RAPID Hand: Robust, Affordable, Perception-Integrated, Dexterous Manipulation Platform for Embodied Intelligence
Authors: Zhaoliang Wan
Zetong Bi
Zida Zhou
Hao Ren
Yiming Zeng
Yihan Li
Lu Qi
Xu Yang
Ming-Hsuan Yang
Hui Cheng
Venue: NeurIPS 2025 poster ()
Keywords: Robotics; Robotic Manipulation; Embodied Intelligence; Dexterous Manipulation; Robotic Grasping
Primary area: applications

Abstract: This paper addresses the scarcity of low-cost but high-dexterity platforms for collecting real-world multi-fingered robot manipulation data towards generalist robot autonomy. 
To achieve it, we propose the RAPID Hand, a co-optimized hardware and software platform where the compact 20-DoF hand, robust whole-hand perception, and high-DoF teleoperation interface are jointly designed.
Specifically, RAPID Hand adopts a compact and practical hand ontology and a hardware-level perception framework that stably integrates wrist-mounted vision, fingertip tactile sensing, and proprioception with sub-7 ms latency and spatial alignment. 
Collecting high-quality demonstrations on high-DoF hands is challenging, as existing teleoperation methods struggle with precision and stability on complex multi-fingered systems.
We address this by co-optimizing hand design, perception integration, and teleoperation interface through a universal actuation scheme, custom perception electronics, and two retargeting constraints. We evaluate the platform’s hardware, perception, and teleoperation interface. Training a diffusion policy on collected data shows superior performance over prior works, validating the system’s capability for reliable, high-quality data collection.
The platform is constructed from low-cost and off-the-shelf components and will be made public to ensure reproducibility and ease of adoption.

RAPID Hand: A Robust, Affordable, Perception-Integratel, Dexterous Manipulation Platform for Generalist Robot Autonomy
Zhaoliang Wan
Zetong Bi
Zida Zhou
Hao Ren'
Yiming Zeng'
Yihan Li
Lu Qi
Xu Yang*
Asug-Hsuan Yang?
Hui Cheng'
Sun Yat-sen University
2 Orbot Ltd
University of California, Merced
CASIA
https:/raapid-and.giibbbb
Ontology Design
Whole-Hand Perception
NAN
Affordable
Temporal Syno
Puch
Robust
Dexterous
Multi-modal Integration
Spatial Allgn
Figure 1: RAPID Hand is an open-source, low-cost, fully direct-driven robotic hand platform with stable integrated, synchronized, and aligned multi- -modal whole-hand perception.
Abstract
This paper addresses the scarcity of low-cost but high-dexterity platforms for col- lecting real-world multi-fingered robot manipulation data towards generalist robot autonomy. To achieve we propose the RAPID Hand, co-optimized hardware and software platform where the compact 20-DoF hand, robust whole-ham hand per- ception, and high-DoF teleoperation interface are jointly designed. Specifically, RAPID Hand adopts compact and practical hand ontology and a hardware-leve perception framework that stably integrates wrist-mounted vision, fingertip tactile sensing, and proprioception with sub-7 ms latency and spatial alignment. Collecting high-quality demonstrations on high-DoF hands challenging, as existing teleop- eration methods struggle with precision and stability complex multi-fingered systems. We address this by co-optimizing hand design, perception integration, and teleoperation interface through a universal actuation scheme, custom perception electronics, and two retargeting constraints. We evaluate the platform's 's hardware, perception, and teleoperation interface. Training diffusion policy collected data shows superior performance over prior works [1, 2], validating the system's 's capability for reliable, high-quality data collection. The platform is constructed from low-cost and off-the-shelf components and will be made public to ensure reproducibility and ease of adoption.
"Conresoondence to: chengh9 sys..ducn
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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RAPID Hand
LEAP Hand
Allegro Hand
Figure 2: Finger Size & Kinematies Comparison. Comparison of RAPID, LEAP, and Allegro in finger thickness, MCP joint configurations, and dexterity, illustrated with representative finger poses.
1 Introduction
Dexterous manipulation [3-10] is essential for generalist robot autonomy, benefiting various applica- tions such household cleaning [11-13] and assistive service [14-17]. Specifically, utilizing large pre-trained vision-language models (VLMs) on high-quality robot-action data [18-24], is emerging as promising direction for embodied reasoning and general-purpose manipulation. Despite significant progress in model architectures and data curation strategies in such methods, this field still faces two major issues due limitations in existing hardware platforms used for data collection and policy deployment in general manipulation tasks.
First, due to the limited accessibility of advanced end-effecctrs. a common practice is use two- finger parallel grippers, which restricts dexterity in tasks requiring complex fine-grained control, such as in-hand manipulation, tool use [25]. Second, existing multi-finger hardware platforms often focus on mechanical design while overlooking sensor consistency and data integrity. This is observed by a concurrent study [26] that reports 4.4% dropout rate and latency of 15-100ms during multi-sensor integration. Both aspects hinder the diversity of manipulation skills and the collection of high-quality real-world demonstration data for generalist robot autonomy.
Collecting high-quality real-world robot demonstrations is challenging due to the lack of a compact and affordable high-Dot -oF hand system for teleoperated manipulation. There two reasons. On one hand, designing an appropriate actuation and transmission requires elaborate design for motor layout that should balance robustness, low cost, sufficient fingertip force, dexterity, and the risk of bulky structures and unnatural MCP kinematics. On the other hand, finger motions can introduce sensor interference and dropouts, and variable latencies within and across modalities [27]. One question raised: can we establish a well-structured hand ontology within a seamlessly integrated hardware-soffwware platform for reliable and high-quality collection?
Then, we build the dexterous manipulation platform from both hardware and software perspectives, ensuring that both components developed in consistent manner. For the hardware design. RAPID Hand adopts a compact 20-DoF hand ontology with a universal nulti-phalangeal actuation scheme, achieving 20 mm finger thickness through optimized motor layout (Fig. 2). Specifically, this scheme uses direct-drive for distal joints and parallel mechanisms for proximal joints, enabling efficient and independent multi-phalange to stably integrate woist-mounted vision, fingertip tactile sensing, and proprioception with precise synchronization (Fig. 5). In software development, own high-DoF teleoperation interface, enabling efficient collection of diverse demonstrations across contact-rich tasks. Last, we propose RAPID Hand co-optimized hardware-soff software platform, where the compact 20-DoF hand, robust whole-hand perception integration, and high-DoF teleoperation interface are jointly designed to close the loop from data collection policy deployment, ensuring durable hardware, stable perception, and efficient, high-quality demonstration collection for dexterous manipulation.
control. Moreover, propose hardware-level perception framework
Built upon our dexterous manipulation platform, we validate RAPID Hand by training conditioned diffusion model on three challenging in-hand manipulation tasks. In our extensive experiments, RAPID achieves superior manipulation performance and policy stability (Section 5.3) over prior and concurrent works [1, 2]. To our knowledge, RAPID Hand outperforms ex- isting hands [28, 29] in both ontology design and multimodal perception integration, while remaining low cost and accessible (for more hand comparisons, see Table 2 in Appendix A.
the method trained
The main contributions of this work are follows:

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A compact and practical hand ontology for dexterous manipulation. We design a fully actuated 20-DoF robotic hand with parallel MCP joints and natural human-likk kinematics (see Fig. 2). Through extensive prototyping and careful optimization of motor layout and wiring, the finger thickness can be reduced to just 20 mm. This significantly thinner than previous designs, such as LEAP (59 mm), while improving the robustness of the MCP joint.
A hardware-level multimodal integration, synchronization, alignment framework. To learn contact-rich manipulation, we develop a perception framework that ensures robust multi-sensor integration, precise temporal synchronization, and spatial alignment across both intra- and inter- modalitees.. This design prevents sensor interference, dropouts, and variable latencies during finger movement, substantially improving the stability and reproducibility of the policy.
Learning manipulation skills from whole-hand multimodal perception. Using a cus- tomized high-DoF teleoperation interface, we collect high-quality demonstrations and relax simplified assumptions (e.g., fixed arms, table support) used in prior methods [1] and tackle more challenging tasks such as in-hand translation and rolling, leveraging multiple modali- ties including vision, touch, and proprioception. In multifinger retrieval, our policy shows significant improvement over concurrent work [2], which relies on single-finge sweeping and ArUco markers for perception.
An open and accessible platform for generalist robot autonomy. RAPID Hand is built from inexpensive, standard, and 3D-printed components in modular design for easy repair and replacement. We will publicly make all hardware, software, and training pipelines available support scalable and reproducible research. Unlike closed-source systems like TRX-Hand 5 [27], we prioritize accessibility by balancing affordability and functionality with pass-produced components.
2 Related Work
Dexterous Manipulation for Generalist Autonomy. Dexterous manipulation is key capability for enabling generalist robot autonomy. However, current frameworks such as RT-2 [18], GR-2 [7]. and mo.s primarily employ two-finger grippers or low-DoF hands, including GROOT-N1 and
Gemini Robotics [23], largely due to the high costs and maintenance demands of more advanced end-effectors. This hardware constraint limits manipulation capabilities to gripper tasks, restricting adaptability in fine- eenigrdined skills such as in-hand manipulation, tool use, and coordinated multi-finger actions [25]. The scarcity of real-world demonstrations in such complex scenarios further underscores the need for accessible, high-DoF platforms that support contact-rich skill acquisition.
Hardware Platforms for Multi-fingered Manipulation. Existing multi-fingered hands, including tendon-driven (e.g.. Shadow Hand [30], Faive Hand [31]), direct-driven (c.g., Allegro Hand [29]. LEAP Hand [28]), and linkage-driven (e.g., Inspire Hand [32], Ability Hand [33]) designs, face inherent trade-offs among dexterity, robustness, and maintainability. More advanced platforms such as TRX Hand [34, 27] and DLR Hand [35] offer advanced capabilities but remain costly and are for internal use only, limiting broader deployment (see Table 2). Similarly, while various tactile sensing technologies, including piezoresistive [34, 36), optical [37, 38], and magnetic [39] sensors, enable fine-grained contact feedback, they often require stable integration and suffer from wear and tear. These factors constrain the scalability of current platforms for real-world dexterous manipulation, pointing to the need for affordable, robust hands with seamless multimodal sensing integration.
Learning Dexterous Skills from Teleoperation. Teleoperation remains a common strategy for collecting robot demonstration data [22]. yet applying it to multi-fingered dexterous tasks remains challenging due to the human-robot embodiment gap. Most telcoperation frameworks, whether vision-based [40-42] or vision-touch integrated [25, 15, 43], are built around low-DoF hands with generic retargeting methods [44], limiting their effectiveness in capturing complex, contact-rich behaviors. Tasks such as in-hand translation and rotation are particularly difficult, as these interfaces often result in unstable grasps and object failures. Methods like TILDE [1] attempt to simplify the problem via constrained arm motion and tabletop setups, but these constraints limit applicability to free-space dexterous manipulation. Overcoming these issues requires teleoperation interfaces and platforms that directly address the challenges of high-DoF retargeting, perception integration, and stable data collection in complex manipulation scenarios.
3

