16-DoF Tendon-Driven Hand

• 16-DoF tendon-driven hand is a robotic platform that mimics human hand biomechanics using independent tendon paths for high dexterity.
• It employs a one-actuator-per-DoF approach with passive coupling via linkages, ensuring precise joint control and diverse grasp configurations.
• Its open-source framework and validated performance benchmarks drive advancements in prosthetics, robotic manipulation, and teleoperation research.

A 16-degree-of-freedom (DoF) tendon-driven hand is a robotic manipulation platform that employs independent tendon paths and actuators to achieve dexterous, human-inspired motion across all core articulations of an anthropomorphic hand. This architecture supports high-dimensional independent joint control, biomimetic linkage patterns, and sophisticated grasping capability, and forms the foundation for research in dexterous manipulation, prosthetics, and robotic learning. Below, the main engineering concepts, mechanical designs, performance benchmarks, and broader significance of 16-DoF tendon-driven hands are described—with factual fidelity to prominent open-source hardware exemplars and foundational studies.

1. Mechanical Architecture and Tendon Routing

A 16-DoF tendon-driven hand (such as BiDexHand (2504.14712)) typically replicates the essential biomechanics of the human hand: four fingers with 3 DoF each (DIP, PIP, MCP flexion/extension), and a thumb with 4 DoF (MCP flexion/extension, MCP abduction/adduction, CMC flexion/extension, CMC abduction/adduction). This yields a total of 16 independently actuated DoFs, with additional joints (e.g., coupled DIP-PIP) linked passively.

The actuation relies on cable/tendon-driven transmission mechanisms, with each DoF driven by a discrete servo via a flexible cable or tendon. These cables are routed anatomically through PTFE sheaths and guide rings to minimize friction and follow human tendon paths. The transmission employs:

• Independent Pull-Pull Routing: Each active DoF uses an antagonistic cable loop for bidirectional (flexion/extension) control.
• Mechanical Coupling via Linkages: In order to economize on actuators and mimic biological synergies, key joints (such as DIP-PIP pairs) use passive anti-parallelogram 4-bar linkages, establishing a prescribed kinematic relationship:

$$\theta_{D} = 2 \arctan\left(\frac{l_1 - l_2}{l_1 + l_2} \cot\left(\frac{\theta_{P}}{2}\right)\right)$$

where $\theta_D$ is the DIP angle, $\theta_P$ is the PIP angle, and $l_1, l_2$ are characteristic linkage lengths.

• Tensioning Systems: Spring-based or adjustable tensioners maintain cable tautness and compensate for wear.

The thumb employs similar strategies, enabling multidirectional opposition and complex opposition tasks.

2. Achieving High-Dimensional Dexterity

The hand’s joint structure (21 joints, 16 actively controlled) permits independent control of each joint relevant to human-like manipulation. This configuration, achieved via:

• One-actuator-per-DoF Design: Ensures that each joint may be commanded independently for maximal workspace and to replicate sophisticated grasps and in-hand manipulation.
• Passive Coupling: Five joints (e.g., DIP joints) are mechanically coupled via linkages—reducing the actuator and cable count while preserving the natural hand’s closed kinematic chains.
• Antagonistic Tendon Arrangements: Enable both flexion and extension, improving energy efficiency and mimicry of biological muscle pairs.

Compared to highly underactuated hands (which may use 2–4 actuators for all joints), the 16-DoF approach maximizes versatility and grasp repertoire at the expense of increased mechanical complexity.

3. Control Modes and Software Infrastructure

Modern 16-DoF tendon-driven hands support several control paradigms to exploit the full dexterity of their hardware:

• Joint-level Position/Velocity Control: Each DoF can be addressed directly, typically over a protocol such as ROS2 for high-level coordination.
• Task-based and Synergy-Based Control: The structure supports mapping user commands (e.g., from a vision-based teleoperation interface or EMG signals) onto joint or synergy spaces for efficient human-robot interaction.
• Teleoperation: The hand can shadow the user’s hand pose, with mapping from computer vision hand landmarks or VR glove data.
• Open-Source ROS2 Software: The design is complemented by open-source drivers, APIs, and simulation models to facilitate both research and deployment.

4. Performance Metrics and Benchmark Evaluation

Standardized benchmarks quantify dexterity, opposability, and force output:

Evaluation	Result	Significance
GRASP Taxonomy	Success on all 33 grasp types	Demonstrates full-spectrum human-like grasping
Kapandji Thumb Test	9/11 positions reached	High thumb opposability; fine in-hand manipulation
Fingertip Force Output	Avg 2.14 N; 10 lb object lifted	Sufficient for real-world manipulation tasks

• GRASP Taxonomy ([Feix et al.]): Completion indicates ability to execute all standardized power, precision, and intermediate grasps over diverse object geometries.
• Kapandji Test: Assesses thumb’s range of opposition against the hand’s key anatomical landmarks; 9/11 positions met indicates near-human thumb function.
• Force Output: Each finger can supply average tip forces suitable for routine lifting and manipulation.
• Open-Source Validation: The platform is fully open-sourced (https://github.com/wengmister/BiDexHand), allowing reproducibility and comparative studies.

5. Analytical Modeling and Engineering Formulas

Design and control are supported by analytical models:

• Coupled Joint Kinematics: The anti-parallelogram linkage enforces the $\theta_D$–$\theta_P$ relationship above, reducing control effort.
• Tendon Excursion: For each joint, the tendon length change $\Delta l$ is computed as a function of joint angle; e.g. for a joint with radius $r$:

$\Delta l(\theta) = r \cdot \theta$

• Force-to-Torque Mapping: At each joint,

$\tau_i = r_i \cdot T_{\text{tendon}}$

with $r_i$ the moment arm and $T_{\text{tendon}}$ tendon tension.

• Spring-Tensioned Cables: Continuous mechanical tension avoids backlash and supports precise kinematic mapping.

These models feed into simulation, trajectory generation, and performance assessment.

6. Mechanical Innovation and Construction

To maximize accessibility, affordability, and reproducibility:

• 3D Printing and Modular Assembly: The hand body, phalanges, and coupler linkages are 3D printed. Modular phalanx design allows for replacement, rapid prototyping, and adaptation.
• PTFE or Similar Low-friction Sheathing: Enables routing of multiple tendons through narrow, tortuous joint pathways without excessive friction losses.
• Compact Integration: Actuators are located proximally (e.g., in the hand base or forearm), reducing distal weight and maintaining inertia close to the joint axes for agile motion.

7. Research Impact and Open-Source Dissemination

The BiDexHand and similar platforms represent a major advance in enabling high-dimensional dexterous manipulation for the broader research community:

• Democratization of Dexterous Hands: With open-source hardware and software, access is no longer limited to specialized labs.
• Benchmarking and Comparability: Clearing standardized performance benchmarks allows for meaningful comparison and iterative improvement in manipulation research.
• Extensibility: Modular, open architectures support integration into larger systems (prosthetic arms, humanoid robots), as well as evaluation of new control strategies (learning-based, model-based, synergy-based, teleoperation).
• Community Contribution: Open repositories facilitate continuous enhancement and adaptation to emerging research needs.

---

A 16-DoF tendon-driven hand exemplifies the convergence of biomimetic mechanism design, robust analytical modeling, and open, reproducible research infrastructure. Through joint-level independence, efficient tendon routing, mechanical coupling innovations, and validated high dexterity, such platforms form the basis for advanced robotic manipulation research and practical application.