spinning flexible objects, exemplified by traditional Chinese handkerchief performances, demands periodic steady-state motions under nonlinear dynamics with frictional contacts and boundary constraints. To address these challenges, we first design an intuitive dexterous wrist based on a parallel anti-parallelogram tendon-driven structure, which achieves 90 degrees omnidirectional rotation with low inertia and decoupled roll-pitch sensing, and implement a high-low level hierarchical control scheme. We then develop a particle-spring model of the handkerchief for control-oriented abstraction and strategy evaluation. Hardware experiments validate this framework, achieving an unfolding ratio of approximately 99% and fingertip tracking error of RMSE = 2.88 mm in high-dynamic spinning. These results demonstrate that integrating control-oriented modeling with a task-tailored dexterous wrist enables robust rest-to-steady-state transitions and precise periodic manipulation of highly flexible objects. More visualizations: https://slowly1113.github.io/icra2026-handkerchief/

Lei Liu, Haonan Zhang, Huahang Xu +8

Department of Computer Science and Technology, Tsinghua University, Beijing, China · School of Biomedical Engineering, Tsinghua University, Beijing, China · School of Artificial Intelligence, Beihang University, Beijing, China +10

Robotic Control Dexterous Manipulation Deformable Object Manipulation Robotics

Apr 19, 2026

MM-Hand: A 21-DOF Multi-modal Modular Dexterous Robotic Hand with Remote Actuation

High-DOF dexterous hands require compact actuation, rich sensing, and reliable thermal behavior, but conventional designs often occupy valuable in-hand space, increase end-effector mass, and suffer from heat accumulation near the hand. Remote tendon-driven actuation offers an alternative by relocating motors to the robot base or an external motor hub, thereby freeing the fingers and palm for additional degrees of freedom, sensing modules, and maintainable mechanical structures. This paper presents MM-Hand, a 21-DOF Multimodal Modular dexterous hand based on remote tendon-driven actuation. The hand integrates spring-return tendon-driven fingers, modular 3D-printed finger and palm structures, quick tendon connectors for maintenance, and a multimodal sensing system including joint angle sensors, tactile sensors, motor-side feedback, and in-palm stereo vision. We further analyze tendon-sheath length variation and friction loss to guide the design of the routing, motor hub, and closed-loop joint control. Experiments validate the transmission, output force, sensing, and control capability of the system. The fingertip force reaches 25N under a 1m remote sheath transmission, demonstrating practical load capacity despite long-distance tendon routing. Closed-loop joint-level experiments further evaluate command tracking with a static arm and during arm motion. These results show that MM-Hand provides a lightweight, sensor-rich, and maintainable hardware platform for dexterous manipulation research. To support the community, all hardware designs and software frameworks are made fully open-source at https://mmlab.hk/research/MM-Hand.

Zhuoheng Li, Qingquan Lin, Checheng Yu +5

The University of Hong Kong · City University of Hong Kong · XiFengGu Ltd.

Robotic Hand Design Dexterous Manipulation Robotic Manipulation Tendon-Driven Actuation

Apr 16, 2026

DEX-Mouse: A Low-cost Portable and Universal Interface with Force Feedback for Data Collection of Dexterous Robotic Hands

Data-driven dexterous hand manipulation requires large-scale, physically consistent demonstration data. Simulation and video-based methods suffer from sim-to-real gaps and retargeting problems, while MoCap glove-based teleoperation systems require per-operator calibration and lack portability, as the robot hand is typically fixed to a stationary arm. Portable alternatives improve mobility but lack cross-platform and cross-operator compatibility. We present DEX-Mouse, a portable, calibration-free hand-held teleoperation interface with integrated kinesthetic force feedback, built from commercial off-the-shelf components under USD 150. The operator-agnostic design requires no calibration or structural modification, enabling immediate deployment across diverse environments and platforms. The interface supports a configuration in which the target robot hand is mounted directly on the forearm of an operator, producing robot-aligned data. In a comparative user study across various dexterous manipulation tasks, operators using the proposed system achieved an 86.67% task completion rate under the attached configuration. Also, we found that the attached configuration reduced the perceived workload of the operators compared to spatially separated teleoperation setups across all compared interfaces. The complete hardware and software stack, including bill of materials, CAD models, and firmware, is open-sourced at https://dex-mouse.github.io/ to facilitate replication and adoption.