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Bavel gear (Fived with PP)
MCP!
DiP
S0N4
joint
MCP1/PPto))
Nonollami Banger
PiP Moter
MCPjooint
MCP2
SEMZ
MCP Motor
MCP Hows
MCP Motor
CMCZ
CMC7
DIP joint
TOSMY
Plumab Buger
DiP Motor
PVP jiPjovint
CMCT
MCP Motar
CMCC
ISBMT
Bevel goar (Flesd with MCCP)
Figure 3: RAPID Hand with universal multi- actuation scheme. The DIP and PIP joints of the non-thumb fingers, and the DIP and MCP joints of the thumb, are directly driven by segment-mounde mounted motors. The MCP joints of the non-thumb fingers and the CMC joint of the thumb are actuated via parallel mechanisms, enabling independent control.
3 RAPID Hand Platform
We present RAPID Hand, cost-effective humanoid hand designed for generalist robot autonomy. The design focuses on two aspects: hand ontology, balancing robustness, affordability.. fingertip force, and dexterity; and whole-hand perception framework, integrating multimodal sensors with spatial alignment and temporal synchronization. Additional hand evaluation and analysis details can be found in the supplementary material.
3.1 RAPID Hand Ontology
Hand Kinematics. The ontology design emphasizes anthropomorphic dexterity, enabling seamless interaction with household objects and tools crafted for human use. To achieve this, require ours to imitate the natural appearance and intricate kinematics of the human hand that usually has 20 to 22 DoFs. The human hand has five fingers, each with specific joint structures [45]. The thumb includes interphalangeal (IP), metacarpophallangall (MCP), and craezzoid-metaanee other fingers contain distal (DIP), proximal (PIP), and MCP joints. To replicate the ball-and-socket structure of the MCP and TM joints, we replace each with two hinge joints, with the remaining joints simplified single-axis hinge types (Fig. 15). The design of the RAPID Hand incorporates 20 motors, four for each finger to allow for precise control over complex movements. A kinematic comparison between the RAPID Hand, LEAP Hand, and Allegro Hand is presented in Fig.
(TM) joints, while the
Hand Ontology Design. In addition to human-like dexterity, the design of our finger ontology emphasizes robustness, affordability, and sufficient fingertip force. To meet these requirements, introduce a universal multi-phalangeal actuation scheme for the fingers. Cost-effectiveness is key consideration, which necessitates using commercially available servo motors instead of custom brushless motors. However, this choice presents challenges due to the larger size of these motors. Building on the foundation of cost-effectivenness, the multi-phalangeal structure aims to achieve dexterity by allowing four DoFs per finger. Achieving this while maintaining anthropomorphic kinematics and appearance challenging when using off-the-shelf servo motors drive the joints. For instance, the MCP joint, which functions as a ball joint, can be approximated with two DoFs: abduction/adduuete and flexion/extension. While some designs (e.g.. [29, 28)) use separate motors positioned in various orientations to achieve these DoFs, they often compromise the natural kinematics and appearance of the hand. Our earlier prototype [46] explores a fully actuated high-DoF design with all actuators placed in the palm, which makes the palm bulky and weakens fingertip force.

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Jestle
Tectile Sensor
LINE
Motor
M0rg
Yrs,
Paim
Rysc
Tiund)
Server
Wrist Camera
Custom Poreeption Intogration Electroncc
Whole Sensora Layout
Wine & Electrnnic Routing
Figure 4: Whole-hand perception framework including vision, tactile, and proprioceptive sensor layout with electronics and wiring routing.
To tackle this challenge, we implement bevel gear differential mechanism in the MCP using off-the-shelf servo motors. We design a spur-bevel gear module (SBM), which integrates a spur gear and bevel gear, replacing traditional belt-driven system with gear-based system. As illustrated in Fig. 3, two motors (MCP-1, MCP-2) drive SBM-1 and SBM-2 through parallel shaft gear sets. These two modules engage with the bevel gear fixed on the proximal phalanx (PP), allowing the rotation of the motors to dictate the movement of the MCP joint. The motion control in the MCP joint can be broken down into three categories:
Flexion Beexion/Extene EExtension::: When SBM-1 and SBM-2 rotate at the same speed and direction, the MCP joint rotates along the MCP-1 axis (pitch), causing the finger flex extend.
Abduction on/ Adduction: When SBM-1 and SBM-2 rotate at the same speed but in opposite directions, the MCP joint rotates along the MCP-2 axis (yaw), enabling the finger to move side-to-side.
Combined Motion: By coordinating the rotations SBM-1 and SBM-2 that control both axes of the joint, the MCP joint can simultaneously flex, extend, and move side-to-side.
The differential mechanism allows for the control of single joint with multiple DoFs, effectively simulating the flexibility of the MCP joints in the human hand. It also enhances the robustness of the MCP joint (see further details in Appendix A.), minimizes the space required compared to belt-driven systems, and eliminates issues related to belt-tension, resulting in more compact and reliable finger mechanism. To ensure adequate fingertip force and robust performance, we embed the same motors for the PIP and DIP joints within the proximal and intermediate phalanges, respectively, This configuration enables each joint to operate independently, improving both force output and the overall durability of the system.
This design is compatible with non- non-thumb fingers and requires only minor adjustments for the thumb. A differential mechanism is employed in the CMC joint for the thumb finger. Two thumb spur-bevel gear modules (TSBM) mesh with bevel gear that fixed to the thumb finger, allowing the CMC joint to rotate along the CMC-1 axis (pitch) and the CMC-2 axis (roll). This arrangement of DoFs effectively simulates the movement of the CMC joint in the human palm.
3.2 Whole-Hand Perception Framework
Multi-modal Perception Integration. Integrating stable whole-hand perception into high-DoF hands presents practical challenges, particularly as finger movements can cause wiring interference and sensor dropouts. Existing solutions, such as the hands described in [27, 34, 47], provide whole- hand tactile coverage but suffer from high complexity, closed-source design, and prohibitive costs. Optical tactile sensors [43] offer higher resolution at lower cost but are prone uniformity issues and degradation of the gel layer over time. Similarly, systems like the Ability Hand [25] integrate wrist vision and fingertip FSR sensors, yet the tactile feedback lacks the sensitivity and consistency
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Hard Synchronization
Signal syme
within 7 ms
Vision Lateney
Trigger camera expo re
Touch
Obs Lic
Prop
Capture tactile readings
Ohe
Lice
(a) Multi-modal Integration
(b) Temporal Synchronization
(c) Spatial Allinment
Figure 5: Whole- Multimodal Perception Alignment. (a) RAPID Hand observation collects a sequence of unsynchronized data, including RGB images, tactile signals, and 20 joint angles with inconsistent latencies. (b) We temporally synchronize the various observation streams using hard-syne framework. (c) The results are sequence of temporally synchronized and spatially aligned whole-hand multimodal perceptions.
required for reliable manipulation data collection. These limitations highlight the need for a more practical, scalable approach to perception integration.
To address these challenges, the RAPID Hand adopts an economical yet robust scheme combining wrist-mounted vision and fingertip tactile sensing (Fig. center). Specifically, we integrate an RGBD camera (Orbbec[48)) and flat piezoresistive tactile sensors (Matrix [49]) at the fingertips, providing global in-hand observation and localized contact feedback. The tactile sensors, offering 96 taxels per fingertip, achieve sufficient sensitivity, resolution, and long-term durability for dexterous manipulation tasks, while remaining affordable and accessible for the broader community. To ensure stable and synchronized multi-modal data, we develop custom electronics (Fig. 4 left), supporting reliable integration of vision, touch, and proprioception streams. Wire and electronics routing are carefully optimized (Fig. 4 right) to minimize protrusions and avoid restricting finger motion, while improving modularity, ease of maintenance, and system robustness during extended operation. Through these design decisions, RAPID Hand achieves balance between affordability, durability, and whole-hand perception capability, providing a scalable alternative to costly, closed-source systems and supporting robust dexterous manipulation research.
Hardware Temporal Synchronization. Beyond integrating multiple sensors into the hand, syn- chronizing their signals temporally is essential for embodied manipulation tasks, particularly when training policies from demonstration data. Inconsistent latencies across or within modalities can degrade data quality, necessitating precise synchronization to ensure reliability. Previous research [13] tackles this issue with software-based synchronization (soft-syncing vision and proprioception), which performs well for in-the-wild data collection. However, this approach may be less robust in teleoperation or sim-to-real settings. contrast, hardware solutions like those described [34] achieve precise spatial alignment for in-hand perception but depend stationary setup for temporal synchronization, which limits their effectiveness in dynamic manipulation scenarios.
Our system addresses these challenges through hard-syne framework integrated into our custom prrception-integratie electronics system. Control commands for the 20 motors are dispatched through the main controller, while a dedicated sync module is simultaneously:
* Captures fingertip tactile readings via PC (within < 7 ms).
* Triggers wrist camera exposure via PWM (within < 2 ms).
This tightly coordinated approach ensures that multimodal data streams are aligned within 7 ms, facilitating reliable real-time data collection (Fig. 5 (b)) at a rate of Hz-a key requirement for dynamic in-hand manipulation tasks.
In-Hand Spatial Alignment. The spatial alignment of multimodal sensor data (Fig. 5 (c)) is essential to complement temporal synchronization, ensuring coherent perception for in-hand manipulation.
Proprioception: The joint angles for all 20 DoF's are calibrated sub-degree precision, thanks to robust mechanical design (refer to the joint accuracy analysis in 3.)..

