---
title: 'LEAP Hand V2: Open-Source Robotic Hand'
url: https://www.emergentmind.com/topics/leap-hand-v2
type: topic
---

# LEAP Hand V2: Open-Source Robotic Hand

The LEAP Hand V2, formally known as the MOTIF Hand, is a five-fingered, tendon-driven robotic hand platform that advances dexterous manipulation capabilities through the integration of multimodal sensing, robust mechanical enhancements, and a three-tier an onboard electronics and control architecture. Extending the original 12-DoF LEAP Hand, the V2 model incorporates onboard dense tactile arrays, a time-of-flight (ToF) depth sensor, a thermal camera, multiple inertial measurement units (IMUs), and an RGB camera while remaining cost-effective (under $4,000 USD) and fully open-source. The MOTIF Hand’s hardware and software enable experimental manipulation tasks that leverage temperature, force, motion, and vision sensing in closed-loop, real-world scenarios [2506.19201].

## 1. Mechanical and Structural Enhancements

The LEAP Hand V2 preserves the underlying architecture of the original LEAP Hand—a 12-DoF, tendon-driven, 5-finger mechanism actuated by Dynamixel servos—but incorporates several significant structural modifications:

- **Actuation and Geometry:** Dynamixel RX-10 servos and a finger-level four-bar, tendon-driven linkage are retained. The proximal phalanges are lengthened by 5 mm, and the distal phalanges are shortened by 3 mm, accommodating IMU boards at each joint with no net change to reach. The palm is thickened by 2 mm (PLA), permitting integration of a second-tier PCB and the side-mounted ToF sensor.
- **Degrees of Freedom:** 12 revolute DoFs persist (3 per finger), with a 2-DoF thumb base for abduction/adduction. Compliance pads (0.5 mm silicone) are added around each joint to provide minor passive compliance and reduce electromagnetic interference.
- **Materials and Compliance:**
  - Load-bearing members: 3D-printed PLA, palm reinforced with a 0.8 mm internal aluminum stiffener.
  - Joints: brass-sleeved revolute bearings.
  - Silicone “valleys” reduce tendon friction and IMU chatter.
  - Palm cavity lined with electromagnetic-shielding tape to minimize interference with BMM350 IMUs.

These design choices preserve the LEAP Hand’s kinematic expressivity while supporting sensor integration and enhanced mechanical robustness [2506.19201].

## 2. Sensor Suite and Multimodal Integration

The MOTIF Hand integrates five sensing modalities:

- **Dense Tactile Arrays:** Each finger pad carries a 6×6 grid of taxels (2.5 mm²/taxel, 20 g trigger), mounted on both proximal and middle phalanges, sampled at 200 Hz (I²C chain).
- **Depth (ToF) Sensor:** VL53L5 module, palm-centered, supply a 64×64 depth map ($\pm10$ cm, 15 Hz).
- **Thermal Camera:** FLIR Lepton 3.5 (raw 160×120, interpolated 1280×960, 8.7–13.5 μm), side-mounted with $\pm15^\circ$ adjustability, outputs $T(u,v)$ at 8 Hz.
- **Visual (RGB) Camera:** Raspberry Pi Camera Module 2 (8 MP, 30 fps), dorsally mounted for color and segmentation in 3D reconstruction.
- **Inertial Measurement Units (IMUs):** BMM350 nine-axis modules at every finger joint (11 total) and 4 on the palm. Accel/gyro sampled at 500 Hz, fused at 200 Hz.

**Calibration and Mapping Models:**
- **Thermal–RGB Reprojection:**
  $$
  \mathbf{x}_{3D} = T_{cw}^{-1}\cdot[d\,K^{-1}[x\ y\ 1]^T]
  $$
  where $K$ are the thermal camera intrinsics, $T_{cw}$ is the camera-to-world pose, and $d$ is ToF-provided depth.
- **Tactile-to-Force Conversion:**
  $$
  F_{ij} = \alpha(v_{ij} - v_0) + \beta(v_{ij} - v_0)^2
  $$
  with calibration over reference weights (20–500 g).
- **IMU Bias Correction (EKF):**
  $$
  \mathbf{x} = [\mathbf{q}, \mathbf{b}_\omega, \mathbf{b}_a]^T
  $$
  with discrete-time prediction and measurement updates for orientation and bias estimation.

This sensor array enables high-fidelity, real-time acquisition of force, visual, thermal, and kinematic data for advanced robotic manipulation [2506.19201].

