🤖 AI Summary
This study addresses the challenge of manipulating paper-like flexible materials, which are highly sensitive to compressive stress and prone to failure under conventional grasping strategies due to minor physical variations. The work proposes a novel, adaptable multi-task grasping framework that systematically integrates environmental constraints with a general-purpose soft gripper. The approach leverages task-oriented manipulation primitives to design grasp motions, establishes corresponding kinematic and mechanical models, and introduces a grasp-force-to-success-rate evaluation metric. Experimental validation delineates the operational workspace and material conditions under which each strategy is effective, significantly enhancing grasping stability. This framework offers a robust solution for domestic service robots handling planar deformable objects.
📝 Abstract
Robotic manipulation of flexible objects is widely required in both industrial and service applications. Among such objects, paper-like materials exhibit distinct mechanical characteristics compared to cloth, being more sensitive to compressive stress, where minor variations in physical properties can significantly affect grasping. This study systematically investigates grasping strategies for paper-like materials using a universal soft gripper by exploiting environmental constraints. Based on manipulation primitives employed in existing grasping strategies, we proposed systematic grasping strategies for flexible materials by exploiting environmental constraints and analyzed their mechanical and kinematic models. To investigate the influence of materials and working conditions on grasping, an evaluation system for measuring grasping force and success rate was defined and experimentally evaluated. Finally, we summarized the specific workspaces and characteristics of different strategies that can satisfy various task requirements and lead to potential applications in household service robots for grasping planar flexible objects.