PHANTOM Hand

Teng Yan; Jiongxu Chen; Qixiang Hua; Yue Yu; Zihang Wang; Yaohua Liu; Bingzhuo Zhong

cs.ROMar 24, 2026

PHANTOM Hand

Authors: Teng Yan, Jiongxu Chen, Qixiang Hua, Yue Yu, Zihang Wang, Yaohua Liu, Bingzhuo Zhong

Organizations: The Hong Kong University of Science and Technology (Guangzhou)

Abstract

Tendon-driven underactuated hands excel in adaptive grasping but often suffer from kinematic unpredictability and highly non-linear force transmission. This ambiguity limits their ability to perform precise free-motion shaping and deliver reliable payloads for complex manipulation tasks. To address this, we introduce the PHANTOM Hand (Hybrid Precision-Augmented Compliance): a modular, 1:1 human-scale system featuring 6 actuators and 15 degrees of freedom (DoFs). We propose a unified framework that bridges the gap between precise analytic shaping and robust compliant grasping. By deriving a sparse mapping from physical geometry and integrating a mechanics-based compensation model, we effectively suppress kinematic drift caused by spring counter-tension and tendon elasticity. This approach achieves sub-degree kinematic reproducibility for free-motion planning while retaining the inherent mechanical compliance required for stable physical interaction. Experimental validation confirms the system's capabilities through (1) kinematic analysis verifying sub-degree global accuracy across the workspace; (2) static expressibility tests demonstrating complex hand gestures; (3) diverse grasping experiments covering power, precision, and tool-use categories; and (4) quantitative fingertip force characterization. The results demonstrate that the PHANTOM hand successfully combines analytic kinematic precision with continuous, predictable force output, significantly expanding the payload and dexterity of underactuated hands. To drive the development of the underactuated manipulation ecosystem, all hardware designs and control scripts are fully open-sourced for community engagement.

Figures & tables

Fig. 1: Overview and DOF mapping of PHANTOM Hand. (a) Human hand annotated with anatomical DOFs (MCP/PIP/DIP, thumb MP/IP, and CMC). (b) The PHANTOM hand with 15 joint DOFs $q_{1}\!-\!q_{15}$ : four long digits each with MCP/PIP/DIP flexion–extension, and the thumb with IP/MP flexion–extension and CMC abduction–adduction.

Fig. 2: Tendon routing and analytic modeling for four fingers. Four-finger figure-eight routing from full extension to an intermediate flexion: straight segments $L_{k}$ , joint pulleys of radius $R_{i}$ , cables connected to the servo hubs (purple), the passive ”cable-spring” return path (green), parameters related to the closed-form geometric expression of the MCP joint ( $\alpha$ , $d_{1}$ , $d_{2}$ ), and joint angles $q_{\text{MCP}}$ , $q_{\text{PIP}}$ , $q_{\text{DIP}}$ . $q_{i,\text{ref}}$ (where $i=\{\text{DIP},\text{PIP},\text{MCP}\}$ ) denotes the structural geometric limits of the independent finger module for analytical derivation, while the actual operational range limits are constrained by the palm housing to prevent excessive flexion.

Fig. 4: PHANTOM hand expressibility benchmark: hand gestures and grasping patterns. Left: Hand gestures, including precise thumb-to-finger opposition (thumb opposing the index, middle, and ring fingers respectively), representative multi-finger combinations and digit gestures 0-8. Right: An extended set 16 representative grasps under the same controller, covering diverse contact topologies (enveloping, lateral, and tip pinches) and tool morphologies (rod-like, ring-like, and press-type), for cross-task consistency assessment. All results from the same control stack (analytic mapping + mechanics-based compensation), no additional sensing.

Finger Joint	Joint Label	Admissible Workspace $[q_{\min},q_{\max}]$ (rad)
Thumb IP	$q_{1}$	$[0.00,0.99]$
Thumb MP	$q_{2}$	$[0.00,1.25]$
Index DIP	$q_{3}$	$[0.00,1.31]$
Index PIP	$q_{4}$	$[0.00,1.61]$
Index MCP	$q_{5}$	$[0.00,1.27]$
Middle DIP	$q_{6}$	$[0.00,1.28]$

TABLE I: Admissible workspace of each finger joint of PHANTOM hand. All values are in radians, corresponding to the joint indices

q_{1}\sim q_{15}

Fig. 5: Comparative analysis of joint angle residuals under uncompensated and compensated control. This figure illustrates the relationship between the angle residuals and the servo control angles for target joints across the five fingers. The red circles represent the original uncompensated residuals (Residual_ideal), while the green squares denote the residuals after mechanics-based compensation (Residual_comp). All subplots are standardized with a Y-axis range from

-2.0

+2.0

degrees to facilitate a direct comparison of performance.

Fig. 6: Mean Absolute Error (MAE) comparison of joint angles for five fingers of PHANTOM Hand under uncompensated and compensated conditions. The red bars represent the MAE between real joint angles and ideal joint angles (without compensation), while the green bars denote the MAE between real joint angles and compensation joint angles (with compensation). The fingers include the thumb (with MP and IP joints) and four fingers (each with MCP, PIP and DIP joints). The MAE is calculated in the unit of degrees ( ∘ ).

Fig. 7: Characterization of fingertip force and variable load manipulation of the PHANTOM hand. (a) Quantitative measurement of fingertip output: the index finger exhibits a stable maximum force of 3.06 N. (b) Flexible wire clamping: the thumb and index finger cooperatively exert a minimal contact force to securely hold a thin wire.

