Title: RAPID Hand: Robust, Affordable, Perception-Integrated, Dexterous Manipulation Platform for Embodied Intelligence
Venue: NeurIPS 2025
Accepted: yes
Keywords: Robotics; Robotic Manipulation; Embodied Intelligence; Dexterous Manipulation; Robotic Grasping
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.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\n1. 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?\n\nLIMITATIONS:\n1. 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.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-07"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\n## 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.\n\nQUESTIONS:\n1. 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?\n\nLIMITATIONS:\nN/A", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-06-28"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\nQuestions:\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.\n\nLIMITATIONS:\nLimitations:\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.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-30"}

comments: 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. 

 Main Contributions: 

Designs a 20-DoF hand with parallel MCP joints and human-like motion. 

Integrates and synchronizes vision, tactile, and proprioceptive sensors. 

Provides a teleoperation interface for demonstration data collection. 

Makes the hardware and software open-source and affordable.

STRENGTHS AND WEAKNESSES:
Strengths： 

Open-source, low-cost, and easy to replicate for the research community. 

Hardware-level integration and synchronization of vision, tactile, and proprioceptive sensing; ensures high data fidelity. 

Weaknesses： 

Evaluation is mainly on a small set of tasks; broader real-world generalization needs further study. 

Lacks force feedback for teleoperation, reducing realism of remote control.

QUESTIONS:
1. The tactile sensors are limited to the fingertips. Can the authors discuss how this impacts tasks involving palm contact?  
2. The teleoperation interface not considered force feedback. Have the authors evaluated how this limitation affects quality?

LIMITATIONS:
please refer to the Weaknesses and questions
TITLE: Official Review
CLARITY: 3
IMPACT: 2
ORIGINALITY: 2
DATE: 2025-07-01