## 3. Electronics and Control Architecture

LEAP Hand V2 features a hierarchical, three-tier electronics and control stack:

- **Tier 1 (Joint-Level):** Each IMU communicates with a joint-dedicated STM32F405 MCU via RS485 (Modbus RTU, accel/gyro@500 Hz, mag@100 Hz).
- **Tier 2 (Palm Aggregation):** A custom PCB aggregates tactile, IMUs, and ToF sensor data. Multistream IMU fusion (EKF), tactile and ToF packaging, and communication with Tier 3 through UART (USB-C) interface.
- **Tier 3 (Central Compute):** Raspberry Pi 5 (8 cores @ 2.4 GHz, 4 GB RAM) operating ROS 2 for sensor synchronization, SLAM (SfM/dense recon), and grasp planning. It interfaces with the thermal camera (SPI + I²C), PiCam (CSI), ToF (I²C), and Tier 2 via UART.

**Controller Hierarchy:**
- **Low-Level:** Dynamixel torque: $\tau = K_p(\theta_{des} - \theta_{act}) + K_d(\omega_{des} - \omega_{act})$
- **Mid-Level:** Fuses IMU/tactile slip detection for pose/impedance control.
- **High-Level:** MuJoCo-supported Real2Sim pipeline for motion planning [2506.19201].

## 4. Quantitative Performance Evaluation

Task-driven evaluations highlight the efficacy of LEAP Hand V2’s multimodal sensing:

- **Temperature-Aware 3D Reconstruction and Safe Grasping:**
  - 90 multi-modal (RGB, thermal, depth) captures processed via SfM and Gaussian splatting for meshing.
  - Thermal–RGB alignment as per the calibration equation.
  - Denoising removes 92% of spurious thermal outliers.
  - Imitation-learned grasp policy (5,000 frames/12 demonstrations) yields 95% success in avoiding regions $>60^\circ$C, with zero contact failures over 30 trials.

- **Mass Differentiation via “Flick”:**
  - U-shaped objects (82 g, 125 g, 219 g) flicked using identical torques; 50 trials per mass.
  - 42 time-series features extracted (min/max/mean/std across axes, range, magnitude).
  - Linear Discriminant Analysis: LD1 captures 77.5% and LD2 22.5% variance; 3-class accuracy 96.4% (5-fold CV).
  - Dominant features: ACC_Z_Min, ACC_Range, MAG_Y_Mean.

These results substantiate the hand’s capacity for closed-loop, safe grasping and tactile-kinematic mass distinction unattainable by vision-only approaches [2506.19201].

## 5. Reproducibility, Open-Source Resources, and Cost

LEAP Hand V2 emphasizes accessibility and community adoption:

| Component                  | Quantity | Total USD |
|----------------------------|----------|-----------|
| Dynamixel RX-10 servos     | 12       | 348       |
| Tactile 6×6 arrays         | 5        | 500       |
| BMM350 IMU modules         | 15       | 75        |
| FLIR Lepton 3.5 camera     | 1        | 200       |
| Raspberry Pi 5             | 1        | 80        |
| Pi Camera Module 2         | 1        | 30        |
| VL53L5 ToF depth sensor    | 1        | 50        |
| PCBs/electronics           | —        | 400       |
| 3D-printed PLA             | —        | 200       |
| **TOTAL (≤$4k)**           |          | 2,113     |

All design files (CAD, PCB layouts), MCU firmware, and ROS 2 software are open-source at [https://github.com/slurm-lab-usc/motif-hand](https://github.com/slurm-lab-usc/motif-hand), including detailed assembly documentation, calibration routines, and demonstration scripts [2506.19201].

## 6. Limitations and Prospective Advancements

LEAP Hand V2 exhibits several current limitations:

- Absence of dedicated fingertip GelSight/DIGIT sensors; tactile arrays are limited to the proximal phalanges.
- Native thermal camera resolution (160×120) constrains fine-grained heat mapping.
- IMU drift and high-frequency magnetic interference require periodic re-zeroing and shielding, respectively.

Proposed directions for future V3 development include:

- Integration of DIGIT 360 or GelSight at every fingertip for sub-millimeter contact geometry.
- Higher-resolution thermal sensing (e.g., Lepton 3.5 to Boson 320×256) with on-chip fusion.
- Addition of strain-gauge torque sensors at the actuator level.
- Soft-skin over-molding for human-like compliance, paralleling planned enhancements in the forthcoming LEAP Hand V2 by Shaw et al.
- End-to-end, visuo-thermal-tactile reinforcement learning for more adaptive manipulation policies.

The MOTIF/LEAP Hand V2, by coupling dense and multimodal sensing with an accessible open-source foundation, establishes a technically robust and reproducible platform for experimental research in dexterous robotic manipulation [2506.19201].

Source: https://www.emergentmind.com/topics/leap-hand-v2