Abstract
Comment: 6 pages, 5 figures, 2 tables. Dongmyoung Lee and Chengxi Li contributed equally to this research
DexTwist: Dexterous Hand Retargeting for Twist Motion via Mixed Reality-based Teleoperation
- Authors
- Dongmyoung Lee, Chengxi Li, Dongheui Lee
- Categories
- cs.RO
Brief
DexTwist introduces a functional twist-retargeting method for MR-based teleoperation that targets contact-rich rotational manipulation where kinematic imitation fails. The system detects tripod pinches, estimates intended screw axis and twist, then performs a real-time joint-space residual optimization minimizing a virtual-object objective (turn angle, axis consistency, fingertip closure, tripod stability). Simulations and real tests demonstrate improved turning-angle tracking and reduced screw-axis drift versus a vector-based baseline.
Why it matters
DexTwist (Lee, Li, Lee; arXiv 2026-05-12) is a mixed-reality dexterous-hand retargeting framework that detects a tripod pinch, estimates the operator's intended screw axis and twist magnitude, and applies a real-time residual joint-space refinement to track turning progress while regularizing robot tripod geometry.
Key details
- The refinement minimizes a virtual-object objective composed of turning angle, screw-axis consistency, fingertip closure, and tripod stability to mitigate embodiment-gap issues (link-length/joint-axis mismatches) that cause tangential fingertip sliding and screw-axis drift in tasks like cap opening, key turning, and bolt screwing.
- Simulation and real-world experiments reported in the 6-page paper (5 figures, 2 tables) show DexTwist improves turning-angle tracking and screw-axis stability compared with a vector-based retargeting baseline (no numeric percentages provided in the abstract).