Vortex-Induced Torque in Grasping Soft Objects: Influence of Geometry and Pneumatic Parameters
Authors: Roman Mykhailyshyn, Tomohiro Motoda, Ivan Virgala, Ann Majewicz Fey, Yukiyasu Domae
Organizations: National Institute of Advanced Industrial Science and Technology (AIST), Tokyo 135-0064, Japan · Department of Industrial Automation and Mechatronics, Technical University of Košice, 04200 Košice, Slovak Republic · Department of Mechanical Engineering, The University of Texas at Austin, Austin, TX 78712, USA · Embodied AI Research Team, National Institute of Advanced Industrial Science and Technology (AIST), Tokyo 135-0064, Japan
Vortex grippers are commonly integrated into automated systems for handling objects with complex geometries in packaging, sorting, and assembling. However, their interaction with compliant objects remains difficult to predict, particularly with respect to the vortex-induced torque transmitted during grasping. This study investigates vortex-induced torque during the grasping of compliant objects and quantifies the coupled effects of supply pressure, tangential nozzle diameter, surface geometry, and curvature. The results show that increasing the tangential nozzle diameter primarily enhances the sensitivity of the generated torque to supply pressure rather than producing a uniformly higher torque. Surface geometry also strongly influences the rotational interaction, with dome-concave surfaces generally producing substantially greater torque than corresponding dome-convex surfaces, whereas the difference between cylindrical concave and convex surfaces is comparatively small. The strongest and most repeatable response was observed for a dome-concave surface exhibiting favorable object-gripper geometric matching. Among the evaluated machine-learning regression methods, Gaussian Process Regression provided the best overall predictive performance under repeated cross-validation, demonstrating that vortex-induced torque can be estimated from combined pneumatic and geometric parameters. These findings provide a basis for predicting and utilizing vortex-induced torque in the dexterous manipulation of soft objects.
Figures & tables
Fig. 1 : Vortex gripper technology and representative applications: (a) compressed air flow direction that form a vortex rotation flow that creates a lifting effect [ 3 ] ; (b) application of vortex technology for grasping objects in manufacturing and assembly (electronics, food, packaging, etc.) [ 33 ] ; (c) integration of soft vortex gripper into the dexterous hand finger [ 44 ] ; (d) vortex gripper for grasping soft tissue of various shapes [ 49 , 50 ] .
Fig. 2 : Experimental setup with different object surfaces for studying the force characteristics of a vortex gripper.
R [mm]
15
20
25
30
35
40
45
50
75
100
δ(7,R) [mm]
1.734
1.265
1.000
0.828
0.707
0.617
0.548
0.492
0.327
0.245
δ(10,R) [mm]
3.820
2.679
2.087
1.716
1.459
1.270
1.125
1.010
0.670
0.501
Λg
0.955
0.670
0.522
0.429
0.365
0.318
0.281
0.253
0.167
0.125
Table I : Geometric Deviation of the Ideal Dome Relative to the Gripper Dimensions
Fig. 3 : Quasi-static lifting force Fz and torque magnitude ∣Mz∣ as functions of gap height for the rigid flat surface using G 1 .
Ps [kPa]
h0 [mm]
Fz,max [N]
h(Fz,max) [mm]
∣Mz∣ [N ⋅ mm]
100
0.070
0.696
0.20
0.319±0.008
200
0.079
1.243
0.25
0.560±0.008
300
0.085
1.931
0.25
1.304±0.005
400
0.110
2.685
0.25
1.796±0.005
Table II : Quasi-Static Rigid-Flat Surface Interaction
Fig. 4 : (a) Vortex-induced torque for the flat compliant surface as a function of supply pressure. (b) Logarithmic pressure sensitivity of the torque between 100 and 400 kPa ( SP=0 indicates no change, SP=1 represents a twofold increase, and SP=−1 represents a twofold decrease).
dn
100 kPa
200 kPa
300 kPa
400 kPa
0.6 mm
0.238±0.048
0.212±0.022
0.307±0.110
0.282±0.064
0.8 mm
0.620±0.050
0.757±0.080
0.952±0.061
0.963±0.110
1.0 mm
0.541±0.096
0.715±0.218
0.994±0.267
1.601±0.376
Table III : Torque for the Flat Compliant Surface [N ⋅ mm]
Geometry
0.6 mm
0.8 mm
1.0 mm
ΔMz
Positive
ΔMz
Positive
ΔMz
Positive
Cylindrical concave
−9.5%
3/10
35.8%
7/10
113.7%
10/10
Cylindrical convex
50.8%
8/10
39.4%
7/10
109.4%
10/10
Dome concave
19.5%
6/10
57.6%
9/10
63.2%
7/10
Dome convex
91.5%
7/10
65.7%
10/10
140.0%
10/10
Table IV : Median Change in Mean Peak Torque Between 100 and 400 kPa
Fig. 5 : Mean peak torque as a function of radius for cylindrical surfaces at different supply pressures: (a, b, c) - concave; (d, e, f) - convex.
