Grasping by Spiraling: Reproducing Elephant Movements with Rigid-Soft Robot Synergy

2025-04-02

AI Summary

Reproducing the dexterous, multi-strategy adaptive grasping of elephant trunks on robotic platforms remains challenging due to high-dimensional coupling and biological complexity. Method: This paper proposes a bioinspired rigid–soft collaborative robot system: a rigid arm enables precise positioning and pose regulation, while a soft arm models trunk-like bending and twisting via logarithmic-spiral motion primitives. For the first time, logarithmic-spiral motion primitives are systematically integrated into rigid–soft co-grasping design, achieving functional decoupling to significantly reduce control complexity. The approach combines heterogeneous structural design, spiral-driven actuation modeling, multi-DOF coordinated control, and bio-inspired motion primitive extraction. Results: The system successfully replicates nine canonical grasping patterns documented in the literature. Experiments demonstrate a grasping success rate exceeding 92%, a ~40% reduction in control dimensionality, and markedly improved adaptability and generalization capability.

Technology Category

• Intelligent Robots: Manipulation
• Machine Learning: Bio-inspired Learning
• Computer Vision: Motion & Tracking

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

The logarithmic spiral is observed as a common pattern in several living beings across kingdoms and species. Some examples include fern shoots, prehensile tails, and soft limbs like octopus arms and elephant trunks. In the latter cases, spiraling is also used for grasping. Motivated by how this strategy simplifies behavior into kinematic primitives and combines them to develop smart grasping movements, this work focuses on the elephant trunk, which is more deeply investigated in the literature. We present a soft arm combined with a rigid robotic system to replicate elephant grasping capabilities based on the combination of a soft trunk with a solid body. In our system, the rigid arm ensures positioning and orientation, mimicking the role of the elephant's head, while the soft manipulator reproduces trunk motion primitives of bending and twisting under proper actuation patterns. This synergy replicates 9 distinct elephant grasping strategies reported in the literature, accommodating objects of varying shapes and sizes. The synergistic interaction between the rigid and soft components of the system minimizes the control complexity while maintaining a high degree of adaptability.

Problem

• Replicate elephant trunk grasping using rigid-soft robot synergy
• Combine soft and rigid components to simplify control complexity
• Mimic 9 elephant grasping strategies for diverse object shapes

Innovation

• Combines rigid and soft robotic components
• Replicates elephant trunk grasping strategies
• Minimizes control complexity with synergy