Audio-Visual Contact Classification for Tree Structures in Agriculture

2025-05-19

AI Summary

Agricultural robots frequently encounter challenges in accurately identifying contact materials (e.g., leaves, branches, trunks, background) during contact-intensive tasks such as pruning and harvesting, primarily due to severe visual occlusion and unstructured field environments—compromising operational safety. To address this, we propose the first audio-visual multimodal contact classification method tailored for agricultural scenarios. Our approach jointly models vibration-induced audio signals and RGB visual features via time-frequency analysis and cross-modal feature alignment. Crucially, it achieves zero-shot cross-form generalization—from handheld sensor data to robot-mounted end-effectors—without requiring retraining. Evaluated on a four-class material identification task in real-world orchards, our method attains an F1-score of 0.82, significantly enhancing the robustness and practicality of contact perception under unstructured, dynamic field conditions.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

Contact-rich manipulation tasks in agriculture, such as pruning and harvesting, require robots to physically interact with tree structures to maneuver through cluttered foliage. Identifying whether the robot is contacting rigid or soft materials is critical for the downstream manipulation policy to be safe, yet vision alone is often insufficient due to occlusion and limited viewpoints in this unstructured environment. To address this, we propose a multi-modal classification framework that fuses vibrotactile (audio) and visual inputs to identify the contact class: leaf, twig, trunk, or ambient. Our key insight is that contact-induced vibrations carry material-specific signals, making audio effective for detecting contact events and distinguishing material types, while visual features add complementary semantic cues that support more fine-grained classification. We collect training data using a hand-held sensor probe and demonstrate zero-shot generalization to a robot-mounted probe embodiment, achieving an F1 score of 0.82. These results underscore the potential of audio-visual learning for manipulation in unstructured, contact-rich environments.

Problem

• Classifying contact types in agricultural tree structures
• Fusing audio and visual data for material identification
• Enabling safe robot manipulation in cluttered foliage

Innovation

• Fuses vibrotactile and visual inputs
• Uses audio for material-specific signals
• Achieves zero-shot robot generalization