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Fingertip tactile arrays capture local 3D contact geometry, which is critical when external cameras are occluded. Forward kinematics transforms these signals into a "local touch point cloud" using calibrated joint states and taxel positions.
Touch:
Vision: A wrist-mounted camera provides global object context. Tactile and visual data are aligned to the vision coordinates by calibrating the camera's intrinsic and extrinsic and aligning taxel positions to the hand's kinematic model.
Unlike previous research on tactile-based manipulation that relies solely on raw tactile readings [47], our approach generates spatially aligned touch point clouds that provide direct geometric correspondence. As shown in Fig. I, all fingertip taxels map precisely to the hand's frame, resulting in synergy between vision, touch, and proprioception, similar to human manipulation. This integrated perception framework allows for reliable whole-hand perception of objects, even under occlusion.
4 Learning Dexterous Skills
To validate the RAPID Hand platform and the collected data, we train a whole-hand visuotactile policy three challenging in-hand manipulation tasks: object-in-hand translation, rolling, and multi-fingered nonprehensile retrieval.
4.1 High-DoF Teleoperation Interface
We first develop high-DoF teleoperation interface (Fig. 20), where human hand poses are captured using an Apple Vision Pro and retargeted in real time to a UR10e arm equipped with RAPID Hand. During demonstrations. we record synchronized in-hand RGB images, tactile readings, joint angles, and taxel spatial positions via RAPID Hand's perception framework.
A key challenge in teleoperating high-DoF end-effectors is bridging the embodiment gap between human and robotic hands. Existing methods commonly approximate the robot hand by uniformly scaling the human hand skeleton, but this suffers from two critical limitations. First, uniform scaling introduces geometric distortions, as it fails to account for mismatched link lengths and joint limits between the human and robot hands. Second, by neglecting inter-finger coupling, these methods often produce functionally inconsistent motions, where essential synergies, such as finger pinch closures. are poorly replicated.
To address these challenges, we introduce two additional constraints into the retargeting optimization: conformal-alignedl constraint that enforces local geometric alignment, and contact-aware coupling constraint that adaptively reinforces inter-finger coordination during critical interactions. Specifically, we apply per-phalangeal segment geometric calibration to align each human keypoint with its corresponding robot link, thereby reducing local kinematic discrepancies and mitigating global-scale distortions. To improve contact consistency during manipulation, we further introduce an interaction- aware thumb-finger coupling term, whose influence increases smoothly as the fingertips approach each other. Finally, lightweight temporal smoothing prior is incorporated to suppress residual jitter while preserving system responsiveness. Together, these constraints enable spatially accurate, contact-consistedt and temporally stable retargeting without requiring manual parameter tuning:
min A -I -u((t)((tt)=(((tt)) g(t q(t-))))
o(e) (U)EK
IET
Conformal-aligned Constraint
Contact-aware Coupling Constraint
Temporal Smoothness
where q(t) E R represents the joint angle vector of the RAPID Hand at time t. See Appendix A.2.1 for detailed method description and symbol explanation.
4.2 Whole-Hand Visuotactile Policy
Based on the collected demonstrations, we train a whole-hand visuotactile policy using diffusion- based generative model [50] (Fig. 19). The policy takes as input wrist images, fingertip tactile readings, and proprioception, all temporally synchronized and spatially aligned through perception
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framework (Fig. 5). Taxel readings are embedded together with their spatial positions; vision and proprioception are processed via pre-trained encoders and MLPs. These modalities are fused into a unified representation, enabling the policy to predict future 26-DoF joint trajectories for both hand and arm. We train the policy for 300 epochs on collected data and deploy at 10 Hz.
5 Experimental Results
We comprehensively evaluate the RAPID Hand platform to validate its hardware robustness, whole- hand perception consistency, and effectiveness in supporting dexterous manipulation learning. We further conduct ablation studies to analyze the impact of perception integration on policy performance and robustness under sensor dropouts and latency.
5.1 Hardware Platform Evaluation
Proprioceptive and Tactile Performance. We first assess the proprioceptive performance of the RAPID Hand under both unloaded and loaded conditions. Sinusoidal tracking tests on the index and thumb joints confirm stable position tracking without performance degradation or overheating during continuous operation (Fig. 8). Under load, extending beyond LEAP's s MCP joint error test with 25g weight [28], we apply 100g and 200g weights to the middle finger and measure MCP joint positional errors. The results suggest the system maintains acceptable precision even under external forces (Fig. 9). Additionally, the parallel MCP joint design improves load tolerance compared to the LEAP Hand, potentially enhancing safety and robustness in contact-rich tasks. These benefits can be attributed to the universal multi-phalangeal actuation scheme and the optimized motor arrangement (Fig. 11).
For tactile sensing, we evaluate sensitivity and consistency using a three-axis calibration stage (Fig. 10). Sensitivity tests indicate the sensors can detect forces as low 0.39g (bare sensor) and 0.59g (with protective cover). For consistency, a 3kg distributed load across 15 sensors shows deviations between -4% and 8%, suggesting sufficient uniformity for reliable manipulation.
Hand Specifications and Dexterity Comparison. The RAPID Hand delivers up to 7N fingertip force, which is sufficient for most dexterous manipulation tasks [27]. Its overall size is comparable to LEAP and Allegro Hands, with the exception of the additional pinky finger (Fig. 13). The system weighs 1.148 kg, slightly heavier than Allegro (1.08 kg) and LEAP (0.748 kg) due to the added pinky and integrated sensors. Similar to LEAP and Allegro, most joints are directly driven, while the MCP joints adopt a gear mechanism with an efficiency of approximately 96-98%.
We evaluate quantitative dexterity using standard metrics, including thumb opposability (Table Fig. 14) and manipulability (Table 4). Given the similar hand sizes, these metrics allow a fair comparison. The RAPID Hand achieves a balanced trade-off between human-like kinematics and dexterity, supporting more natural retargeting and hand poses (Fig. 16).
For qualitative dexterity, RAPID Hand successfully replicates all 33 grasp types in the Feix tax- onomy [51], demonstrating its versatility in power, intermediate, and precision grasps (Fig. 22). We further compare in-hand translation tasks using the teleoperation method from [44]. As shown in Fig. 17, RAPID Hand enables more natural lateral finger motions, while Allegro easily drops objects, and LEAP shows minimal motion. These results illustrate RAPID Hand's capability to support human-like grasping and manipulation behaviors, facilitating more efficient collection of robot demonstrations.
5.2 Software Interface Evaluation
Qualitative Evaluation of Retargeting Optimization. We visualize the retargeting results in Fig. 18 and Fig. 23 to qualitatively assess retargeting constraints. In Fig. 18, we visualize the retargeting process step by step. Column (a) shows the human hand performing the target gestures. Colunm (b) presents the corresponding 3D keypoints and pose estimated by MediaPipe [52]. In column (c), applying our conformal alignment constraint corrects these distortions, producing geometrically consistent human hand poses. Column (d) illustrates the resulting retargeted robot poses optimized by our method, while column (e) shows the RAPID Hand executing these poses, closely matching the intended geometry without visible distortions. Fig. 23 further compares our method with uniform

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(a) In-hand Rolling
Fallure Cases
(b) In-hand Translation
(c) Multi-lingered Non-prehensile Retrieval
kamprehenaile
Crasping
Figure 6: Dexterous Manipulation Performance.
scaling baseline across four scale factors a. None of the a values achieves consistent alignment across all gestures: smaller scales under-extend longer fingers, while larger scales over-extend distal links and still fail to close thumb fonbb-innger gaps. In contrast, our method, using a single parameter set, accurately reconstructs all poses with correct link lengths and reliable thumb-finger contact, demonstrating the effectiveness of our local geometric alignment and contact-aware coupling.
5.3 Dexterous Skills Evaluation
Policy Learning Performance. Using the high-DoF teleoperation interface, we collect demonstra- tions and train whole-hand visuotactile policy on three challenging tasks: translation, rolling, and non-prehensile retrieval. The policy achieves near near-perfect success rates rolling and translation tasks, while retrieval remains challenging due to longer sequences and complex interactions (Table I). Notably, compared to prior and concurrent works, platform relaxes common assumptions such as fixed arms or table support [1], enabling more generalizable in-hand rolling and translation. For retrieval, multifinger policy substantially outperforms concurrent methods [2], which rely single-finges sweeping and ArUco- based perception. Ablation studies further highlight the comple- mentary roles of vision, touch, and proprioception across tasks. Removing any modality notably degrades performance, particularly in scenarios requiring fine contact adjustments.
Robustness under Delays and Dropouts. We further introduce controlled perception delays and random dropouts during policy execution. As shown in Fig. and Table 1, stable perception integration and synchronization significantly enhance policy stability and reduce action errors, confirming the effectiveness of the RAPID Hand platform in ensuring robust policy deployment.
Generalization to Unseen Objects. We test the learned policy on unseen objects such as comn, wine bottles, and chip bags. The policy generalizes well without task- specific retraining, demonstrating the benefits of reliable whole-hand perception and consistent data collection pipelines (Fig. 21).
Task
Rolling
Translation
Retrieval
Action HSE:
w.O. Vision
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44/50 50
41/.0
23/.50
0.a
Ballog
Mansiacion
Table Success Rate on the Three Manipulation Tasks
Figure Action MSE w./woo. latency.

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Conclusion
This work presents RAPID Hand, an affordable and parception-integraael 20-DoF robotic hand de- signed to facilitate high-quality data collection for dexterous manipulation. Through co-optimization of hand ontology, perception integration, and teleoperation interface, we address key challenges in controlling high-DoF hands and collecting reliable multi-modal data. Experiments on in-hand manipulation tasks demonstrate the platform's effectiveness in supporting policy learning, with improved performance and stability over prior systems. While promising, the current design remains constrained by servo motor size and lacks direct haptic feedback. Future work will explore more compact actuators and enhanced telcoperation interfaces to further improve usability and precision.
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Table 2: Comparison of Multi-Fingered Hands. An overview of widely used multi-fingered hands in recent dexterous manipulation research, detailing their finger count, in-hand perception capabilities, actuation types, and other specifications, including cost, DoF's and alignment and synchronization availability. The symbols v and - indicate the presence or absence of a modality or feature, respectively, while denotes unclear.
Finger In-hand Perception
Types of Actuation
Other Specifications
Hands
Num.
Vision Touch Prop. Tendon Direct linkage Cost (USD) DoEs Align or Sync
Barrett Hand [53]
50,000
TRX Hand [34]
3
8
ByeSight Hand [43]
v
v
v 2,500
Allegro Hand [29]
4
v
v 16,000
16
LEAP Hand [28]
v
2,000
16
DLR Hand 11 [35]
Delta Hand [1]
V
1,000
12
Shadow Hand [30]
300,000
TRX Hand [27]
13
Faive Hand [31]
11
Inspire Hand [32]
5,000
Ability Hand [33]
20,000
RAPID Hand
3,500
20
A
Appendix
A.1
Hand Analysis
A.1.1
Robustness
Reliability. The RAPID Hand employs current-based position control for precise joint actuation. As shown in Fig. 8, sinusoidal input tests for both the index finger and thumb joints demonstrate stable positional tracking with minimal deviation. Throughout repeated trials, the hand consistently maintains its accuracy without performance degradation overheating. This reliability is attributed to the universal multi-phalangeal actuation scheme and the optimized arrangement of the motor, wiring, and electronics. These features facilitate extensive real-world data collection and effective policy deployment, demonstrating the robustness of the ontology design.
Fingertip Force. We measured fingertip force by having the RAPID Hand apply pressure with its index finger onto a 6D) force sensor, which recorded measurements of up to 7 N for the index finger. Given that most daily items weigh less than kg [54], this force is sufficient for most tasks requiring dexterous manipulation. Additionally, pull-push tests demonstrate that the parallel MCP joint design supports load capacity 2.3 times greater than that of the LEAP Hand's tandem configuration, ensuring robustness during forceful interactions.
A.1.2 Affordability
Fabrication Costs.
The RAPID Hand offers a cost-effective platform for researchers in dexterous manipulation. Its primary components-off -oaeeenttttt-tt----hhhheeee motors and sensors, 3D-printed parts, a main controller, and other electronics-tote approximately $3,500. The majority of this expense comes from the 20 DYNAMIXEL servo motors, which are commonly used in previous studies [28, 31, 55, 56]. For researchers who already possess LEAP Hands, upgrades primarily require just four additional motors ($360), significantly lowering costs. This cost efficiency, combined with the system's 's modularity, makes the RAPID Hand an accessible platform for scalable research in dexterous manipulation.
Maintenance. The RAPID Hand features an open-source design optimized for self melf-maintenanee which addresses the challenges of long-term use, such as inevitable wear and tear on motors and sensors. Its modular multi-phalangeal architecture allows researchers to easily replace or repair components without relying on factory services, thereby reducing downtime and costs-common limitations of commercial robotic hands. This capability for self- maf--mainte is particularly valuable
15