Joonho Koh, Haechan Jung, Nayoung Kim +2

Dept. of Artificial Intelligence, Sogang University, Korea · Dept. of Electronic Engineering, Sogang University, Korea · Dept. of Mechanical Engineering, Sogang University, Korea +1

Dexterous Manipulation Robotic Data Collection Robot Teleoperation

Apr 16, 2026

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

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

Jongbin Lim, Taeyun Ha, Seongho Cha +6

Seoul National University · RLWRLD

Robotic Grasping Dexterous Manipulation Robotic Data Collection Cross-Embodiment Robot Learning Robot Manipulation Benchmarks Human-Object Interaction

Apr 13, 2026

Micro-Dexterity in Biological Micromanipulation: Embodiment, Perception, and Control

Microscale manipulation has advanced substantially in controlled locomotion and targeted transport, yet many biomedical applications require precise and adaptive interaction with biological micro-objects. At these scales, manipulation is realized through three main classes of platforms: embodied microrobots that physically interact as mobile agents, field-mediated systems that generate contactless trapping or manipulation forces, and externally actuated end-effectors that interact through remotely driven physical tools. Unlike macroscale manipulators, these systems function in fluidic, confined, and surface-dominated environments characterized by negligible inertia, dominant interfacial forces, and soft, heterogeneous, and fragile targets. Consequently, classical assumptions of dexterous manipulation, including rigid-body contact, stable grasping, and rich proprioceptive feedback, become difficult to maintain. This review introduces micro-dexterity as a framework for analyzing biological micromanipulation through the coupled roles of embodiment, perception, and control. We examine how classical manipulation primitives, including pushing, reorientation, grasping, and cooperative manipulation, are reformulated at the microscale; compare the architectures that enable them, from contact-based micromanipulators to contactless field-mediated systems and cooperative multi-agent platforms; and review the perception and control strategies required for task execution. We identify the current dexterity gap between laboratory demonstrations and clinically relevant biological manipulation, and outline key challenges for future translation.

Kangyi Lu, Lan Wei, Zongcai Tan +1

Department of Bioengineering, Imperial-X AI Initiative, Imperial College London, London, United Kingdom

Dexterous Manipulation Robotic Manipulation Robotics

Apr 13, 2026

ViserDex: Visual Sim-to-Real for Robust Dexterous In-hand Reorientation

In-hand object reorientation requires precise estimation of the object pose to handle complex task dynamics. While RGB sensing offers rich semantic cues for pose tracking, existing solutions rely on multi-camera setups or costly ray tracing. We present a sim-to-real framework for monocular RGB in-hand reorientation that integrates 3D Gaussian Splatting (3DGS) to bridge the visual sim-to-real gap. Our key insight is performing domain randomization in the Gaussian representation space: by applying physically consistent, pre-rendering augmentations to 3D Gaussians, we generate photorealistic, randomized visual data for object pose estimation. The manipulation policy is trained using curriculum-based reinforcement learning with teacher-student distillation, enabling efficient learning of complex behaviors. Importantly, both perception and control models can be trained independently on consumer-grade hardware, eliminating the need for large compute clusters. Experiments show that the pose estimator trained with 3DGS data outperforms those trained using conventional rendering data in challenging visual environments. We validate the system on a physical multi-fingered hand equipped with an RGB camera, demonstrating robust reorientation of five diverse objects even under challenging lighting conditions. Our results highlight Gaussian splatting as a practical path for RGB-only dexterous manipulation. For videos of the hardware deployments and additional supplementary materials, please refer to the project website: https://rffr.leggedrobotics.com/works/viserdex/

Arjun Bhardwaj, Maximum Wilder-Smith, Mayank Mittal +2

ETH Zurich · NVIDIA

Robotic Manipulation Dexterous Manipulation Sim-to-Real Transfer Object Pose Estimation Domain Randomization 3D Gaussian Splatting

Apr 10, 2026

A Benchmark of Dexterity for Anthropomorphic Robotic Hands

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

Davide Liconti, Yuning Zhou, Yasunori Toshimitsu +2

Soft Robotics Lab, D-MAVT, ETH Zurich

Dexterous Manipulation Robot Manipulation Benchmarks Robotic Hand Design Robotic Grasping

