Controlling friction at the fingertip is fundamental to dexterous manipulation, yet remains difficult to realize in robotic hands. We present the design and analysis of a robotic fingertip equipped with passive rollers that can be selectively braked or pivoted to modulate contact friction and constraint directions. When unbraked, the rollers permit unconstrained sliding of the contact point along the rolling direction; when braked, they resist motion like a conventional fingertip. The rollers are mounted on a pivoting mechanism, allowing reorientation of the constraint frame to accommodate different manipulation tasks. We develop a constraint-based model of the fingertip integrated into a parallel-jaw gripper and analyze its ability to support diverse manipulation strategies. Experiments show that the proposed design enables a wide range of dexterous actions that are conventionally challenging for robotic grippers, including sliding and pivoting within the grasp, robust adaptation to uncertain contacts, multi-object or multi-part manipulation, and interactions requiring asymmetric friction across fingers. These results demonstrate the versatility of passive roller fingertips as a low-complexity, mechanically efficient approach to friction modulation, advancing the development of more adaptable and robust robotic manipulation.
Figures & tables
Fig. 1: Prototype fingertips with pivotable, brakeable rollers mounted on a Robotiq 2F-85 Adaptive Gripper.
Fig. 2: A CAD model of the proposed roller module: (a) An exploded view showing the separation between brake and roller. (b) An exploded view of the rollers. (c) An exploded view of the brake mechanism. (d) A complete view with call-outs for relevant dimensions.
Property
wm
tm
lm
dr
wr
Dimension [mm]
41
27.5
92
9.2
22.5
TABLE I: Roller Module Dimensions
Fig. 3: Kinematic model of a general object under antipodal grasp by the proposed gripper with two arbitrary rolling axes, including environmental contact.
Fig. 4: Friction Test Apparatus (Top View)
Material
Rollers braked
Rollers Unbraked
Plastic
0.809 ± 0.130
0.029 ± 0.015
Glass
0.736 ± 0.162
0.034 ± 0.009
Metal
0.491 ± 0.130
0.026 ± 0.013
Wood
0.491 ± 0.037
0.024 ± .0030
TABLE II: Breakaway Friction Coefficients
Fig. 5: Object Manipulation with a Unilateral External Constraint. Red arrows indicate gripper actions, while green arrows indicate object motion. (A) Planar Object Translation. (B) Cylindrical Object Rotation.
Fig. 6: Object Manipulation with Uncertain Surface Adaptation. Red arrows indicate gripper actions, while green arrows indicate the object’s adaptive motion. (A) Contact Adaptation in Sliding (dragging). (B) Contact Adaptation in Pick and Place. (C) Free-body diagram of the object during pick-and-place: (i) in free space, (ii) in contact with the placement surface.
Fig. 7: Manipulation with asymmetric finger friction. (A) Cylinder rolling: left roller braked as the gripper moves downward; right roller braked as the gripper moves upward. (B) Object retrieval from a container: left finger (braked) contacts the object, while the right finger (unbraked) contacts the container wall. (C) Card extraction from a deck: left finger braked and right finger unbraked, enabling pickup of the leftmost card. (D) Closing a zip bag slider: left finger (braked) contacts the slider, while the right finger (unbraked) supports the back of the bag.
Fig. 8: Manipulation using roller pivoting. (A) Basic pivoting. (B) Long-object manipulation combining pivoting with other strategies.
We investigate in-hand rolling manipulation using a multifingered robot hand, where each finger is compliant and equipped with a tactile fingertip providing contact location and wrench information. We derive the equations of motion for compliant quasistatic in-hand rolling manipulation and formulate a fingertip rolling manipulation controller for multiple fingers to achieve a desired object twist within a grasp. The contact mechanics are demonstrated in simulation and the controller is tested on an experimental robot system.
Huan Weng, Yifei Chen, Kevin M. Lynch
Center for Robotics and Biosystems and the Department of Mechanical Engineering, Northwestern University, Evanston, IL 60208 USA · Northwestern Institute on Complex Systems (NICO)
Multi-fingered hands offer great potential for compliant and robust grasping of unknown objects, yet their high-dimensional force control presents a significant challenge. This work addresses two key problems: (1) distributing forces across multiple contacts to counteract an object's weight, and (2) preventing rotational slip caused by gravitational torque when a grasp is distant from the object's center of mass. We address these challenges via tactile feedback and a Second-Order Cone Programming (SOCP)-based controller, without explicit torque modeling or slip detection. Our key insights are (1) rotational slip inevitably induces translational slip at some contact points for a multi-fingered grasp, and (2) the ratio of tangential to normal force at each contact is an effective early stability indicator. By actively constraining this ratio for each finger below the estimated friction coefficient, our controller maintains grasp stability against both translational and rotational slip. Real-world experiments on 12 diverse objects demonstrate the robustness and compliance of our approach.
Yubin Ke, Jiayi Chen, Hang Lv +2
Peking University · Galbot · Beijing Academy of Artificial Intelligence
Teleoperation is widely used to collect high-quality demonstrations for robot learning. Dexterous manipulation, however, requires coordinated control of motion and contact forces. Operators can specify motion, but struggle to reproduce the subtle force adjustments of human manipulation without feeling the robot's contacts. We introduce Force-Modulated Dexterity (FOMO-Dex), a unified compliance formulation for contact-rich manipulation. FOMO-Dex expresses nominal actions in coordinates defined by task geometry and contact structure. It applies force-driven motion corrections along selected directions while leaving the remaining command components unchanged. Across card picking, paper cutting, screwdriving, and nut-and-bolt assembly, FOMO-Dex reduces excessive contact forces compared with stiff control. A user study shows that operators collect more successful demonstration data per hour after brief practice. For policy learning, imitating the corrected actions improves card-picking success over imitating raw operator commands. Policies trained on FOMO-Dex demonstrations also achieve more rotation in nut-and-bolt manipulation and screwdriving than those trained on demonstrations collected with the stiff controller. Videos are shown on https://yingyuan0414.github.io/fomo-dex/ .