Simulation-Driven Evolutionary Motion Parameterization for Contact-Rich Granular Scooping with a Soft Conical Robotic Hand

2026-04-07

AI Summary

This work addresses the challenge of stable control in soft conical grippers during granular material scooping tasks, where high compliance and complex deformations hinder reliable performance. The study presents the first integration of high-fidelity physics simulation with evolutionary strategies to model the passive reconfiguration dynamics of a soft hand transitioning from a planar to a conical shape. By leveraging this framework, the method automatically optimizes sensorless scooping trajectories without manual parameter tuning. It enables accurate modeling of intricate soft-body deformations and facilitates unsupervised motion planning. Extensive evaluations demonstrate that the approach achieves strong generalization, robustness, and efficiency across diverse container geometries and granular materials. Both simulated and real-world experiments validate the effectiveness of the proposed methodology.

Abstract

Tool-based scooping is vital in robot-assisted tasks, enabling interaction with objects of varying sizes, shapes, and material states. Recent studies have shown that flexible, reconfigurable soft robotic end-effectors can adapt their shape to maintain consistent contact with container surfaces during scooping, improving efficiency compared to rigid tools. These soft tools can adjust to varying container sizes and materials without requiring complex sensing or control. However, the inherent compliance and complex deformation behavior of soft robotics introduce significant control complexity that limits practical applications. To address this challenge, this paper presents the development of a physics-based simulation model of a deformable soft conical robotic hand that captures its passive reconfiguration dynamics and enables systematic trajectory optimization for scooping tasks. We propose a novel physics-based simulation approach that accurately models the soft tool's morphing behavior from flat sheets to adaptive conical structures, combined with an evolutionary strategy framework that automatically optimizes scooping trajectories without manual parameter tuning. We validate the optimized trajectories through both simulation and real-robot experiments. The results demonstrate strong generalization and successfully address a range of challenging tasks previously beyond the reach of existing approaches. Videos of our experiments are available online: https://sites.google.com/view/scoopsh

Problem

• soft robotics
• contact-rich manipulation
• granular scooping
• control complexity
• deformable end-effectors

Innovation

• simulation-driven
• soft robotic hand
• evolutionary optimization
• contact-rich manipulation
• morphing dynamics