Grasping by interconnection: robust closing motions from coarse object templates

Julien Vanderheyden, Guillaume Drion, Fulvio Forni, Pierre Sacr\'e · N/A · 2026

Summary

Abstract Summary

Dexterous robot hands must often grasp objects whose shape, size, and pose are known only approximately. Grasp planners typically require accurate object models or correct errors with feedback, but how much inaccuracy a closing motion can tolerate on its own remains unclear. To address this question, we designed a motion planner based on four principles: a coarse template of the object, human grasp types, an object-centric interaction, and compliant, sliding contacts instead of prescribed contact points. This paper presents the planner, implemented through virtual model control, and its evaluation on a Shadow Dexterous Hand. Without feedback, the planned closing motions tolerated size errors of about 1cm and pose errors of several centimeters and tens of degrees, a wider range than a state-of-the-art data-driven planner in 25 of 27 tested conditions. They also grasped 82.5% of 80 everyday objects and succeeded within an autonomous pipeline. Robustness can thus be designed into the closing motion itself, rather than left only to feedback. This planner opens a path toward reliable manipulation in uncertain settings, which we will pursue by combining it with adaptive feedback control on the physical hand.

Key Points

Dexterous robot hands must often grasp objects whose shape, size, and pose are known only approximately.
Grasp planners typically require accurate object models or correct errors with feedback, but how much inaccuracy a closing motion can tolerate on its own remains unclear.
To address this question, we designed a motion planner based on four principles: a coarse template of the object, human grasp types, an object-centric interaction, and compliant, sliding contacts instead of prescribed contact points.
This paper presents the planner, implemented through virtual model control, and its evaluation on a Shadow Dexterous Hand.
Without feedback, the planned closing motions tolerated size errors of about 1cm and pose errors of several centimeters and tens of degrees, a wider range than a state-of-the-art data-driven planner in 25 of 27 tested conditions.

Grasping by interconnection

This section describes what our motion planner reads, what it computes, and how it implements the four principles. Section III-A specifies the coarse template and its grasp type. Section III-B explains why virtual model control implements an object-centric interaction and how the virtual hand is simulated.

Experiments

Experiments were conducted with a Shadow Dexterous Hand [ 10 ] mounted on a UR10e arm (Fig. 4 ). The hand is position controlled: its joint controllers track the closing motion computed by the planner. All computations 1 1 1 The code, including the grasping pipeline of Section IV , will be released on a GitHub page after the double-blind review process.

use VMRobotControl.jl [ 49 ] , a Julia library for the design, simulation, control, and optimization of VMC architectures.

Experiments — Grasping by interconnection: robust closing motions from coarse object templates — Fig. 4: Robustness tests use 27 3D-printed cylinders, spheres, and boxes (bottom), grasped by a Shadow Dexterous Hand on a UR10e arm (top).

Sources and Demonstrations

Figures are reproduced from Vanderheyden et al. See the full paper for experimental details and the project page, when the paper links one, for demonstrations and videos.