Rigidity-Based Multi-Finger Coordination for Precise In-Hand Manipulation of Force-Sensitive Objects

TL;DR

A dual-layer framework for multi-finger coordination is proposed, enabling high-precision manipulation of force-sensitive objects through joint control without tactile feedback, and it is validated on a custom dexterous hand, demonstrating the capability to manipulate fragile objects with high precision and safety.

Abstract

Precise in-hand manipulation of force-sensitive objects typically requires judicious coordinated force planning as well as accurate contact force feedback and control. Unlike multi-arm platforms with gripper end effectors, multi-fingered hands rely solely on fingertip point contacts and are not able to apply pull forces, therefore poses a more challenging problem. Furthermore, calibrated torque sensors are lacking in most commercial dexterous hands, adding to the difficulty. To address these challenges, we propose a dual-layer framework for multi-finger coordination, enabling high-precision manipulation of force-sensitive objects through joint control without tactile feedback. This approach solves coordinated contact force planning by incorporating graph rigidity and force closure constraints. By employing a force-to-position mapping, the planned force trajectory is converted to a joint trajectory. We validate the framework on a custom dexterous hand, demonstrating the capability to manipulate fragile objects-including a soft yarn, a plastic cup, and a raw egg-with high precision and safety.

Figures (11)

• Figure 1: Precise in-hand movement of force-sensitive objects: (A) Manipulating a soft yarn while maintaining its shape during the movement process. (B) Manipulating a disposable plastic cup to move the AprilTag along a predefined trajectory. (C) Manipulating a raw egg to move the AprilTag along a predefined trajectory.
• Figure 2: Precision in-hand object movement: (A) The object moves along a predefined trajectory, with coordinate system $\{\mathcal{M}\}$ following each waypoint ($Wp1, \dots$), achieving precise motion through joint position control. (B) The motion process of manipulation frame $\{\mathcal{M}\}$.
• Figure 3: RFP-FJC Algorithm Framework
• Figure 4: A graph-based abstraction of geometric constraints among contact points, modeling a rigid configuration that remains invariant during grasp execution.
• Figure 5: Contact force analysis: (A) Contact force $f_c$ lies within the friction cone, indicating stability. (B) With only $f_{\text{ope}}$ and $f_{\text{int},R}$, the resultant falls outside the cone. (C) The combined force of $f_{\text{ope}}$, $f_{ \text{int},R}$, and $f_{\text{int},\mu}$ lies well within the cone, ensuring a stable grasp.

Theorems & Definitions (1)

• Definition 1