SimTO: A simulation-based topology optimization framework for bespoke soft robotic grippers

2026-01-27

AI Summary

Existing soft robotic grippers struggle to safely grasp objects with high topological complexity and rich geometric features—such as gears, coral, or broccoli—due to the absence of well-defined optimal contact surfaces and susceptibility to damage. This work proposes SimTO, a novel framework that seamlessly integrates physics-based contact simulation with high-resolution topology optimization to automatically extract realistic loading conditions during grasping without manual specification. By doing so, SimTO generates highly customized gripper geometries that precisely conform to the target object’s shape. The approach enables adaptive gripper design for previously unseen complex objects and demonstrates superior customization performance and generalization capability in numerical experiments.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Intelligent Robots: Manipulation
• Computer Vision: Low Level & Physics-based Vision

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Web Mining and Content Analysis: Web data generation and simulation

Abstract

Soft robotic grippers are essential for grasping delicate, geometrically complex objects in manufacturing, healthcare and agriculture. However, existing grippers struggle to grasp feature-rich objects with high topological variability, including gears with sharp tooth profiles on automotive assembly lines, corals with fragile protrusions, or vegetables with irregular branching structures like broccoli. Unlike simple geometric primitives such as cubes or spheres, feature-rich objects lack a clear"optimal"contact surface, making them both difficult to grasp and susceptible to damage when grasped by existing gripper designs. Safe handling of such objects therefore requires specialized soft grippers whose morphology is tailored to the object's features. Topology optimization offers a promising approach for producing specialized grippers, but its utility is limited by the requirement for pre-defined load cases. For soft grippers interacting with feature-rich objects, these loads arise from hundreds of unpredictable gripper-object contact forces during grasping and are unknown a priori. To address this problem, we introduce SimTO, a framework that enables high-resolution topology optimization by automatically extracting load cases from a contact-based physics simulator, eliminating the need for manual load specification. Given an arbitrary feature-rich object, SimTO produces highly customized soft grippers with fine-grained morphological features tailored to the object geometry. Numerical results show our designs are not only highly specialized to feature-rich objects, but also generalize to unseen objects.

Problem

• soft robotic grippers
• feature-rich objects
• topology optimization
• contact forces
• grasping

Innovation

• topology optimization
• soft robotic grippers
• contact-based simulation
• feature-rich objects
• load case automation