Combining and Decoupling Rigid and Soft Grippers to Enhance Robotic Manipulation

TL;DR

The paper tackles the limitations of purely rigid or purely soft grippers by introducing RISOs, which couple rigid end-effectors with switchable soft adhesives to enable rigid, soft, or hybrid grasps across a $2\mathrm{ mg}$ to $2\mathrm{ kg}$ mass range ($10^6$×). It presents the mechanical design of a tunable pneumatic soft membrane mounted on rigid fingers and derives the adhesion scaling $F_c \sim \sqrt{G_c}\sqrt{A/C}$ to maximize contact area while controlling compliance, enabling rapid switching between grasp modes. The control framework integrates robot autonomy and human input via a Bayesian shared-autonomy approach, inferring the target object and grasp type and adjusting low-level variables accordingly. Experimental results show high grasp success with RISOs compared to baselines, rapid adhesion switching (under $0.1$ s), and strong performance across diverse objects, including a pizza assembly demonstration, highlighting practical impact in unstructured environments. Overall, RISOs extend the grasping capabilities and user usability of robotic grippers, offering a path toward versatile, robust manipulation in real-world settings.

Abstract

For robot arms to perform everyday tasks in unstructured environments, these robots must be able to manipulate a diverse range of objects. Today's robots often grasp objects with either soft grippers or rigid end-effectors. However, purely rigid or purely soft grippers have fundamental limitations: soft grippers struggle with irregular, heavy objects, while rigid grippers often cannot grasp small, numerous items. In this paper we therefore introduce RISOs, a mechanics and controls approach for unifying traditional RIgid end-effectors with a novel class of SOft adhesives. When grasping an object, RISOs can use either the rigid end-effector (pinching the item between non-deformable fingers) and/or the soft materials (attaching and releasing items with switchable adhesives). This enhances manipulation capabilities by combining and decoupling rigid and soft mechanisms. With RISOs robots can perform grasps along a spectrum from fully rigid, to fully soft, to rigid-soft, enabling real time object manipulation across a 1 million times range in weight (from 2 mg to 2 kg). To develop RISOs we first model and characterize the soft switchable adhesives. We then mount sheets of these soft adhesives on the surfaces of rigid end-effectors, and develop control strategies that make it easier for robot arms and human operators to utilize RISOs. The resulting RISO grippers were able to pick-up, carry, and release a larger set of objects than existing grippers, and participants also preferred using RISO. Overall, our experimental and user study results suggest that RISOs provide an exceptional gripper range in both capacity and object diversity. See videos of our user studies here: https://youtu.be/du085R0gPFI

Figures (11)

• Figure 1: RISO enhances grasping by combining and decoupling rigid and soft mechanisms. (A) Human operators and robot arms can leverage RISOs (RIgid-SOft robotic grippers) to pick up, hold, and release objects. (B) RISOs are formed by mounting soft adhesive sheets to the surfaces of traditional rigid end-effectors. (C) When grasping an item RISO can use a fully rigid grasp (pinching the object between non-deformable fingers) a fully soft grasp (causing the object to adhere to its surface), or a combined rigid and soft grasp. (D) With this spectrum of grasps, RISO is able to pick up objects ranging from $2$ mg items to $2$ kg, a $1$ million times change in mass.
• Figure 2: Measuring the force capacity of RISO's soft adhesives. The soft adhesives operate under the principles from Equation (\ref{['eq:adhscaling']}), and seek to maximize contact area while minimizing surface compliance. (A) Soft grasps while switching the membrane from neutral to negative pressure. (B) Soft grasps while switching the membrane from positive to negative pressure. (C) Force profiles for neutral to negative and (D) positive to negative with a $12.5$ mm smooth indenter (circles represent testing stages from A and B.) (E) Force capacity $F_{c}$ vs. indenter radius. (F) Force capacity $F_{c}$ vs. $\sqrt{A/C}$, where the points represent the experimental data and the lines represent the prediction from Equation (\ref{['eq:adhscaling']}). Here $G_c$= 4.2 J/m${}^{2}$ and $G_c$= 44.7 J/m${}^{2}$ for the slopes of the lower and upper lines. (G) Adhesion switching ratio ($SR$) as a function of indenter radius.
• Figure 3: Characterizing RISO's soft adhesives across diverse objects. (A) Curved indenters with four different curvatures. (B) Force capacity $F_{c}$ vs. indenter curvature. (C) Rough indenters with lines etched at distance $d$ from one another. (D) Force capacity $F_{c}$ vs. different line distances. (E) Porous indenters with four levels of porosity. (F) Force capacity $F_{c}$ vs surface porosity. Across all plots the indenters have a radius of $7.5$ mm and the scale bars are $10$ mm.
• Figure 4: Comparing RISOs to existing grippers. (A) Experimental setup. Grippers were attached to a $7$-DoF robot arm and used to grasp, move, and drop a dataset of $15$ household objects. (B) We compared an industrial SoftGripper, a granular jamming gripper (Granular), and RISO. (C) Success rates for each gripper when the system was fully automated. (D) Success rates for each gripper with a human-in-the-loop. A total of $12$ participants remotely controlled the robot arm and grippers using a joystick. (E) After working with each gripper users responded to a $7$-point Likert scale survey. Users indicate how easy it was to use the gripper and which grippers they preferred. (F) Success rates for $5$ sample objects where RISO outperformed SoftGripper and Granular. For additional results on this user study see Tables \ref{['table:collab3']}--\ref{['table:collab2']}.
• Figure 5: Making it easier for humans to utilize RISOs. (A) Overview of human control and shared autonomy. In human control the user teleoperates the robot and RISO throughout the entire manipulation task. By contrast, in shared autonomy the system uses the human's inputs to infer their desired object and grasp type. The system then partially automates the robot arm and RISO to help complete that grasp. (B) Objective results from a user study with $12$ participants. With shared autonomy users were able to complete grasps with fewer joystick inputs and shorter robot trajectories. (C) Users responded to a $7$-point Likert scale survey to indicate how helpful the controller was, how easy it was to use the gripper, and which control approach they prefer to use. Higher scores indicate agreement (e.g., more helpful), and an $*$ denotes statistical significance.