Wearable Roller Rings to Augment In-Hand Manipulation through Active Surfaces

2024-03-19

AI Summary

Unstable grasping and complex modeling arise in in-hand manipulation due to active finger motions—such as rolling, sliding, and contact interruptions—under conventional finger-driven paradigms. Method: This paper introduces the Wearable Roller Ring (RR), a tilt-actuated surface module mounted at the finger’s proximal phalanx, enabling dexterous object reorientation and repositioning without finger lifting or contact loss. We establish that only two non-collinear RRs suffice for full-degree-of-freedom nonholonomic in-hand manipulation. The approach integrates differential kinematic modeling, nonholonomic constraint analysis, modular mechatronic design, and multibody contact dynamics simulation. Contribution/Results: Experiments demonstrate high stability, low environmental constraints, and shape-agnostic manipulation of arbitrarily shaped objects on both robotic and human hands. The RR paradigm transcends traditional finger-actuated manipulation, offering seamless human–robot compatibility and practical engineering deployability.

Technology Category

• Intelligent Robots: Manipulation
• Computer Vision: Motion & Tracking
• Machine Learning: Learning with Manifolds

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

In-hand manipulation is a crucial ability for reorienting and repositioning objects within grasps. The main challenges in this are not only the complexity of the computational models, but also the risks of grasp instability caused by active finger motions, such as rolling, sliding, breaking, and remaking contacts. This paper presents the development of the Roller Ring (RR), a modular robotic attachment with active surfaces that is wearable by both robot and human hands to manipulate without lifting a finger. By installing the angled RRs on hands, such that their spatial motions are not colinear, we derive a general differential motion model for manipulating objects. Our motion model shows that complete in-hand manipulation skill sets can be provided by as few as only 2 RRs through non-holonomic object motions, while more RRs can enable enhanced manipulation dexterity with fewer motion constraints. Through extensive experiments, we test the RRs on both a robot hand and a human hand to evaluate their manipulation capabilities. We show that the RRs can be employed to manipulate arbitrary object shapes to provide dexterous in-hand manipulation.

Problem

• Enhancing in-hand manipulation with wearable active surfaces
• Reducing grasp instability risks during object reorientation
• Simplifying complex computational models for robotic manipulation

Innovation

• Modular robotic attachment with active surfaces
• Differential motion model for object manipulation
• Non-holonomic object motions with few RRs