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(a) Index
2.3
00
1-21
Thumb
Thar redibeooedd
Thw wwl.beroodd
Tier end Eencmntt
C2OC
CHC-T
111
01.
033
Derten (alle)
Detta Cadaaal
-33
10.1
250
240
E30
650
800
4.59
12:00
250
830
Tha
Figure 8: Accuracy of the index and thumb finger.
MCP-Side Performance under Loads
Conerol Command
Position (rad)
0.2
enet 200g -0 10
Max emtrr 201g 0.08
Max eeror 0g:-2.00
100g Load
10000
Time (ms)
MCP-Forward Performance under Loads
Conrel Command
Position (rad)
0.2
0g land
130y and 200g Lord
0.0
-0.2
200g Load
2000
4000
Time (ms)
6000
8000
10000
Figure 9: Accuracy of the middle finger under loads.
for continuous data collection and policy deployment in embodied manipulation, where interruptions can significantly impede progress. Compared to tendon-driven or linkage-driven hands, the RAPID Hand's ' design simplifies the repair process, empowering researchers to maintain their hardware efficiently and focus on advancing their algorithms.
A.1.3 Perception-Integraate
Whole-hand Perception. The RAPID Hand provides researchers with practical and cost-effective alternative for whole-hand perception. While full-coverage tactile sensors are ideal, they remain prohibitively expensive due to the complexity of integrating multi-curved surfaces with robust tactile performance, as shown in Fig. 12. To accommodate printed circuits, large surfaces must often be partitioned into smaller, developable ones, further increasing cost and design complexity. To address this, the RAPID Hand integrates wrist-mounted vision with mass-produced flat tactile sensors on its fingertips, offering scalable and robust solution for whole-hane perception. Especially since the
16

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Response of 15 Tactile Sensors (3 kg)
100
Taxel Reading
XYZ
80
Linear stage
Sample
40
20
Median Taxe! Reading
Range 1-4% 8% Veriation)
0
2
5
9
12 13 14 15
Tactile Sensor Number
Figure 10: Tactile performance evaluation.
54
20
32
140,50
260 50
148,50
45,50
283,50
Figure 11: Optimized Motor Arrangement Design. The finger thickness is reduced 20 mm, significantly thinner than LEAP's 59 mm.
tactile sensors are inevitable to wear and tear after long-term use, the five tactile sensors ($500 in total) are affordable.
Whole-hand Perception Alignment. The RAPID Hand aligns vision, touch, and proprioception both temporally and spatially, supporting the requirements for large-scale manipulation data collection. Temporal synchronization ensures consistent and stable multi-sensor data streams, while precise calibration maintains spatial aligmment, allowing for accurate whole-hand perception.
Reliable multi-modal alignment particularly important for touch sensing, where tne-grained spatial accuracy is needed to capture local 3D contact information. While the TRX Hand [34] achieves spatial alignment by collecting stationary in-hand data, the RAPID Hand designed for real real-time synchronization and alignment in dynamic tasks, making it potentially useful tool for embodied manipulation research. The system achieves hardware-level synchronization within 7 ms latency and pixel-level spatial accuracy, ensuring consistency in perception data, helping to improve data quality, and preventing sensor dropouts occasionally or latency inconsistencies during collection.
This whole-hand multimodal perception system could also contribute to reinforcement learning (RL)-based manipulation methods [37, 57, 58], where policy training benefits from well-aligned multi-modal data. In Sim2Real transfer, precise spatiotemporal alignment is particularly crucial for tactile-based manipulation, where discrepancies between simulated and real-world feedback remain significant challenge. To help address this, the RAPID Hand includes tactile simulation environment
17

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Figure 12: Simulated and real-world hands with whole-hand perception. The figure compares tactile sensing in the four- fingered Allegro (top) and the five-fingered RAPID (bottom). The Allegro Hand integrates full-coverage tactile sensors, which are costly and may degrade over time. In contrast, the RAPID Hand offers cost-effective alternative, combining fingertip tactile sensors with wrist- mounted camera for whole-hand perception. Red points indicate 480 simulated taxels MuJoCo, with activated sensor signals and positions recorded as tactile readings.
A672
30.5)
Allegro Hand
RAPID Hand
LEAP Hand
Figure 13: Visualization of hand sizes.
Table 3: Finger-to-Thumb Opposability Volume (mm3)
Hands
Index
Middle
Ring
Pinky
Allegro Hand
320,388
265,961
107,904
LEAP Hand
989,013
829,705
534,242
RAPID Hand (Ours)
312,233
317,805
252,510
144,970
based MuJoCo [59], shown in Fig. 12. This simulation focuses accurate contact position feedback rather than replicating the non-linear force responses of individual taxels. Each taxel is modeled a force sensor, positioned based real-world 3D scans. Upon contact, the system records the activated sensor positions as tactile reading signals, using the hand's kinematics, and estimates their relative poses within the hand's reference frame. While challenges remain, the RAPID Hand's design aims to help bridge the gap between simulated and real-world tactile interactions, contributing further research in this
A.1.4 Dexterity
Quantitative Dexterity. To fairly compare the dexterity of the Allegro Hand, LEAP Hand, and RAPID Hand, we adopt the evaluation metrics used in [29, 28]: thumb opposability and manipulability. Thumb opposability(60) a key factor for in-hand manipulation, summarized in Table and visualized in Fig. 14. The LEAP Hand exhibits the highest opposability due to its MCP joint
18

---

Figure 14:
Visualization of Thumb Opposability Volume in the RAPID Hand. Blue points
indicate the possible positions within the opposability range from each non-thumb finger to the thumb of the RAPID Hand.
Table 4: Manipulability Ellipsoid Volume (mm²)
Hand/Finger Pose
Down
Up
Curled
Allegro Hand
Linear
246
48.3
2.21 x 105
Angular
0
0
0
LEAP Hand
Linear
3.03 x 103
3.03 x 10a
1.36 x 10a
Angular
1.18 103
5.23 x 105
2.50 x 105
RAPID Hand (Ours)
Linear
3.01
10
3.03 x 104
1.77 x 105
Angular
3.24
10a
20.8
2.84 x 104
placement on the second phalangeal segment. However, this design results a bulkier appearance with unnatural kinematics. The RAPID Hand, in contrast, demonstrates significant improvement over the Allegro Hand by optimizing finger design and motor arrangement, achieving better balance between dexterity and form factor. Manipulability [61] measures the hand's 's dexterity in specific poses. The manipulability ellipsoid volumes for three standard poses poses-down, up, and curled-and listed in Table The RAPID Hand demonstrates improved manipulability in most tested poses compared to the LEAP and Allegro Hands, reflecting its refined ontology design for dexterous tasks.
Qualitative Dexterity. The RAPID Hand provides more natural finger poses than the LEAP Hand when retargeting human hand movements to robotic hands. As shown in Fig. 16, direct mapping of human motions the LEAP Hand often leads collisions and unnatural finger configurations, particularly during fist closure. In contrast, the RAPID Hand achieves a more anthropomorphic fist pose, improving usability in teleoperation and imitation learning. To further assess its anthropo- morphic capabilities, we applied the Feix taxonomy [51], which classifies human hand grasp types based functionality. The RAPID Hand successfully replicates all 33 grasp types defined in the iinoommy-including power, intermediate, and precision grasps-as as illustrated in Fig. 22. These results highlight the RAPID Hand's ability to mimic human grasping strategies, making well-suited for human-to-robot motion retargeting applications.
A.1.5 Compatibility
Compatible Ontology Design. The RAPID Hand's ontology design follows a universal multi- phalangeal actuation scheme with an optimized motor arrangement, ensuring both anthropomorphic dexterity and compatibility with various motor types. the RAPID hand, 20 DYMANXIEL servo motors are used practical compromise. Although brushless motors offer greater power, their larger size, and higher cost currently limit their use in directly driven 20-DoF hands. With minimal adjustments, the design can accommodate brushless motors, making adaptable for future
19

