# DroneTrap: Drone Catching in Midair by Soft Robotic Hand with Color-Based Force Detection and Hand Gesture Recognition

arXiv: https://arxiv.org/abs/2102.03923 · HTML: https://mlsanity.com/paper/2102.03923/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Feb 2021.

## Built on by (most cited first, 1 of 1)

- [Curriculum-Based Adversarial Heterogeneous Agent Reinforcement Learning for Autonomous Quad-Copter Landing in Maritime Settings](https://mlsanity.com/paper/2609.12758/) (2026)

## Abstract (arXiv)

The paper proposes a novel concept of docking drones to make this process as safe and fast as possible. The idea behind the project is that a robot with a soft gripper grasps the drone in midair. The human operator navigates the robotic arm with the ML-based gesture recognition interface. The 3-finger robot hand with soft fingers is equipped with touch sensors, making it possible to achieve safe drone catching and avoid inadvertent damage to the drone's propellers and motors. Additionally, the soft hand is featured with a unique color-based force estimation technology based on a computer vision (CV) system. Moreover, the visual color-changing system makes it easier for the human operator to interpret the applied forces. Without any additional programming, the operator has full real-time control of the robot's motion and task execution by wearing a mocap glove with gesture recognition, which was developed and applied for the high-level control of DroneTrap. The experimental results revealed that the developed color-based force estimation can be applied for rigid object capturing with high precision (95.3%). The proposed technology can potentially revolutionize the landing and deployment of drones for parcel delivery on uneven ground, structure maintenance and inspection, risque operations, and etc.