Finger	Thumb	Index	Middle	Ring	Pinky
Force (N)	3.35	2.87	2.51	2.73	2.33

TABLE II: Measured average fingertip forces of the PHANTOM hand. The data represent the mean output force of each finger calculated from five independent experimental trials using the digital force gauge shown in Fig. 7 .

Explore similar work

Jul 21, 2026

Design and stability analysis of an underactuated hand with passively rotating fingers

This paper presents an innovative design and stability analysis of an underactuated robotic finger with spatial mobility, designed to enhance gripping dexterity in robotic hands. The finger architecture incorporates a revolute joint at its base, enabling passive spatial rotation that facilitates both cylindrical and spherical grasping. With only two phalanges per finger, the design simplifies kinematic complexity while supporting precision and enveloping grasps. Stability criteria, based on the moment at the finger base joint induced by contact forces, are introduced to ensure reliable object gripping and prevent ejection during manipulation. The study also examines a differential mechanism that distributes a single actuation torque across multiple fingers, allowing adaptive and coordinated motion. This mechanism enhances the hand's ability to grasp diverse object shapes with minimal pre-grasp adjustments, leveraging passivity for autonomous adaptation. Theoretical findings are experimentally validated using a fully mechanical prototype, demonstrating versatility in performing cylindrical, spherical, parallel, and enveloping grasps. The integration of underactuation-both within individual fingers and among multiple fingers-reduces mechanical complexity, cost, and control demands while preserving functional adaptability. This work advances the development of compliant robotic hands suitable for applications requiring dexterity and robustness, such as agricultural robotics, logistics, assistive technologies, and waste sorting. Future research will focus on automating actuation and refining control strategies to further improve grasp stability and precision, paving the way for autonomous manipulation in unstructured environments.

Léonie Plancoulaine, Sylvain Guégan, Franck Plestan +1

LS2N, LS2N - équipe RoMas · Nantes Université, École Centrale Nantes, CNRS, LS2N, UMR 6004, 44300 Nantes, France · LGCGM +2

Robotic Hand Design Robotic Grasping

Ask PDF

Sep 23, 2026

A Quasi-Direct-Drive Underactuated Asymmetric Hand for Dexterous and Efficient Grasping and Manipulation

In this paper, we present the Berkeley QUAD (Quasi-direct-drive, Underactuated, Asymmetric Design) Hand, a four-finger anthropomorphic robotic hand with 11 degrees of freedom and 8 degrees of actuation. The design utilizes QDD actuation at the base of each finger, enabling high force transparency for dexterous, adaptive performance. However, the low torque density of these actuators traditionally presents major issues with size, weight, and thermal limits. We overcome this by applying bio-inspired asymmetry, delegating dexterity to the radial fingers through individual QDD actuation, and strength to the ulnar finger through an underactuated, compliantly coupled transmission driven by a larger QDD motor. A novel preloaded, linkage-based transmission permits this ulnar coupling in a way that preserves human-like workspace reachability. Under light loads, the ulnar motor drives the third (middle) finger directly for dexterity while the fourth (ring) finger mirrors its motion. However, under larger loads, the middle finger complies while the motor drives the ring finger further downwards and inwards toward the center of the grasp to apply better closure forces. Hardware evaluations validate this architecture, demonstrating that the hand achieves 29 out of 33 Feix taxonomy grasps and exhibits backdrive forces as low as 50 g for delicate interactions. Additionally, the underactuated fourth finger improves grasp closure and provides the spatial efficiency necessary for larger actuation, yielding up to a 96-fold reduction in heat generation during sustained loading. Webpage: https://benudavis.github.io/berkeley-quadhand/

Benjamin Davis, Chase Kidder, Hannah S. Stuart

Embodied Dexterity Group, Dept. of Mechanical Engineering, University of California Berkeley, Berkeley, CA, USA · General Motors, Warren, MI, USA

Robotic Hand Design Robotic Grasping Dexterous Manipulation

Ask PDF

Aug 17, 2026

H-PAC Hand: Control-Oriented Modeling and Tendon-Elasticity Compensation for an Underactuated Robotic Hand

Underactuated tendon-driven hands offer compact actuation and passive compliance, but tendon elongation under restoring-spring loading introduces configuration-dependent joint deviations. This paper presents H-PAC, a modular 6-actuator, 15-DoF robotic hand with a control-oriented modeling and implementation framework. A sparse analytical actuator-joint model is derived from the tendon-routing geometry, and a mechanics-based compensation model is developed to account for tendon-elasticity-induced joint errors. The proposed method is implemented in a hierarchical architecture: a host computer performs workspace-constrained posture mapping and compensation, while an ESP32 generates synchronized commands for six position-controlled servos. The same control parameters and execution strategy are used across all tasks without task-specific retuning. Monotonic servo-sweep experiments show that the compensation substantially improves joint-angle prediction. The MAE of the index DIP joint decreases from 1.15 degrees to 0.18 degrees, and all nine evaluated joints achieve an MAE below 0.23 degrees. Representative postures and grasping configurations are further executed using the same control pipeline without external joint or force sensing in the control loop. The results demonstrate a practical approach to improving posture reproducibility in compact underactuated robotic end-effectors.

Teng Yan, Jiongxu Chen, Teng Wang +5

The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China · Southeast University, Nanjing, China

Robotic Hand Design Tendon-Driven Actuation

Ask PDF