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Fingertip
Ring
Middle
Fralex
direction
Powky
Middi
Dinul
Pitch
joint
Vaw
Middle phalans
PIP jiP
Roll
Procinal plaiane
MCP MCPjaint
sensor
CMCjoint
Pale
Thumb
Widar
Merocarpal
Prostimal
Dtimal
(Ral)))
sensor
phalanx
plasiiaa
IP joint
MCPjoint
Figure 15: Hand Kinematics. Simplified human hand kinematics (left) and RAPID Hand kinematics (right).
(a)
(b)
(c)
Figure 16: Hand Retargeting Comparison. (a) Human fist closure; (b) LEAP Hand with collisions
and unnatural finger poses using [44]; (c) Retargeted RAPID Hand with natural configuration.
advancements in compact and powerful motors. This flexibility allows significant reductions in hand size without sacrificing dexterity.
Expandable Perception Alignment. The RAPID Hand's perception alignment framework integrates multi-modal data through temporal synchronization and spatial alignment, ensuring compatibility with diverse sensor types.
The framework supports the seamless integration of various tactile sensors on the fingertips, including optical, magnetic, and capacitive-basee based sensors. Additionally, it accommodates wrist-mounted cameras, such as fisheye or stereo cameras, and allows for further expansion with additional palm or pulp-mounted sensors and external vision systems. A key feature of this framework is its hardware- level synchronization, which ensures that all sensors- across different modalities are precisely aligned in time. This prevents exposure mismatches between cameras, common issue in software- only synchronization, which can lead to data inconsistency and degraded perception accuracy. By addressing these challenges the hardware level, the RAPID Hand provides reliable and adaptable perception system for complex manipulation tasks.
A.2 Learning Dexterous Skills
To evaluate the RAPID Hand's performance and potential, we employ imitation learning on three challenging tasks: multi- multi-finggred nonprehensile retrieval, object-in-hand translation, and rolling. This section details the data collection interface, task specifications, and our whole-hand visuotactile policy.
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Figure 17: Teleoperated In-Hand Translation Comparison. Teleoperation results of in-hand object translation using (top) LEAP Hand, (middle) Allegro Hand, and (bottom) RAPID Hand. The RAPID Hand enables stable lateral translation with coordinated multi-finger motion, while LEAP and Allegro exhibit limited or unstable motions.
A.2.1 Adaptive Robot Hand Motion Retargeting
To enable accurate and generalizable teleoperation of multi-DoF robotic hand, we formulate comprehensive retargeting objective that combines conformal-aligae constraints, contact-aware coupling constraints, and temporal smoothness. This formulation builds upon the structural similarity between the human and robotic hand by first calibrating and adjusting the human keypoints to spatially match the robot's geometry. However, aligning individual fingers alone is often insufficient for faithfully capturing complex behaviors such as pinching coordinated grasping. To address this, we incorporate a multi-finger coordination term that dynamically enforces relative pose constraints based inter-finger proximity. Finally, temporal smoothness regularizer mitigates abrupt joint fluctuations, ensuring consistent and stable trajectories over time.
As shown in Eq. The full retargeting objective is expressed as:
min A a(t) (KJ)EX
El -FK (1(t) +A Σ (1) a ((((() q(t 1)|
Conformal-aligned Constraint
Contact aware Coupling Constraini
Temporal Smowttheess
where q(t) E R. represents the joint angle vector of the RAPID Hand at time t.
(i) Conformal-alignee Constraint The first term minimizes spatial discrepancies between adjusted human hand keypoints and their corresponding positions on the robotic hand, where K. C {(i, j) s i < 4,0 0 j } defines pairs of keypoints for alignment, typically fingertip and intermediate joints and U. (1) E R denotes the adjusted position of the j-th keypoint on the i-th finger of the human hand at time The forward kinematics function FK, computes the corresponding keypoint based on current joint angles qt)).
To compute the adjusted keypoints (t), first define the detected human hand keypoints follows:
V; (Wi,,, Wi.,,,...,, Wi,n. }. i .....,
(3)
where each keypoint wi R represents the position of the j-th keypoint the i-th finger, expressed in the wrist coordinate frame.
Human keypoint adjustments involve scaling each finger phalangeal segment and translating overall finger positions. Adjusted human keypoint positions, (t,(()) R3 are computed using calibration data:
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Scaling factors ri, for each finger phalangeal segment:
(+1 (9o1)
| w2,
where FKg denotes the RAPID Hand keypoint at initial configuration go. and wi is the corresponding static calibrated human keypoint.
Translational adjustments u; for each finger:
U mep ".m
(5)
where FK; mepp (go) and mep represent MCP joint positions of the robotic and human hand. Adjusted keypoints are computed as:
Wi,0,
j =
V4 =
0i 1oj-1-1%s00 +i.- - w1 + Us
(6)
Ci.j-1 -i.j wa.j-))
2 2
These adjusted keypoints preserve the relative orientations between finger phalangeal segments while scaling the overall hand geometry to match the robotic counterpart, thereby eliminating the need for introducing additional global scaling factor manually align human hand keypoints.
(ii) Contact-aware Coupling Constraint The second term ensures anthropomorphic consistency during grasping by enforcing relative positional constraints among fingers, particularly between the thumb and other fingers. This constraint is crucial for accurately capturing complex multi-finger interactions such as pinching and grasping. We first define the relative vector between the thumb and the fingertip of finger on the human hand as:
(t) Wi,tingertpp (t) Wihumb, Winunb,ingertipp (t)
where Wi, fingenmm (t) and Wthumb,fingerm fingertip (t) denote the positions of the fingertip of finger and the thumb, respectively, at time t.
Correspondingly, robotic relative vectors are calculated as:
(i(g(t)) = FK, fingertip (g((t) FKuhumb..tngeripp ((())))))))()))
(8)
To dynamically emphasize the coordination constraint based on the real-time interaction intensity between fingers, we introduce adaptive weighting factors wi))). These weights are computed by assessing the normalized distance between the thumb and each finger:
(()) [()l dmiini
dmax,i - dmin,i
where dmin R 0 corresponds to finger-thumb contact, and dmax derived from calibration data when the hand is fully extended. Consequently, d,(t) E [0, quantitatively represents the proximity of the thumb to finger i.
We apply a sigmoid function to smoothly adjust the influence of coordination:
W. 1+ -ed
(10)
where & controls the steepness and c sets the sensitivity threshold the sigmoid curve. Such adaptive modulation ensures that strong coordination constraints enforced when fingers are close (c.g.. during grasping) and significantly reduced when fingers are separated, thus preserving natural hand movements without unnecessary constraints.
(iii) Temporal Smoothness time steps:
The final term penalizes abrupt joint angle changes between successive
(1(()) - - 1)11
(11)
Typically, A, A2, A3 = to equally balance optimization components.
The optimization is solved in real-time using Sequential Least- Quadratic Programming (SLSQPP [62], providing stable and precise motion retargeting with minimal manual tunin.. Leverag- ing pre-calibrated human keypoints ensures accuracy and scalability of teleoperation.
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(a)
(b)
(c)
(d)
(e)
(a)
(b)
(c)
(d)
(e)
Figure 18: Retargeting Processing Pipeline. (a) hand images with keypoint annotations, (b) detected
keypoints using MediaPipe [52], (c) adjusted keypoints after calibration using Eq. 6, (d) corresponding
robot keypoints computed via optimization from Eq. I, and (e) rendered robot hand poses after motion
retargeting.
A.2.2 Task Specifications
This task involves repositioning objects within the grasp to correct suboptimal initial poses, which is essential for tasks such adjusting bottle's position for pouring. human operator provides a chip bottle to the robot at an arbitrary starting position. Using its five-fingered dexterity, the robot slides the bottle laterally to the lefimost edge of its grasp while maintaining secure hold. Success achieved only if the bottle reaches the target position without slipping dropping.
Object-in-Hane Translation.
Object-in-hand Rolling, fundamental skill in food preparation (e.g., peeling vegetables), this task requires the robot to continuously roll cylindrical objects like corn, zucchini, or eggplant within its grasp. The robot must adapt to object shape, texture, and hardness variations while maintaining steady rotation. Successful execution ensures the object remains in motion without slipping or stalling.
Multi-fingered Nonprehensile Retrieval. Inspired by human dexterity in cluttered spaces, this task challenges the robot to retrieve a target object (e.g., from a drawer) obstructed by surrounding items. Using nonprehensile motions, the RAPID Hand first clears obstructions with its fingers before grasping the target. A successful retrieval requires completing both stages without failure.
A.2.3 Learning Whole-Hand Visuotactile Policy
train a whole-hand visuotactile policy using collected demonstrations to infer joint-space actions (e.g., 26-DoF sequences) from multi-modal observations. The policy architecture is based on Diffusion Policy [50], generative model leveraging a time-series diffusion transformer to denoise and predict actions conditioned on historical observations, explored in recent imitation learning works [63-65].
As shown in Fig. 19, at time step t, the input observations O, = (V., P.) include:
RGB vision V. No. x610x Wrist camera images over T' timesteps.
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Multimodal Perception
N1
Whole-hand Action
Taxels FK
Angles
(MLM))
Prop, Encodor
Diflusion Transformer
Contiai moo Pcclio Diffusion
20 Joint Commands
Autoo Dacodd (HLP)
Denolsing
Steps
Mision
Diftusion Transtomer
Wrist Image
Whole-hand Action Tokons
Whole-hand Visuotactile Policy leverages whole-hand perception for whole-hand dexterity. Left: The current robot and environment state is observed via RAPID Hand's wrist camera, fingertip touch, and its 20 joint angles, converted to local touch point cloud using hand forward kinematics. Each image (pink) is represented by the class token of vision foundation model. Each tactile reading (blue) and the corresponding spatial information is embedded using our touch encoding. Twenty joint angles (orange) are embedded using proprioceptive encoder. The concatenation of each camera's 's image, touch, and proprioception tokens yields a whole-hand multimodal perception token. Right: Our whole-body visuotactile policy consumes these vision, touch tokens, proprioception, and denoising-stie tokens as conditions via cross-attention. We diffuse T whole-hand dexterity tokens (blue), each corresponding an action time step. Per time step, project the predicted dexterity token to the 20 joint angles to be achieved by the dexterity action.
Figure 19:
Follower robot
Vision Pro
Hand Poses
Human Demonstration
Figure 20: Data Collection Interface. The operator uses an Apple Vision Pro to control the robot, whose hand poses are sent to the robot in real-time as position targets. The human hand poses recorded as target actions, while the images, tactile signals, and joint angles of the robotic hand and arm are recorded observations.
Touch T, E NT x1x12xx88 Fingertip taxel (tactile pixel) readings from 5 tactile pads and the corresponding spatial position of each taxel calculated from hand's forward kinematics.
Proprioception P. TT266 Joint angles for the 20-DoF hand and the 6- 6-DoF arm.
To extract in-hand visual features, we use vision encoder initialized with pre-trained ResNet- 18. However, when objects occlude the robot's fingers during manipulation, tactile signals become critical-especiallyy as finger movements dynamically and the contact state with the in-hand object is not accessible for vision. To enhance the policy's 's spatiotemporal awareness, we compute each taxel's spatial position in the hand frame using forward kinematics and current proprioception. requiring the perception of temporal synchronization and spatial alignment (Section 3.2). RGB images are extracted by ResNet-18 18 and compressed into 128-dimensional vectors by an MLP. Taxel readings and their positions are concatenated and embedded into a 64-dimensional vector via MLPs. Proprioception is similarly projected to 192-dimensional vector. These embeddings are combined to form a 484-dimensional state representation, capturing the robot's whole-hand interaction with the environment time l.
