DexWrist: A Robotic Wrist for Constrained and Dynamic Manipulation

Martin Peticco, Gabriella E. Ulloa, John Marangola, Pulkit Agrawal

Keywords: Wrist, Hardware Design, Imitation Learning

TL;DR: DexWrist mimics human-like wrist kinematics, exhibiting improvements in teleoperation and learning.

Abstract: We present the DexWrist, a compliant robotic wrist designed to advance robotic manipulation in highly-constrained environments, enable dynamic tasks, and speed up data collection. DexWrist is designed to be close to the functional capabilities of the human wrist and achieves mechanical compliance and a greater workspace as compared to existing robotic wrist designs. The DexWrist can supercharge policy learning by (i) enabling faster teleoperation and therefore making data collection more scalable; (ii) completing tasks in fewer steps which reduces trajectory lengths and therefore can ease policy learning; (iii) DexWrist is designed to be torque transparent with easily simulateable kinematics for simulated data collection; and most (iv) importantly expands the workspace of manipulation for approaching highly cluttered scenes and tasks.

Submission Number: 13