Fig. 6 : Mean peak torque as a function of radius for dome surfaces at different supply pressures: (a, b, c) - concave; (d, e, f) - convex.
Fig. 7 : (a) Logarithmic concave-to-convex torque ratio for matched surface radius, pressure, and nozzle diameter. (b) Mean peak torque for the R=20 mm dome-concave surface as a function of supply pressure.
Fig. 8 : Measured and repetition-averaged out-of-fold predictions of peak vortex-induced torque for the selected GPR model.
Fig. 9 : GPR prediction torque map for the vortex gripper (G 3 ) with dome-concave surface.
Reliable robotic grasping benefits from estimating the evolving physical interaction and selecting a grasp-dependent compression target. Tactile sensors provide direct interaction measurements but require dedicated hardware at deployment. We introduce ZeroTouch, a tactile-supervised framework that predicts dense contact deformation, the instantaneous six-axis wrench, and a grasp-dependent desired compression target from wrist RGB observations, gripper state, and local gravity direction. Tactile measurements are used only as privileged supervision during training and are not required at deployment. On the full validation set, the complete architecture reduces normal-force MAE from 2.017 N for a state-only baseline to 0.531 N. In physical evaluation with 20 trials per condition, ZeroTouch achieves 95% success on an unseen object, 80% in a seen-object/unseen-grasp condition, and 90% under a content/load shift. Under the same evaluation protocol, OpenVLA achieves 25%, 40%, and 55%, while SmolVLA achieves 10%, 25%, and 35%, respectively.
Dmitriy Kosenkov, Daniia Zinniatullina, Miguel Altamirano Cabrera +3
Intelligent Space Robotics Laboratory, Skolkovo Institute of Science and Technology, Moscow, Russian Federation.
Robotic grippers operating in unstructured environments must robustly grasp objects of different shapes and sizes despite perception and positioning errors. Soft grippers that can achieve this robustness through intelligent mechanical design rather than sophisticated control software offer a promising solution. However, no existing computational design framework explicitly optimises soft gripper morphology for robustness across object geometries and grasp poses. We introduce SimTO-Robust, a generalisation of simulation-driven topology optimisation (SimTO) that simulates grasps across multiple objects and poses and aggregates their contact forces to guide gripper morphology optimisation. We generate 6,646 designs and physically evaluate 24 of them alongside 10 specialised SimTO designs, two rigid baseline grippers, and a baseline generalist soft gripper produced using conventional topology optimisation. All 37 grippers are tested in a pick-and-place task involving five feature-rich objects and six grasp poses, with external disturbances of increasing magnitude applied during the task. On average, SimTO-Robust designs complete pick-and-place more reliably than specialised SimTO designs and the rigid baselines. The best SimTO-Robust designs also withstood more of the applied disturbances than any other tested gripper. These results show that incorporating diverse grasp conditions into simulation-driven topology optimisation is a simple and effective approach to improving real-world soft gripper robustness. More broadly, they show that robust robotic grasping can be achieved through intelligent mechanical design rather than sophisticated sensing and control.
Kurt Enkera, Joshua Pinskier, Marcus Gallagher +1
CSIRO Robotics, Data61, CSIRO, Australia. · School of Electrical Engineering and Computer Science (EECS), The University of Queensland, St Lucia, Australia.
Grasping deformable objects with varying stiffness remains a significant challenge in robotics. Estimating the local stiffness of a target object is important for determining an optimal grasp pose that enables stable pickup without damaging the object. This paper presents a probe-to-grasp manipulation framework for estimating the relative stiffness of objects using a passive soft-rigid two-finger hybrid gripper equipped with self-sensing pneumatic variable-stiffness joints. Each finger of the gripper consists of two rigid links connected by a soft pneumatic ring placed at the joint, enabling both compliant interaction and controllable joint stiffness via internal pressurization. By measuring the pressure inside the pneumatic ring, we can estimate the interaction force during contact. Building on this, we propose a practical probing strategy to infer relative object stiffness by correlating the estimated normal force with known gripper closing displacement. We validate the self-sensing model through stiffness characterization experiments across bending angles and pressure ranges, and demonstrate stiffness-aware probing-and-grasping in real-life applications: selecting grasp locations on fruits with spatially varying stiffness. The proposed system offers a minimal, low-cost sensing approach for stiffness-aware soft manipulation while retaining probing and grasping capability.