The policy generates action sequences A, = (at+I,.... de+T,) } E TR X26 which respecify 26-DoF joint targets (20 for the hand, 6 for the arm) over T, future timesteps. During training, denoising
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iterations refine the noisy action proposals A (indexed by step k), which are processed as condi- tional inputs tokens in transformer decoder blocks. Observations O, serve as conditional inputs via multi-head cross-attention layers, enabling the decoder to align sensorimotor data with action sequences. Cross-attention [66] integrates multi-modal observations by learning latent mappings be- tween perception streams (visual, tactile, proprioceptive) and corresponding robotic actions, ensuring coordinated whole-hand manipulation. In our implementation, the policy is trained on an NVIDIA TITAN X GPU with batch size of 32. We set the observation horizon To 1, the action prediction horizon to Tр = 64, and the action execution horizon Ta 64. All policies are trained for 300 epochs and deployed 10 Hz.
A.3
Limitations
RAPID Hand still faces several practical limitations. Its overall size constrained by the use of servo motors, which, while affordable and accessible, limit further miniaturization and restrict deployment in more compact robotic systems. Additionally, the current teleoperation interface lacks direct haptic feedback. This absence reduces the operator's ' ability to perceive subtle contact forces, increasing the risk of unintended excessive force during demonstrations. Future work will focus on integrating more compact, high-performane actuators and adding haptic feedback mechanisms improve control precision, task safety, and user experience.
A.4 Societal Impact
By open-sourcing RAPID Hand, we aim to provide practical and accessible platform for dexterous manipulation research. The system's low cost and modular design allow broader adoption, enabling more researchers to explore in-hand manipulation without relying expensive proprietary hardware. We hope this will help accelerate progress in generalist robot autonomy. At the same time, care must be taken to ensure responsible use, particularly in safety-critical or human-facing applications.
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Rolling Zucchini
Rolling Cucumber
Rolling Carrot
Rolling Corn
Rolling Wine Bottle
Figure 21: Generalization Performance Visualization of In-hand Rolling.
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1. Large Diameter
2. Small Diameter
3. Medium Wrap
4. Adducted Thumb
5. Light Tool
Power Grasp
15. Fixed Hook
10. Power Disk
11. Power Sphere
17. Index Extension
Extension Type
Distal Type
Sphere 4-Finger
28. Sphere 3-Finger
30. Palmar
31. Ring
Intermediate Grasp
6. Prismatic 4-Finger
7. Prismatic 3-Finger
8. Prismatic 2- 2-Finger
9. Palmar Pinch
12. Precision Disk
Lateral Tripod
Tripod
20. Writing Tripod
22. Parallel
24. Tip Pinch
Precision Grasp
27. Quadpod
33. Inferior Pincer
Ventral
16. Lateral
21. Tripod Variation
23. Adduction Grip
25. Lateral Tripod
29. Stick
Figure 22: Grasp Taxonomy of the RAPID Hand. Grasp poses and their indices correspond the Feix taxonomy [5]].
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Figure 23: Qualitative Comparison of Retargeting Results RAPID Hand. From left to right: (1) original human hand gestures, (2-5) results of baseline method [44] using global scaling factors 1.25, 1.50, 1.75, 2.00, and (6) results from our proposed method.
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Reviews:
[{"review_id": "zHp0ihXE8r", "reviewer": "Reviewer_S1H9", "summary": "This paper presents RAPID Hand, a 20-DoF robotic hand platform that is open-source and low-cost. It integrates vision, tactile, and proprioceptive sensors, and uses a teleoperation interface for data collection. \n\n Main Contributions: \n\nDesigns a 20-DoF hand with parallel MCP joints and human-like motion. \n\nIntegrates and synchronizes vision, tactile, and proprioceptive sensors. \n\nProvides a teleoperation interface for demonstration data collection. \n\nMakes the hardware and software open-source and affordable.", "questions": "1. The tactile sensors are limited to the fingertips. Can the authors discuss how this impacts tasks involving palm contact?  \n2. The teleoperation interface not considered force feedback. Have the authors evaluated how this limitation affects quality?", "limitations": "please refer to the Weaknesses and questions", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths： \n\nOpen-source, low-cost, and easy to replicate for the research community. \n\nHardware-level integration and synchronization of vision, tactile, and proprioceptive sensing; ensures high data fidelity. \n\nWeaknesses： \n\nEvaluation is mainly on a small set of tasks; broader real-world generalization needs further study. \n\nLacks force feedback for teleoperation, reducing realism of remote control.", "was_revised": true, "final_justification": "addressed my concerns, I keep my initial rating", "rebuttal": "We thank the reviewer for the detailed reading of our paper and constructive suggestions! We hope our responses adequately address the following questions raised about our work. Please let us know if there’s anything we can clarify further.\n\n```\n1. Evaluation is mainly on a small set of tasks; broader real-world generalization needs further study. \n```\n**Reply**: For autonomous manipulation with imitation learning, most existing SOTA work [1, 2, 3] focuses on pick-and-place, while our tasks, including in-hand rolling, translation, and multi-finger retrieval, involve more complex, contact-rich manipulation. We select them as atomic skills to test stability, sensing consistency, and dexterous control, providing a practical benchmark for evaluating RAPID Hand’s 20-DoF design and synchronized multimodal sensing.\n\nFor dexterous teleoperation, a concurrent SOTA work on tool use [4] relaxes several conditions, such as placing tools in grasp friendly poses, assuming the tool is already in hand, or fixing the arm for stability. Our preliminary experiments under similar settings suggest that RAPID Hand can also support such capabilities.\n\nMore complex tasks, such as full tool use or fine assembly, would further extend the platform’s potential. Progress toward these tasks remains constrained by the difficulty of collecting safe, high-quality demonstrations, particularly without tactile feedback [5]. As a natural next step, we are exploring **RAPID Glove**, a high-**R**esolution, **A**ffordable, ha**P**tic **I**nterface for **D**emonstrating contact-rich dexterous manipulation. While this is beyond the scope of the present paper, we see it as an important direction for extending the platform’s capabilities.\n\n\n\n```\n2. The teleoperation interface lacks force feedback, reducing realism of remote control; have the authors evaluated how this limitation affects quality?\n```\n**Reply**: Tactile feedback can improve the realism and quality of teleoperation. In this work, we use a Vision Pro–based interface to ensure accessibility and ease of use. While it does not provide force feedback, it allows us to collect stable and high-quality demonstrations for all evaluated tasks.\n\nWe do not directly quantify the impact of missing force feedback, as such an evaluation would require high-resolution haptic gloves, which are currently prohibitively expensive (USD 15,000–75,000) and bulky (e.g., HaptX [6], Meta [7]). Instead, we evaluate its effect through repeated teleoperation trials and qualitative monitoring of task outcomes. Based on these observations, the absence of tactile feedback can occasionally result in excessive force, such as surface damage during rolling.\n\n\n\n\n```\n3. The tactile sensors are limited to the fingertips. Can the authors discuss how this impacts tasks involving palm contact?\n```\n**Reply**: \nIn this work, we limit tactile sensing to the fingertips due to cost and integration constraints. Full-palm coverage requires custom-shaped or curved sensors, which remain costly (e.g., ReSkin [8], Paxini [9], Xela Robotics [10], Tencent [11]). Prior work shows that palm sensing can benefit certain tasks; for example, ReSkin applied to the Allegro Hand’s palm achieved nearly 100% success in in-hand translation [12].\n\nIn our case, the evaluated tasks: rolling, translation, and multi-finger retrieval, have so far remained reliable without palm sensors. For in-hand translation, performance largely results from RAPID Hand’s natural lateral finger motion and fingertip tactile feedback, complemented by wrist-mounted vision. From a cost-effectiveness perspective, we currently refrain from deploying palm sensors, as they require complex custom designs that are expensive to implement.\n\n[1] Yang, Shiqi, et al. \"Ace: A cross-platform visual-exoskeletons system for low-cost dexterous teleoperation, 2024.\" Conference on Robot Learning (CoRL 2024).\n\n[2] Cheng, Xuxin, et al. \"Open-television: Teleoperation with immersive active visual feedback.\" Conference on Robot Learning (CoRL 2024).\n\n[3] Lin, Toru, et al. \"Learning visuotactile skills with two multifingered hands.\" 2025 IEEE International Conference on Robotics and Automation (ICRA 2025).\n\n[4] Zorin, Anya, et al. \"Ruka: Rethinking the design of humanoid hands with learning.\" Robotics: Science and Systems (RSS 2025).\n\n\n[5] Yin, Zhao-Heng, et al. \"DexterityGen: Foundation controller for unprecedented dexterity.\" Robotics: Science and Systems (RSS 2025).\n\n[6] https://g1.haptx.com/.\n\n[7] https://research.facebook.com/publications/linking-haptic-parameters-to-the-emotional-space-for-mediated-social-touch/\n\n\n[8] https://ai.meta.com/blog/reskin-a-versatile-replaceable-low-cost-skin-for-ai-research-on-tactile-perception/\n\n[9] https://paxini.com/\n\n[10] https://www.xelarobotics.com/tactile-sensors\n\n[11] Lu, Peng, et al. \"Thermoformed electronic skins for conformal tactile sensor arrays.\" 2024 IEEE International Conference on Robotics and Automation (ICRA 2024).\n\n[12] Yin, Jessica, et al. \"Learning in-hand translation using tactile skin with shear and normal force sensing.\" 2025 IEEE International Conference on Robotics and Automation (ICRA 2025)."}, {"review_id": "rBuNHHKhSA", "reviewer": "Reviewer_GKwW", "summary": "This paper presents RAPID Hand, a low-cost, 20-DoF robotic hand with integrated multimodal perception for dexterous manipulation. The platform is validated through multiple experiments. By prioritizing affordability and open sourcing the hardware, software, the authors aim to make dexterous manipulation research more accessible and scalable.", "questions": "Questions:\n1.\tIn Figure 2: Why does the paper not compare the proposed hand with the five-finger Shadow Hand? This omission appears significant.\n2.\tIn Figure 4: What is the server being used? If it is running on typical operating systems like Windows or Linux, which are not real-time systems, the reported latency values seem unconvincing.\n3.\tIn Figure 7: What does the bar represent—mean values? If so, where are the error bars or other indicators to show the distribution? Further, a significance test should be considered to support the conclusions drawn from the results.", "limitations": "Limitations:\n1.\tThe main text focuses primarily on the platform’s characterization, with limited discussion on its practical applications.\n2.\tThe unique advantages and contributions of the platform to the whole AI community are not adequately presented or verified.", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1.\tThe paper introduces a well-designed hardware platform with a high degree of freedom, which is directly and exactly actuated. \n2.\tThe integration of hard-synchronized multimodal perception enhances the efficiency and quality of data collection.\n\nWeaknesses:\n1.\tThe paper emphasizes the characterization of the hardware system but devotes much less attention to the unique capabilities and advantages that the new hardware offers. This imbalance may reduce its appeal to the broader NeurIPS audience.\n2.\tCertain critical aspects, such as system latency, are not fully validated.\n3.\tThe advancements introduced by this new hardware are not sufficiently evaluated or contextualized.", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for the detailed reading of our paper and constructive suggestions! We hope our responses adequately address the following questions raised about our work. Please let us know if there’s anything we can clarify further.\n\n```\n1. The paper emphasizes the characterization of the hardware system but devotes much less attention to the unique capabilities and advantages that the new hardware offers. This imbalance may reduce its appeal to the broader NeurIPS audience. \n```\n**Reply**: We conclude the unique capabilities of RAPID Hand as follows.