Ultra-Low-Impedance Robotic Gripper for High-Bandwidth and Transparent Physical Interaction
Joon Lee, Ari Choi, Seokhwan Jeong
AI summary
Problem
Conventional robotic grippers suffer from high mechanical impedance, limited control bandwidth, and reliance on external force sensors due to bulky actuators and high gear ratios that degrade physical transparency.
Approach
The researchers integrated direct-drive motors with a 1:2 low-ratio differential transmission, centralizing actuator mass at the base to minimize moving inertia while amplifying torque for flexion movements.
Key results
- Motor contribution to system inertia reduced to 0.236%
- Delivered 15 N nominal grasping force and 3.1 N fingertip force per finger
- Maintained mechanical impedance below 700 N/m within typical human manipulation frequencies
- Successfully demonstrated grasping of everyday objects and complex unscrewing tasks
Why it matters
Provides a hardware foundation for highly responsive, sensorless proprioceptive force estimation and robust physical interaction in dynamic robotic applications.
Abstract
Conventional robotic grippers relying on external force sensors or high gear-ratio actuators suffer from high me- chanical impedance and limited control bandwidth. To address these limitations, this study proposes a novel 9-DOF, three- fingered Direct-Drive Differential (DDD) gripper that integrates DD motors with an low gear ratio (1:2) differential transmission. This mechanism centralizes the actuator mass at the base to achieve an ultra-low inertia design, while the differential architecture couples motors in parallel to amplify torque for flexion movements. Performance evaluations demonstrate that the prototype delivers a nominal grasping force of 15 N and a fingertip force of 3.1 N, while maintaining a remarkably low system inertia (motor contribution of 0.236%) and mechanical impedance (<700 N/m) within the typical human manipulation frequency range. The proposed hardware successfully resolves the trade-offs among torque, transparency, and kinematics, establishing a robust foundation for highly responsive, sensorless proprioceptive force estimation in dynamic environments.