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Hierarchical Performance-Based Design Optimization Framework for Soft Grippers

Hamed Rahimi Nohooji, Holger Voos

TL;DR

The paper introduces a hierarchical, performance-based design optimization framework for multi-fingered soft grippers, organizing optimization into Task Space, Motion Space, and Design Space to balance adaptability, dexterity, reachability, and stability without fixed weightings. It defines a Pareto-driven, multi-objective objective and couples parametric, topological, and field optimization with sensitivity analysis to systematically refine geometry, material distribution, and actuation. The approach enables scalable, task-driven SG designs with balanced trade-offs and provides a workflow that can be evaluated via a final multi-indicator assessment. This framework advances soft robotics by offering a structured path to robust, versatile grippers capable of handling diverse objects and manipulation tasks. Its emphasis on Pareto optimality, hierarchical design, and regional field adjustments supports practical deployment across applications like healthcare, logistics, and human-robot interaction.

Abstract

This paper presents a hierarchical, performance-based framework for the design optimization of multi-fingered soft grippers. To address the need for systematically defined performance indices, the framework structures the optimization process into three integrated layers: Task Space, Motion Space, and Design Space. In the Task Space, performance indices are defined as core objectives, while the Motion Space interprets these into specific movement primitives. Finally, the Design Space applies parametric and topological optimization techniques to refine the geometry and material distribution of the system, achieving a balanced design across key performance metrics. The framework's layered structure enhances SG design, ensuring balanced performance and scalability for complex tasks and contributing to broader advancements in soft robotics.

Hierarchical Performance-Based Design Optimization Framework for Soft Grippers

TL;DR

The paper introduces a hierarchical, performance-based design optimization framework for multi-fingered soft grippers, organizing optimization into Task Space, Motion Space, and Design Space to balance adaptability, dexterity, reachability, and stability without fixed weightings. It defines a Pareto-driven, multi-objective objective and couples parametric, topological, and field optimization with sensitivity analysis to systematically refine geometry, material distribution, and actuation. The approach enables scalable, task-driven SG designs with balanced trade-offs and provides a workflow that can be evaluated via a final multi-indicator assessment. This framework advances soft robotics by offering a structured path to robust, versatile grippers capable of handling diverse objects and manipulation tasks. Its emphasis on Pareto optimality, hierarchical design, and regional field adjustments supports practical deployment across applications like healthcare, logistics, and human-robot interaction.

Abstract

This paper presents a hierarchical, performance-based framework for the design optimization of multi-fingered soft grippers. To address the need for systematically defined performance indices, the framework structures the optimization process into three integrated layers: Task Space, Motion Space, and Design Space. In the Task Space, performance indices are defined as core objectives, while the Motion Space interprets these into specific movement primitives. Finally, the Design Space applies parametric and topological optimization techniques to refine the geometry and material distribution of the system, achieving a balanced design across key performance metrics. The framework's layered structure enhances SG design, ensuring balanced performance and scalability for complex tasks and contributing to broader advancements in soft robotics.

Paper Structure

This paper contains 17 sections, 6 equations, 3 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. Optimization Framework for Soft Grippers
  3. Problem Formulation
  4. Optimization Goals
  5. Hierarchical Architecture for Soft Gripper Design
  6. Optimization Model
  7. Design Objective
  8. Design Variables
  9. Constraints
  10. Optimization Implementation
  11. Optimization Formulation
  12. Parametric Optimization
  13. Topological Optimization
  14. Field Optimization
  15. Sensitivity Analysis
  16. ...and 2 more sections

Figures (3)

  • Figure 1: A multi-layered framework for SGs optimization, integrating task-level objectives, motion behavior, and design-space refinement to improve dexterity, adaptability, stability, and reachability.
  • Figure 2: Performance indices for soft gripper optimization focusing on adaptability, dexterity, reachability, and stability. These indices guide the optimization process in relation to the gripper configuration.
  • Figure 3: Design Optimization Model for SGs, illustrating the key design objectives, variables, and constraints.