\n\n**First**, the hardware is co-designed with perception and teleoperation to address practical challenges in dexterous manipulation data collection. The universal multi-phalangeal actuation scheme with a bevel gear differential achieves a compact 20 mm finger thickness while maintaining robustness, and the parallel MCP joint provides 2.3× higher load capacity than the LEAP Hand’s tandem design.\n\n**Second**, on the sensing side, hardware-level synchronization ensures sub-7 ms multimodal latency, and fingertip tactile signals are spatially aligned into local “touch point clouds,” enabling direct geometric correspondence not available in prior tactile systems.\n\n**Third**, these capabilities translate into practical benefits: in in-hand translation, RAPID Hand achieves smooth lateral finger motion, while Allegro often drops the object and LEAP shows minimal movement. Compared with prior work [1], our setup also removes simplified assumptions such as fixed end-effectors and table support, as shown in the supplementary video.\n\nWe will devote more attention to highlighting these unique capabilities in the final version.\n```\n2. Certain critical aspects, such as system latency, are not fully validated. Particularly, in Figure 4: What is the server being used? If it is running on typical operating systems like Windows or Linux, which are not real-time systems, the reported latency values seem unconvincing.\n```\n**Reply**: The sub-7 ms latency reported in Figure 4 refers specifically to multimodal synchronization, not end-to-end teleoperation delay. It measures the interval between the MCU (Figure 4 “Hard Sync”) receiving fingertip haptic data and triggering the camera exposure. This was verified using a logic analyzer.\n\nSuch low latency is achieved through hardware-level synchronization (e.g., MCU-based triggering and timestamp alignment) and is independent of the upper-level operating system. While Linux is not a real-time OS, it does not affect this measurement, as the synchronization occurs entirely on the MCU. We will clarify this definition in the final version.\n\n\n\n```\n3. In Figure 2: Why does the paper not compare the proposed hand with the five-finger Shadow Hand? This omission appears significant. \n```\n**Reply**: We compare RAPID Hand with Allegro and LEAP because they are fully actuated, direct-drive, relatively affordable, and widely adopted in recent works [3, 4, 5], making them the most relevant baselines. The Shadow Hand, while highly capable, follows a design focus different from ours, being tendon-driven and oriented toward more advanced manipulation skills. Our work emphasizes platforms suited for long-term autonomous operation and large-scale data collection, where accessibility and robustness are critical. While Shadow is not included in Figure 2, we provide a specification-level comparison in Table 2 and plan to add a size comparison in the final version.\n\n\n\n\n\n```\n4. In Figure 7: What does the bar represent—mean values? If so, where are the error bars or other indicators to show the distribution? Further, a significance test should be considered to support the conclusions drawn from the results.\n```\n**Reply**: **Mean Values and Standard Deviations** \nThe bars in Figure 7 show the mean action MSE for each condition. The mean and standard deviation for each setting are summarized below:\n\n| Task | Condition | Mean | Std |\n|-------------|---------------|-------|--------|\n| Rolling | Ours | 0.84 | 0.0239 |\n| Rolling | w. latency | 0.93 | 0.0277 |\n| Translation | Ours | 0.98 | 0.0809 |\n| Translation | w. latency | 1.16 | 0.1152 |\n| Retrieval | Ours | 0.51 | 0.0523 |\n| Retrieval | w. latency | 0.56 | 0.0554 |\n\n**Statistical Significance** \nWe conducted two-sample t-tests for each task, and all differences between \"Ours\" and \"w. latency\" are statistically significant (p < 0.001).\n\n\n[1] Si, Zilin, et al. \"Tilde: Teleoperation for dexterous in-hand manipulation learning with a deltahand.\" Robotics: Science and Systems (RSS 2024).\n\n[2] Bai, Fengshuo, et al. \"Retrieval dexterity: Efficient object retrieval in clutters with dexterous hand.\" arXiv preprint arXiv:2502.18423 (2025).\n\n[3] Qi, Haozhi, et al. \"In-hand object rotation via rapid motor adaptation.\" Conference on Robot Learning (CoRL 2023).\n\n[4] Suresh, Sudharshan, et al. \"NeuralFeels with neural fields: Visuotactile perception for in-hand manipulation.\" Science Robotics (2024)\n\n[5] Zhang, Jialiang, et al. \"Dexgraspnet 2.0: Learning generative dexterous grasping in large-scale synthetic cluttered scenes.\" 8th Annual Conference on Robot Learning. (CoRL 2024)."}, {"review_id": "kUUkMsye7q", "reviewer": "Reviewer_CUJk", "summary": "This paper presents RAPID Hand, a 20-DoF robotic hand platform designed for collecting high-quality demonstration data for dexterous manipulation. The key contributions include: (1) a compact hand ontology with universal multi-phalangeal actuation achieving 20mm finger thickness, (2) a hardware-level perception framework integrating wrist vision, fingertip tactile sensing, and proprioception with sub-7ms synchronization, and (3) a high-DoF teleoperation interface with conformal-aligned and contact-aware constraints. The authors validate their system on three manipulation tasks (in-hand translation, rolling, and multi-fingered retrieval) using diffusion policy learning, demonstrating superior performance compared to prior works.", "questions": "1. Teleoperation latency: What is the end-to-end latency from human hand motion to robot execution? How does the optimization solver perform under rapid movements or when approaching joint limits?\n2. Tactile sensor durability: Given that flat piezoresistive sensors are \"inevitable to wear and tear,\" what is the expected lifetime under typical manipulation loads? How does sensor degradation affect policy performance?\n3. Force control capabilities: The system uses position control throughout. How does this limit performance in tasks requiring precise force regulation, such as fragile object handling or assembly?", "limitations": "N/A", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 4, "originality": 4, "strengths_and_weaknesses": "## Strengths\n1. Comprehensive hardware-software co-design: The paper presents a well-integrated system where hardware design, perception integration, and teleoperation interface are jointly optimized. This holistic approach addresses real practical challenges in dexterous manipulation data collection.\n2. Innovative mechanical design: The universal multi-phalangeal actuation scheme with bevel gear differential mechanism is clever, achieving compact 20mm finger thickness while maintaining robustness. The parallel MCP joint design shows 2.3x better load capacity than LEAP Hand's tandem configuration.\n3. Robust perception integration: The hardware-level synchronization achieving sub-7ms latency across modalities is impressive. The spatial alignment of tactile readings into \"local touch point clouds\" provides direct geometric correspondence, addressing a key limitation in prior tactile manipulation work.\n\n## Weaknesses\n1. Limited task complexity: While the three evaluated tasks demonstrate basic dexterous capabilities, they are relatively simple compared to real-world manipulation needs. Tasks like tool use, bimanual coordination, or fine assembly would better demonstrate the platform's capabilities for \"generalist robot autonomy.\"\n2. Perception limitations: The flat tactile sensors provide only normal force measurements, lacking shear force sensing crucial for slip detection and fine manipulation. The single wrist-mounted camera may suffer from occlusions during complex manipulations.", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for the detailed reading of our paper and constructive suggestions! We hope our responses adequately address the following questions about our work. Please let us know if there’s anything we can clarify further.\n```\n1. Limited task complexity: While the three evaluated tasks demonstrate basic dexterous capabilities, they are relatively simple compared to real-world manipulation needs. Tasks like tool use, bimanual coordination, or fine assembly would better demonstrate the platform's capabilities for \"generalist robot autonomy.\"\n```\n**Reply**: The three evaluated tasks, namely in-hand rolling, translation, and multi-finger retrieval, are chosen as atomic skills that test durability, sensing consistency, and dexterous control. More complex tasks such as tool use can be viewed as compositions of these fundamentals. RAPID Hand is designed as an affordable, reliable platform for consistent multimodal data collection. While advancing to more complex tasks would further demonstrate its potential, progress is constrained by the difficulty of collecting safe, high-quality demonstrations, since human teleoperation without tactile feedback makes producing dexterous motions on a different embodiment particularly challenging [1]. As a next step, we are exploring **RAPID Glove**, a high-**R**esolution, **A**ffordable, ha**P**tic **I**nterface for **D**emonstrating contact-rich manipulation, as an important direction for extending the platform’s capabilities.\n\n```\n2. Perception limitations: The flat tactile sensors provide only normal force measurements, lacking shear force sensing crucial for slip detection and fine manipulation. The single wrist-mounted camera may suffer from occlusions during complex manipulations.\n```\n**Reply**: The current system uses flat fingertip tactile arrays to balance affordability with consistent performance. While shear force sensing can improve slip detection and fine manipulation, our tests with sensors from Paxini [2], TashanTec [3], and Xela Robotics [4] confirmed such benefits but also showed significant increases in cost, maintenance effort, and difficulty in ensuring high-resolution, consistent readings. Given our focus on cost-effectiveness and robustness, we chose flat arrays for this design.\n\nFor vision, we recognize that a single wrist-mounted camera may lead to occlusions. Our perception framework can readily support hardware-synchronized external cameras, and we plan to explore this extension in future work to improve coverage during complex manipulations.\n\n```\n3. Teleoperation latency: What is the end-to-end latency from human hand motion to robot execution? How does the optimization solver perform under rapid movements or when approaching joint limits?\n```\n**Reply**: The end-to-end latency from human hand motion to robot execution is mainly due to the UR collaborative arm, typically around 1–2 s. The RAPID Hand itself responds with minimal delay. While this latency is noticeable, teleoperators generally adapt after some practice, allowing us to collect demonstrations reliably.\n\nFor rapid movements, the optimization solver can compute inverse kinematics solutions, but execution remains limited by the arm’s delay. To reduce risks of collision or hardware strain, we avoid aggressive motions and prevent the arm from approaching joint limits. We acknowledge that reducing arm latency would further improve teleoperation, and this could be addressed by adopting a robotic arm with faster execution in future work.\n\n```\n4. Tactile sensor durability: Given that flat piezoresistive sensors are \"inevitable to wear and tear,\" what is the expected lifetime under typical manipulation loads? How does sensor degradation affect policy performance?\n```\n**Reply**: Given that piezoresistive sensors are inevitably subject to wear and tear, we believe their durability is largely determined by fabrication processes and quality. For reference, we previously used the tactile sensor in [5], which maintained stable performance for about three months of daily manipulation before gradually declining. The sensors used in this work, according to supplier testing, can withstand over one million impacts at a 10 kg load. Based on our experience, since their deployment in November 2024, we have not needed to replace any, and no noticeable degradation has been observed.\n\nIf replacement is required, each sensor is mass-produced and costs about **$70** [6], in contrast to alternatives such as Paxini [2] (**$400**) and Xela USkin [4] (**$2,000**), which helps keep maintenance affordable. To date, RAPID Hand has consistently performed in-hand translation and rolling tasks, including repeated public demonstrations, without evident loss of performance.\n\n```\n5. Force control capabilities: The system uses position control throughout. How does this limit performance in tasks requiring precise force regulation, such as fragile object handling or assembly?\n```\n\n**Reply**: Using position control alone has inherent limitations, especially for tasks that require precise force regulation. In our evaluations, we occasionally observed excessive forces, such as minor surface damage to vegetables during in-hand rolling. In this work, we adopt position control to validate the stability and effectiveness of the RAPID Hand platform for autonomous policy learning, as it allows reliable and consistent execution across a range of contact-rich tasks, though we note that force control will be essential for more delicate tasks such as fragile object handling or fine assembly and plan to pursue this in future work.