Apr 8, 2026

BiDexGrasp: Coordinated Bimanual Dexterous Grasps across Object Geometries and Sizes

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

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

Sun Yat-sen University · Peking University · Tsinghua University

Robotic Grasping Dexterous Manipulation Bimanual Robotic Manipulation Synthetic Data Generation for Robotics

Apr 7, 2026

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

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

Elizaveta Semenyakina, Ivan Snegirev, Mariya Lezina +3

Intelligent Space Robotics Laboratory, Skolkovo Institute of Science and Technology

Robotic Grasping Dexterous Manipulation Force-Controlled Robotic Manipulation

Apr 5, 2026

Learning Dexterous Grasping from Sparse Taxonomy Guidance

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

Juhan Park, Taerim Yoon, Seungmin Kim +10

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

RL for Robotics Robotic Grasping Dexterous Manipulation

Mar 29, 2026

Robotic Dexterous Manipulation via Anisotropic Friction Modulation using Passive Rollers

Controlling friction at the fingertip is fundamental to dexterous manipulation, yet remains difficult to realize in robotic hands. We present the design and analysis of a robotic fingertip equipped with passive rollers that can be selectively braked or pivoted to modulate contact friction and constraint directions. When unbraked, the rollers permit unconstrained sliding of the contact point along the rolling direction; when braked, they resist motion like a conventional fingertip. The rollers are mounted on a pivoting mechanism, allowing reorientation of the constraint frame to accommodate different manipulation tasks. We develop a constraint-based model of the fingertip integrated into a parallel-jaw gripper and analyze its ability to support diverse manipulation strategies. Experiments show that the proposed design enables a wide range of dexterous actions that are conventionally challenging for robotic grippers, including sliding and pivoting within the grasp, robust adaptation to uncertain contacts, multi-object or multi-part manipulation, and interactions requiring asymmetric friction across fingers. These results demonstrate the versatility of passive roller fingertips as a low-complexity, mechanically efficient approach to friction modulation, advancing the development of more adaptable and robust robotic manipulation.

Ethan Fisk, Taeyoon Lee, Shenli Yuan

Department of Mechanical Engineering, Northeastern University, Boston, MA 02115, USA · RAI Institute, Cambridge, MA 02142, USA

Robotic Manipulation Dexterous Manipulation Robotic Hand Design Contact-Rich Robotic Manipulation In-Hand Manipulation

Mar 23, 2026

DexDrummer: In-Hand, Contact-Rich, and Long-Horizon Dexterous Robot Drumming

Performing in-hand, contact-rich, and long-horizon dexterous manipulation remains an unsolved challenge in robotics. Prior hand dexterity works have considered each of these three challenges in isolation, yet do not combine these skills into a single, complex task. To further test the capabilities of dexterity, we propose drumming as a testbed for dexterous manipulation. Drumming naturally integrates all three challenges: it involves in-hand control for stabilizing and adjusting the drumstick with the fingers, contact-rich interaction through repeated striking of the drum surface, and long-horizon coordination when switching between drums and sustaining rhythmic play. We present DexDrummer, a hierarchical object-centric bimanual drumming policy trained in simulation with sim-to-real transfer. The framework reduces the exploration difficulty of pure reinforcement learning by combining trajectory planning with residual RL corrections for fast transitions between drums. A dexterous manipulation policy handles contact-rich dynamics, guided by rewards that explicitly model both finger-stick and stick-drum interactions. In simulation, we show our policy can play two styles of music: multi-drum, bimanual songs and challenging, technical exercises that require increased dexterity. Across simulated bimanual tasks, our dexterous, reactive policy outperforms a fixed grasp policy by 1.87x across easy songs and 1.22x across hard songs F1 scores. In real-world tasks, we show song performance across a multi-drum setup. DexDrummer is able to play our training song and its extended version with an F1 score of 1.0.

Hung-Chieh Fang, Amber Xie, Jennifer Grannen +2

Stanford University

Dexterous Manipulation Contact-Rich Robotic Manipulation Long-Horizon Robotic Manipulation Bimanual Robotic Manipulation