---
title: 'Ruka-v2: Open-Source Dexterous Robotic Hand'
url: https://www.emergentmind.com/topics/ruka-v2
type: topic
---

# Ruka-v2: Open-Source Dexterous Robotic Hand

Ruka-v2 is a fully open-source, tendon-driven humanoid robotic hand designed for dexterous manipulation and robot learning research. Building on its predecessor, Ruka, Ruka-v2 introduces two previously absent degrees of freedom—wrist mobility and finger abduction/adduction—enabling human-like behaviors such as in-hand rotation, fine grasping of thin objects, and manipulation in confined spaces. The platform is engineered for accessibility, with all parts 3D-printable using consumer FDM equipment, open-source controller software, and standard hardware components, maintaining a total hardware cost of approximately \$1,500 [2603.26660].

## 1. Mechanical Architecture and Kinematics

Ruka-v2 features a fully tendon-driven actuation using Faulhaber DC motors positioned in the proximal forearm shell, minimizing distal inertia. Tendons, routed via Bowden cables, pass through a central wrist pivot and custom routing plate to the fingers, reducing frictional losses and sharp bends.

The hand incorporates a total of 18 actuated degrees of freedom (DOF) in the fingers and thumb: for each non-thumb finger (index, middle, ring, pinky), the DIP (1 DOF), PIP (1 DOF), MCP flexion/extension (1 DOF), and MCP abduction/adduction (1 DOF) are implemented, except the middle finger which is fixed for lateral spread as a reference. The thumb includes IP (1 DOF), MCP (1 DOF), and CMC opponency (1 DOF). This yields $4 \times 4 + 3 - 1 = 18$ DOF for the digits, plus a decoupled 2-DOF wrist, for a total of 20 actuated DOF.

### Parallel Wrist and Finger Abduction

The wrist implements two axes—flexion/extension (±45° flex, ±30° extension) and radial/ulnar deviation (±35°)—driven independently via rectangular four-bar linkages intersecting at a single geometric pivot defined by a passive spherical joint. All finger tendons traverse a central through-hole in the wrist for direct routing. Each finger’s abduction/adduction axis is housed in an independent knuckle module with passive compliance provided by extension springs; a dedicated tendon enables active adduction.

### Modular Components and Sensing

Structural components, pulleys, and housings are single-material PETG or PLA; soft fingertip pads use flexible TPU. Bearings, springs, and fasteners are off-the-shelf, streamlining assembly. Optional DIP/PIP coupling uses fixed-length strings to enforce $\theta_2 \approx \theta_1$, enhancing repeatability at the cost of compliance.

Joint angle measurement is facilitated by AS5600 magnetic encoders (12-bit), attachable via press-fit magnets and multiplexed via ESP32 QTPy for calibration and closed-loop experiments.

## 2. Control System and Human-to-Robot Mapping

The control stack employs a two-stage architecture:

1. **Vision-based Retargeting:** 3D keypoints, captured via stereo/RGB cameras and processed using Mediapipe or DepthAI, are aligned to the robot-base coordinate frame. The AnyTeleop-DexRetarget system solves:
   $$
   \min_{\theta} \sum_{i=1}^N \lVert\mathbf{v}_i(\theta) - \mathbf{d}_i\rVert^2
   $$
   where $\mathbf{v}_i(\theta)$ are robot link vectors and $\mathbf{d}_i$ are the corresponding human finger vectors.
2. **Joint-to-Motor Linear Mapping:** Each joint maps desired angle $\theta \in [\theta_{\min}, \theta_{\max}]$ to motor position $p \in [p_{\min}, p_{\max}]$ by:
   $$
   p = p_{\min} + c\, \frac{\theta - \theta_{\min}}{\theta_{\max} - \theta_{\min}} (p_{\max} - p_{\min})
   $$
   where $c$ is a per-joint gain for friction/tendon stretch compensation.

The control loop consists of: (1) video keypoint extraction, (2) retargeting optimizer for joint angles $\theta$, (3) mapping to motor positions $p$, (4) dispatch via CAN bus, and (5) optional encoder-based closed-loop correction. All calibration limits are determined by automated routines moving joints to hard-stops.

## 3. Empirical Performance and Evaluation

### User Study: Ruka vs. Ruka-v2

A study with $N=10$ users evaluated teleoperation (Oculus + OpenTeach, 7-DOF Franka + Ruka hand) on three tasks—bread pick & place, pen grasp, and book opening. Results indicated a 51.3% reduction in mean completion time and a 21.2% increase in success rate for Ruka-v2 relative to the original Ruka.

| Metric                     | Ruka-v1 | Ruka-v2 | Relative Change |
|----------------------------|---------|---------|-----------------|
| Mean Completion Time       |   —     |   —     | $-51.3\%$       |
| Overall Success Rate       |   —     |   —     | $+21.2\%$       |

### Thermal and Mechanical Endurance

Continuous 5-hour tests (finger and wrist sweeps) showed peak/steady temperature changes: fingers (Δ0.81°C), thumb (Δ1.70°C), wrist (Δ7.25°C), with no observed thermal throttling.

Static payload tests showed:
- DIP–PIP (non-thumb): 1200 g (15 s)
- MCP (non-thumb): 780 g (15 s)
- Adduction: 150 g (15 s)
- Thumb curl: 835 g (20 s)
- Wrist supination/pronation: 1215 g (20 s)
- Wrist radial/ulnar-side up: 835 g (20 s)

### Kinematic and Control Fidelity

Joint tracking averaged 8.26° absolute error (10.68% of ROM). Coupled DIP/PIP fingers achieved ±2–3° repeatability, compared to >10° hysteresis and variability for uncoupled fingers.

## 4. Applications in Robot Learning

### Teleoperation Benchmarks

Ruka-v2 supports extensive teleoperation suites: 10 single-arm and 3 bimanual tasks (including bread pick & place, pen grasp, book opening, soup ladle scooping, calligraphy, magnetic assembly, music-box opening, and cloth manipulation), with data collection via OpenTeach and Oculus interfaces.

### Autonomous Policy Learning

Autonomous learning leverages the BAKU framework (transformer with action-chunking). Observations comprise a 23-dimensional vector (7 Franka + 16 Ruka-v2 joints) concatenated with RGB visual features via ResNet-18 and MLP. Policies are trained with ≈100 demonstrations/task and Gaussian noise ($\sigma_p = 0.02$ rad, $\sigma_a = 0.01$ rad). Evaluated tasks include bread pick & place (success 8.2 ± 1.4), music-box opening (7.5 ± 2.0), and pen grasp with abduction (9.1 ± 0.8) out of 10. Loss combines standard behavior cloning MSE and transformer cross-entropy for chunked actions.

## 5. Limitations and Future Directions

The joint-to-motor mapping is a linear simplification. Planned work includes replacing this with data-driven tendon modeling using dense magnetic encoder feedback. Integration of tactile sensors (TPU-based e-flesh) may be affected by magnet interference, requiring further study. Additional avenues include reducing friction/slack using advanced liners, and torque-based control exploiting the tendon Jacobian:
$$
\tau = \mathbf{J}(\theta)^\top \mathbf{F}_\text{tendon}
$$
to facilitate force-sensitive manipulation.

## 6. Open-Source Accessibility and Reproducibility

All 3D print files, CAD models, build instructions, controller code, and demonstration videos are publicly available at https://ruka-hand-v2.github.io/. The platform is designed for broad accessibility and straightforward repair, lowering barriers for hardware teams and researchers seeking to reproduce or extend dexterous robot hand research [2603.26660].

Source: https://www.emergentmind.com/topics/ruka-v2