\n\n[1] Yin, Zhao-Heng, et al. \"DexterityGen: Foundation controller for unprecedented dexterity.\" Robotics: Science and Systems (RSS 2025).\n\n[2] https://paxini.com/\n\n[3] https://www.tashantec.com/\n\n[4] https://www.xelarobotics.com/tactile-sensors\n\n[5] Lu, Peng, et al. \"Thermoformed electronic skins for conformal tactile sensor arrays.\" 2024 IEEE International Conference on Robotics and Automation (ICRA 2024).\n\n[6] https://www.moxiantech.com/robot-scene/14"}, {"review_id": "YRFGu292oK", "reviewer": "Reviewer_65oh", "summary": "This paper presents RAPID Hand, a low-cost, modular, and open-source 20-DoF robotic hand platform designed for collecting high-quality real-world dexterous manipulation data. The system integrates whole-hand multimodal perception—including vision, touch, and proprioception—with sub-7ms synchronization. It allows researchers to collect data with high-DoF teleoperation and high-fidelity perception. The effectiveness of the system has been validated by collecting demonstrations on three common manipulation tasks and training a diffusion policy to achieve stably high success rate.", "questions": "1. In terms of labor, time, and expertise, what does it take to assemble a RAPID Hand?\n2. How robust is the hand? How often does it break or burn out? How does the system handle sensor degradation or calibration drift over time? The paper shows stable tracking under load, no overheating, and larger load capacity over LEAP Hand. However, failure rates or long-term durability (e.g., motor burnout, sensor degradation) are not reported.\n3. Can the hand be equipped on mainstream robot arms and bodies? In the paper, the hand is equipped on UR10e. But it does not discuss integration with other arms or humanoid platforms, nor does it provide mounting specs or interface standards.\n4. Can the system fully replay a recorded trajectory?\n5. How transferable is the data or policies on other widely used dexterous hands (e.g., Allegro, Shadow) to RAPID Hand?", "limitations": "1. The paper does not mention whether the system can reliably replay recorded trajectories, which is important for assessing control fidelity, calibration stability, and reproducibility of demonstrations.\n2. While the RAPID Hand is cost-effective in terms of hardware components, its assembly requires time, technical expertise, and access to tools such as 3D printers. These practical barriers may limit its accessibility to broader segments of the research community, particularly those without prior experience in hardware prototyping.\n3. Given the number of existing dexterous hands that have been in used in the community for years, one general question for a new hardware system is how easily it can be integrated with mainstream robotic arms or humanoid platforms. Furthermore, it remains unclear whether data or policies trained on other widely used dexterous hands (e.g., Allegro, Shadow) can be effectively transferred to RAPID Hand, which is a critical consideration for adoption in existing research pipelines.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. RAPID Hand has 20 DoF with anthropomorphic kinematics that support highly synchronized teleoperation and dexterous manipulation.\n2. The paper lays out a comprehensive list of critical hardware specifications, such as thumb opposability volume, fingertip force, weight, size, joint accuracy, record frequency, robustness under latency and dropout.\n3. RAPID Hand uses low-cost and off-the-shelf components with a total cost of only $3,500 and is thus more repairable.\n4. RAPID Hand collects three modalities—vision from a wrist-mounted RGB-D camera, touch from piezoresistive tactile sensors, and 20-DoF proprioception from the joints—that enable high performance on common manipulation tasks.\n5. The paper shows that a diffusion policy trained on the data collected by this system can achieve stably high success rate on three different common manipulation tasks.\n\nWeaknesses:\n1. As RAPID Hand requires manual assembly, even if the bill of materials is low, some hidden costs, such as time, tooling (e.g., 3D printer), and expertise required for assembly are non-trivial and may limit accessibility for some labs.\n2. The paper does not mention repeatability by replaying collected trajectories, which should be a straightforward evaluation of the accuracy of the entire system.\n3. While the paper emphasizes the utility of wrist-mounted vision for in-hand perception, it does not discuss the compatibility of RAPID Hand with diverse robot arms or full-body humanoid platforms. As dexterous hands are increasingly deployed in integrated systems, the lack of evaluation or guidance on cross-platform adaptability may limit the system's practical adoption.", "was_revised": true, "final_justification": "Taking the authors' rebuttal into consideration, RAPID Hand presents sufficient evidence of being robust, reliable, and economical. The authors also have a plan for future support and the development of RAPID Glove, which holds great potential for widespread adoption by the community. Therefore, I have decided to change my rating to \"Accept.\"", "rebuttal": "We thank the reviewer for the detailed reading of our paper and constructive suggestions! We hope our responses adequately address the following questions about our work. Please let us know if there’s anything we can clarify further.\n\n```\n1. As RAPID Hand requires manual assembly, even if the bill of materials is low, some hidden costs, such as time, tooling (e.g., 3D printer), and expertise required for assembly are non-trivial and may limit accessibility for some labs. In terms of labor, time, and expertise, what does it take to assemble a RAPID Hand?\n```\n**Reply**: Manual assembly inevitably involves non-trivial costs in time, tooling, and expertise beyond the bill of materials, a challenge shared by other hardware platforms such as LEAP Hand (about 4 hours to assemble [1]). In our experience, assembling a RAPID Hand takes about 6 hours for an undergraduate student familiar with standard prototyping tools. Although RAPID Hand includes slightly more components than LEAP Hand, the overall assembly difficulty is comparable. The required equipment is limited to a desktop 3D printer and common tools such as soldering kits and screwdrivers.\n\nTo lower the barrier, we design the hand with modular components and provide detailed open-source CAD files, wiring diagrams, and assembly guides. While some effort is unavoidable, we believe the overall accessibility remains significantly higher than that of existing dexterous hands. To further lower the entry barrier, we plan to release step-by-step assembly instructions and partially assembled kits in future updates.\n\n```\n2. The paper does not mention repeatability by replaying collected trajectories, which should be a straightforward evaluation of the accuracy of the entire system. Can the system fully replay a recorded trajectory?\n```\n**Reply**: Yes, the system can fully replay a recorded trajectory. Repeatability involves both the arm and the hand. For the arm, we use the widely adopted UR10e, which offers a pose repeatability of ±0.05 mm [2]. For the hand, we reported joint precision during motion (Fig. 8) and performance under varying loads (Fig. 9) in the appendix, both demonstrating consistent behavior. Moreover, RAPID Hand has autonomously executed repeated public demonstrations, further supporting the system’s repeatability.\n\n```\n3. While the paper emphasizes the utility of wrist-mounted vision for in-hand perception, it does not discuss the compatibility of RAPID Hand with diverse robot arms or full-body humanoid platforms. As dexterous hands are increasingly deployed in integrated systems, the lack of evaluation or guidance on cross-platform adaptability may limit the system's practical adoption. Can the hand be equipped on mainstream robot arms and bodies? In the paper, the hand is equipped on UR10e. But it does not discuss integration with other arms or humanoid platforms, nor does it provide mounting specs or interface standards.\n```\n**Reply**:  RAPID Hand can be mounted on other robot arms and humanoid platforms, though some additional adapters are required. The hand uses a modular wrist structure in which the camera mount remains fixed, and only the wrist flange needs to be replaced when adapting to a different arm. Both the flange and the camera bracket follow standard mechanical interfaces, which facilitates integration across platforms.\n\nWe are preparing wrist flanges for mainstream collaborative arms (e.g., Franka, xArm) and humanoid platforms (e.g., Unitree G1), together with detailed mounting specifications and interface standards to further support cross-platform use.\n\n\n\n```\n4. How robust is the hand? How often does it break or burn out? How does the system handle sensor degradation or calibration drift over time? The paper shows stable tracking under load, no overheating, and larger load capacity over LEAP Hand. However, failure rates or long-term durability (e.g., motor burnout, sensor degradation) are not reported.\n```\n**Reply**: Based on our experience, RAPID Hand has remained generally robust under daily use for about six months. The only notable incident occurred when the arm lost control and the hand struck the table, breaking a finger linkage. The part was replaced within about 5 minutes, and the system continued to operate normally. In tasks such as multi-finger retrieval, where fingers frequently interact with stacked objects in confined spaces, the hand has so far functioned reliably without structural failures.\n\nWe have not observed clear signs of degradation in the tactile sensors to date. For proprioception, we use a custom calibration board to reset each phalange, typically once per day, to maintain accuracy. Current-based position control also helps reduce the risk of motor burnout from stall-induced overheating. We will include these details in the paper to provide a clearer picture of the system’s robustness and durability.\n\n\n\n\n```\n5. How transferable is the data or policies on other widely used dexterous hands (e.g., Allegro, Shadow) to RAPID Hand?\n```\n**Reply**: Cross-hand transfer remains a significant challenge in the field. Differences in actuation and transmission mechanisms, hand size, and sensing modalities (e.g., optical, piezoresistive, capacitive tactile sensors) all contribute to distributional gaps that affect transferability. For example, the Allegro hand lacks tactile sensing, while the Shadow hand is tendon-driven, making direct policy transfer difficult.\n\nA practical direction we see is to use data from other hands (e.g., Allegro, Shadow) as part of training robot foundation models [3, 4, 5], and then fine-tune with a smaller set of RAPID Hand demonstrations. While we have not yet conducted such experiments, we consider this an important avenue for future work.\n\n[1] Shaw, Kenneth, Ananye Agarwal, and Deepak Pathak. \"Leap hand: Low-cost, efficient, and anthropomorphic hand for robot learning.\" Robotics: Science and Systems (RSS 2023).\n\n[2] https://www.universal-robots.com/media/1807466/ur10e_e-series_datasheets_web.pdf\n\n[3] Physical Intelligence, et al. “π0.5: a vision-language-action model with open-world generalization.” arXiv preprint arXiv:2504.16054, 2025.\n\n[4] Gemini Robotics Team, et al. “Gemini robotics: Bringing ai into the physical world.” arXiv preprint arXiv:2503.20020, 2025.\n\n[5] Johan Bjorck, et al. “GR00T-N1: An open foundation model for generalist humanoid robots.” arXiv preprint arXiv:2503.14734, 2025."}]

Author rebuttal:

Meta-review: This paper introduces the RAPID Hand, a low-cost, 20-DoF robotic hand with integrated multimodal perception (vision/tactile) for dexterous manipulation. The hand design has parallel MCP joints and natural human-like kinematics and seems superior to alternatives like the LEAP or Allegro hand. The authors also provide a carefully designed hardware integration and software stack for sensing and teleoperation. They then validate this via policy learning on a number of high dexterity problems. Given the timeliness of the field of dexterous manipulation and the urgent need for an easily usable, dexterous hardware platform - this paper fills an important need for the field of dexterous manipulation. The reviewers did bring up some good points about haptic feedback, more clearly highlighting the advantages of the RAPID hand over others, lack of a clear tutorial for asssembly and limited task validation. These concerns should be addressed in a final version of the submission.
Decision: Accept (poster)